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

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.