While XML as a data format can take many forms from flat to deeply nested, data frames must adhere to a single structure of two dimensions: row by column. Hence, as noted in docs, pandas.read_xml, is a convenience method best for flatter, shallow XML files. You can use xpath to traverse different areas of the document, not just the default /*.

However, you can use XSLT 1.0 (special purpose language designed to transform XML files) with the default parser, lxml, to transform any XML to the needed flat format of data frame. Below stylesheet will restyle the <slike> node for comma-separated text of its children <slika>:

XSLT (save as .xsl file, a special .xml file)

<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
    <xsl:output method="xml" omit-xml-declaration="no" indent="yes"/>
    <xsl:strip-space elements="*"/>

    <xsl:template match="node()|@*">
     <xsl:copy>
       <xsl:apply-templates select="node()|@*"/>
     </xsl:copy>
    </xsl:template>
    
    <xsl:template match="slike">
     <xsl:copy>
       <xsl:for-each select="*">
         <xsl:value-of select="text()"/>
         <xsl:if test="position() != last()">
            <xsl:text>,</xsl:text>
         </xsl:if>
       </xsl:for-each>
     </xsl:copy>
    </xsl:template>  
</xsl:stylesheet>

Online Demo

Python

artikal_df = pd.read_xml("my_filename.xml", stylesheet="my_style.xsl") 

# CONVERT COMMA-SEPARATED VALUES TO EMBEDDED LISTS
artikal_df["slike"] = artikal_df["slike"].str.split(',')

# PREFIX PARENT NODE NAME
artikal_df = artikal_df.add_prefix('artikal_')

artikal_df.info()
# <class 'pandas.core.frame.DataFrame'>
# RangeIndex: 2 entries, 0 to 1
# Data columns (total 12 columns):
#  #   Column               Non-Null Count  Dtype  
# ---  ------               --------------  -----  
#  0   artikal_id           2 non-null      int64  
#  1   artikal_sifra        2 non-null      int64  
#  2   artikal_barKod       2 non-null      int64  
#  3   artikal_naziv        2 non-null      object 
#  4   artikal_kategorija1  2 non-null      object 
#  5   artikal_kategorija2  2 non-null      object 
#  6   artikal_kategorija3  2 non-null      object 
#  7   artikal_vpCena       2 non-null      float64
#  8   artikal_mpCena       2 non-null      float64
#  9   artikal_dostupan     2 non-null      int64  
#  10  artikal_opis         0 non-null      float64
#  11  artikal_slike        2 non-null      object 
# dtypes: float64(3), int64(4), object(5)
# memory usage: 320.0+ bytes

I've made a package for similar use case. It could work here too.

pip install pandas_read_xml

you can do something like

import pandas_read_xml as pdx

df = pdx.read_xml('filename.xml', ['data'])

To flatten, you could

df = pdx.flatten(df)

or

df = pdx.fully_flatten(df)

You want to append the text values from the ItemNr elements which are under the shop element to the items list and not the xml Element python object which is what you were doing.

The following code was working for me:

items.append([item_nr_element.text for item_nr_element in node.getchildren()])

You can easily use xml (from the Python standard library) to convert to a pandas.DataFrame. Here's what I would do (when reading from a file replace xml_data with the name of your file or file object):

import pandas as pd
import xml.etree.ElementTree as ET
import io

def iter_docs(author):
    author_attr = author.attrib
    for doc in author.iter('document'):
        doc_dict = author_attr.copy()
        doc_dict.update(doc.attrib)
        doc_dict['data'] = doc.text
        yield doc_dict

xml_data = io.StringIO(u'''YOUR XML STRING HERE''')

etree = ET.parse(xml_data) #create an ElementTree object 
doc_df = pd.DataFrame(list(iter_docs(etree.getroot())))

If there are multiple authors in your original document or the root of your XML is not an author, then I would add the following generator:

def iter_author(etree):
    for author in etree.iter('author'):
        for row in iter_docs(author):
            yield row

and change doc_df = pd.DataFrame(list(iter_docs(etree.getroot()))) to doc_df = pd.DataFrame(list(iter_author(etree)))

Have a look at the ElementTree tutorial provided in the xml library documentation.

Because your XML is pretty complex with text values spilling across nodes, consider XSLT, the special-purpose language designed to transform XML files especially complex to simpler ones.

Python's third-party module, lxml, can run XSLT 1.0 even XPath 1.0 to parse through the transformed result for migration to a pandas dataframe. Additionally, you can use external XSLT processors that Python can call with subprocess.

