I've got the needed outcome using following script.

XML File:

<?xml version="1.0" encoding="UTF-8"?>
<base>
  <element1>element 1</element1>
  <element2>element 2</element2>
  <element3>
    <subElement3>subElement 3</subElement3>
  </element3>
</base>

Python code:

import pandas as pd
from lxml import etree

data = "C:/Path/test.xml"

tree = etree.parse(data)

lstKey = []
lstValue = []
for p in tree.iter() :
    lstKey.append(tree.getpath(p).replace("/",".")[1:])
    lstValue.append(p.text)

df = pd.DataFrame({'key' : lstKey, 'value' : lstValue})
df.sort_values('key')

Result:

Sould print this:

# importing csv module 
import csv 

# csv file name 
filename = "aapl.csv"

# initializing the titles and rows list 
fields = [] 
rows = [] 

# reading csv file 
with open(filename, 'r') as csvfile: 
    # creating a csv reader object 
    csvreader = csv.reader(csvfile) 

    # extracting field names through first row 
    fields = csvreader.next() 

    # extracting each data row one by one 
    for row in csvreader: 
        rows.append(row) 

    # get total number of rows 
    print("Total no. of rows: %d"%(csvreader.line_num)) 

# printing the field names 
print('Field names are:' + ', '.join(field for field in fields)) 

# printing first 5 rows 
print('\nFirst 5 rows are:\n') 
for row in rows[:5]: 
    # parsing each column of a row 
    for col in row: 
        print("%10s"%col), 
    print('\n') 

Source: https://www.geeksforgeeks.org/working-csv-files-python/

Given the two levels of nodes that cover the Coluna attributes, consider XSLT, the special-purpose language designed to transform or style original XML files. Python's lxml can run XSLT 1.0 scripts and being the default parse to pandas.read_xml can transform your raw XML into a flatter version to parse to DataFrame.

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

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

    <!-- REDESIGN XML TO ONLY RETURN AnaliseDiaria NODES -->
    <xsl:template match="/*">
     <xsl:copy>
       <xsl:apply-templates select="descendant::pace:AnaliseDiaria"/>
     </xsl:copy>
    </xsl:template>
    
    <!-- REDESIGN AnaliseDiaria NODES -->
    <xsl:template match="pace:AnaliseDiaria">
     <xsl:copy>
       <!-- BRING DOWN Produto ATTRIBUTES WITH CURRENT ATTRIBUTES -->
       <xsl:copy-of select="ancestor::pace:Produto/@*|@*"/>
     </xsl:copy>
    </xsl:template>
    
</xsl:stylesheet>

Online Demo

Python

analise_diaria_df = pd.read("input.xml", stylesheet="style.xsl")

analise_diaria_df 
#        Coluna1   Coluna2  Coluna3  ...    Coluna14  Coluna15   Coluna16
# 0    21-851611  CAMIO VO      NaN  ...         NaN       NaN        NaN
# 1   21-3667984    SCA4X2     -1.0  ...         NaN       NaN        NaN
# 2   21-3667994    SCA963     -1.0  ...         NaN       NaN        NaN
# 3   21-3676543    SCA713     -1.0  ...         NaN       NaN        NaN
# 4   21-3676601     SCA97     -1.0  ...         NaN       NaN        NaN
# 5   21-3814014    CAMIX2      NaN  ...         NaN       NaN        NaN
# 6   21-3814087     SCA56      NaN  ...         NaN       NaN        NaN
# 7   21-3814087     SCA56      NaN  ...  195.000,00       NF9  10203910A
# 8   21-3814087     SCA56      NaN  ...  195.090,00       NaN        NaN
# 9   21-3814087     SCA56      NaN  ...  195.270,00       NaN        NaN
# 10  21-3814087     SCA56      NaN  ...  195.482,60       NaN        NaN
# 11  21-3814087     SCA56      NaN  ...  195.627,80       NaN        NaN
# 12  21-3814087     SCA56      NaN  ...  204.529,82       NaN        NaN
# 13  21-3814087     SCA56      NaN  ...         NaN       NaN     158PES

One way to achieve this is to use XSLT Transformation. Most programming languages including Python will have support to convert an XML document into another document (e.g. HTML) when supplied with an XSL.

A good tutorial on XSLT Transformation can be found here

Use of Python to achieve transformation (once an XSL is prepared) is described here

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.