To parse an XML file into a Pandas DataFrame, you can use the from_dict method of the DataFrame class. First, you will need to use the ElementTree module to parse the XML file and extract the relevant data. Here is an example of how this can be done: import xml.etree.ElementTree as ET import pandas as pd Parse the XML file using ElementTree tree = ET.parse('my_file.xml') root = tree.getroot() Extract the column names from the 'columns' element columns = [col.attrib['friendlyName'] for col in root.find('columns')] Create an empty list to store the data for each row data = [] Iterate over the 'row' elements and extract the data for each one for row in root.find('rows'): row_data = {} for col in row: # Add the data for each column to the dictionary row_data[col.attrib['name']] = col.text # Add the dictionary for this row to the list data.append(row_data) Create a DataFrame using the column names and data df = pd.DataFrame.from_dict(data, columns=columns) This code will parse the XML file and extract the data for each row and column, storing it in a dictionary. The dictionary is then used to create a DataFrame using the from_dict method. This DataFrame will have the column names as the columns and each row of data as a row in the DataFrame. More on reddit.com

Hello again. It may be simpler to skip the pandas xml methods and use something else. Even reading this data is a problem: >>> pd.read_xml("merge.xml.1678125433.bak").T 0 1 Company x None SenderName y None SenderPhone z None TransferDate a None BrandAAIAID b None DocumentTitle c None DocFormNumber 2.0 NaN EffectiveDate 2023-02-22 None SubmissionType FULL None MapperCompany d None MapperContact e None MapperPhone f None MapperEmail g None VcdbVersionDate 2023-01-26 None QdbVersionDate 2023-01-26 None PcdbVersionDate 2023-01-26 None action None A id NaN 1.0 BaseVehicle NaN NaN BodyType NaN NaN EngineBase NaN NaN Note None WITHOUT AUTO LEVELING SYSTEM Qty NaN 1.0 PartType NaN NaN Position NaN NaN Part NaN 701940.0 See how there is only a single id - all of the other id values in your data are lost. It looks like xmltodict may be the best choice here. import xmltodict from pathlib import Path xmldict = xmltodict.parse(Path("merge.xml").read_bytes()) df = pd.json_normalize(xmldict).explode("ACES.App") df["Aces.App.Part"] = pd.json_normalize(df["ACES.App"]).set_index(df.index)["Part"] I'm guessing this is the Part ID used in your unique checks. This will give you: ACES.@version 4.2 4.2 ACES.Header.Company x x ACES.Header.SenderName y y ACES.Header.SenderPhone z z ACES.Header.TransferDate a a ACES.Header.BrandAAIAID b b ACES.Header.DocumentTitle c c ACES.Header.DocFormNumber 2.0 2.0 ACES.Header.EffectiveDate 2023-02-22 2023-02-22 ACES.Header.SubmissionType FULL FULL ACES.Header.MapperCompany d d ACES.Header.MapperContact e e ACES.Header.MapperPhone f f ACES.Header.MapperEmail g g ACES.Header.VcdbVersionDate 2023-01-26 2023-01-26 ACES.Header.QdbVersionDate 2023-01-26 2023-01-26 ACES.Header.PcdbVersionDate 2023-01-26 2023-01-26 ACES.App {'@action': 'A', '@id': '1', 'BaseVehicle': {'... {'@action': 'B', '@id': '2', 'BaseVehicle': {'... Aces.App.Part 701940 12345 To go back to XML - you "reverse" the "json_normalize" def df_to_xmltodict(df, sep="."): result = {"ACES": {"Header": {}, "App": []}} for _, row in df.iterrows(): new_row = {} for name, value in row.items(): keys = name.split(sep) count = len(keys) - 1 parent = new_row for index, key in enumerate(keys): if index == count: parent[key] = value else: parent.setdefault(key, {}) parent = parent[key] result["ACES"]["App"].append(new_row["ACES"]["App"]) del new_row["ACES"]["App"] del new_row["Aces"] for key, value in new_row.items(): result["ACES"].update(value) return result You can then give this to xmltodict.unparse to go back to XML: >>> print(xmltodict.unparse(df_to_xmltodict(df), pretty=True)) x y z a b c 2.0 2023-02-22 FULL d e f g 2023-01-26 2023-01-26 2023-01-26 WITHOUT AUTO LEVELING SYSTEM 1 701940 YOLO 1 12345 More on reddit.com