Specifically, below XSLT extracts necessary attributes from both defendant and victim and entire paragraph text value by using XPath's descendant::* from the root, assuming <p> is a child to it.

XSLT (save as a .xsl file, a special .xml file)

<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
  <xsl:output indent="yes" method="xml"/>
  <xsl:strip-space elements="*"/>
  
  <xsl:template match="/*">
    <xsl:apply-templates select="p"/>
  </xsl:template>
  
  <xsl:template match="p">
    <data>
      <defendantName><xsl:value-of select="normalize-space(descendant::persName[@type='defendantName'])"/></defendantName>
      <defendantGender><xsl:value-of select="descendant::persName[@type='defendantName']/interp[@type='gender']/@value"/></defendantGender>
      <offenceCategory><xsl:value-of select="descendant::interp[@type='offenceCategory']/@value"/></offenceCategory>
      <offenceSubCategory><xsl:value-of select="descendant::interp[@type='offenceSubcategory']/@value"/></offenceSubCategory>
      
      <victimName><xsl:value-of select="normalize-space(descendant::persName[@type='victimName'])"/></victimName>
      <victimGender><xsl:value-of select="descendant::persName[@type='victimName']/interp[@type='gender']/@value"/></victimGender>
      <verdictCategory><xsl:value-of select="descendant::interp[@type='verdictCategory']/@value"/></verdictCategory>
      <verdictSubCategory><xsl:value-of select="descendant::interp[@type='verdictSubcategory']/@value"/></verdictSubCategory>
      <punishmentCategory><xsl:value-of select="descendant::interp[@type='punishmentCategory']/@value"/></punishmentCategory>
      
      <trialText><xsl:value-of select="normalize-space(/p)"/></trialText>
    </data>
  </xsl:template>       
 
</xsl:stylesheet>

Python

import lxml.etree as et
import pandas as pd

# LOAD XML AND XSL
doc = et.parse("Source.xml")
xsl = et.parse("XSLT_Script.xsl")

# RUN TRANSFORMATION
transformer = et.XSLT(xsl)
result = transformer(doc)

# OUTPUT TO CONSOLE
print(result)

data = []
for i in result.xpath('/*'):
    inner = {}
    for j in i.xpath('*'):
        inner[j.tag] = j.text
        
    data.append(inner)
    
trial_df = pd.DataFrame(data)

print(trial_df)

For the 1,000 similar XML files, loop through this process and append each one-row trial_df dataframes in a list to be stacked with pd.concat.

XML Output

<?xml version="1.0"?>
<data>
  <defendantName>Alice Jones</defendantName>
  <defendantGender>female</defendantGender>
  <offenceCategory>theft</offenceCategory>
  <offenceSubCategory>shoplifting</offenceSubCategory>
  <victimName>Edward Hillior</victimName>
  <victimGender>male</victimGender>
  <verdictCategory>guilty</verdictCategory>
  <verdictSubCategory>theftunder1s</verdictSubCategory>
  <punishmentCategory>transport</punishmentCategory>
  <trialText>Alice Jones , of St. Michael's Cornhill, was indicted for privately stealing a Bermundas Hat, value 10 s. out of the Shop of Edward Hillior , on the 21st of April last. The Prosecutor's Servant deposed that the Prisner came into his Master's Shop and ask'd for a Hat of about 10 s. price; that he shewed several, and at last they agreed for one; but she said it was to go into the Country, and that she would stop into Bishopsgate-street. and if the Coach was not gone she would come and fetch it; that she went out of the Shop but he perceiving she could hardly walk fetcht her back again, and the Hat mentioned in the Indictment fell from between her Legs. Another deposed that he saw the former Evidence take the Hat from under her Petticoats. The Prisoner denyed the Fact, and called two Persons to her Reputation, who gave her a good Character, and said that she rented a House of 10 l. a Year in Petty France, at Westminster, but she had told the Justice that she liv'd in King-Street. The Jury considering the whole matter, found her Guilty to the value of 10 d. Transportation .</trialText>
</data>

Dataframe Output

#   defendantGender defendantName offenceCategory offenceSubCategory  \
# 0          female   Alice Jones           theft        shoplifting   

#   punishmentCategory                                          trialText  \
# 0          transport  Alice Jones , of St. Michael's Cornhill, was i...   

#   verdictCategory verdictSubCategory victimGender      victimName  
# 0          guilty       theftunder1s         male  Edward Hillior