Import XML to MYSQL - mysql

I need to create Mysql tables using this XML but i can't find the way to import all the child's data.
This is the XML.
<product>
<ref>2101</ref>
<name>VERSALLES</name>
<printjobs>
<printjob>
<teccode>100115</teccode>
<tecname>TAMPOGRAFÍA E
</tecname>
<areas>
<area>
<areacode>384</areacode>
<areaname>En la tapa</areaname>
</area>
</areas>
</printjob>
<printjob>
<teccode>101200</teccode>
<tecname>TERMOGRABADO SECO
</tecname>
<areas>
<area>
<areacode>488</areacode>
<areaname>Esquina inferior derecha de la portada</areaname>
</area>
</areas>
</printjob>
<printjob>
<teccode>101201</teccode>
<tecname>TERMOGRABADO CINTA COLOR
</tecname>
<areas>
<area>
<areacode>488</areacode>
<areaname>Esquina inferior derecha de la portada</areaname>
</area>
</areas>
</printjob>
</printjobs>
</product>
<product>
<ref>2233</ref>
<name>1L</name>
<printjobs>
<printjob>
<teccode>100115</teccode>
<tecname>TAMPOGRAFÍA E
</tecname>
<areas>
<area>
<areacode>259</areacode>
<areaname>En el cuerpo</areaname>
</area>
</areas>
</printjob>
</printjobs>
</product>
I tried to import using LOAD DATA LOCAL INFILE to create a different table for area, printjob and product but its only imports the first area of every product.
This is the SQL that i used for area.
LOAD DATA LOCAL INFILE '/var/lib/mysql/allprintdatafile_esp.xml'
INTO TABLE areas
CHARACTER SET 'utf8'
LINES STARTING BY '<product>' TERMINATED BY '</product>'
(#product)
SET
id = NULL,
id_area = ExtractValue(#product, '//areacode'),
id_print_job = ExtractValue(#product, '//teccode'),
id_product = ExtractValue(#product, '//ref'),
areaname = ExtractValue(#product, '//areaname'),
);
Once imported I need the table to look like this.
id
id_area
id_print_job
id_product
areaname
1
384
100115
2101
En la tapa
2
488
101200
2101
Esquina inferior derecha de la portada
3
488
101201
2101
En la tapa
4
259
100115
2233
En el cuerpo
Thank you.

Related

How to write single CSV file using pyspark in Databricks

Good morning all!!
Yesterday I was looking for a function that was able to write a single file CSV with pyspark in Azure Databricks but I did not find anything. So I've built my own function and I wanted to share my solution with the community and if it's possible create like a thread with different solutions for the same problem.
Sorry, because I commented the code in Spanish but basically the function does:
Save the dataframe you've created into a new directory (which is allocated inside the path you've defined and begin with 'temp_') and save the partition there using "coalesce(1)"
Rename the CSV file as you want and moves it to the desired path
Delete de temporary file
That's all! You have your unique CSV file
def escribe_fichero_unico(dataframe, path, file_name, file_format = 'csv'):
"""
Definición: (1) Genera carpeta temporal para guardar particiones que Spark genera por defecto
a la hora de guardar archivos, (2) Une todas las particiones en un único archivo, (3) Mueve este
archivo al directorio anterior y (4) Borra la carpeta temporal
Parámetros:
dataframe: dataframe que quieras guardar como fichero único
file_name: en formato string escribe nombre del archivo
file_format: en formato string escribe 'csv' o 'parquet'
path: en formato string escribe el path donde quieres guardar el csv
"""
import os
# 1) Guardamos el dataframe creando una carpeta temporal que guarda todas las particiones
path_temp = path + 'temp_' + file_name + '_trash'
if file_format == 'csv':
dataframe.coalesce(1).write.format('csv').mode('overwrite') \
.options(header="true", schema="true", delimiter=";") \
.save(path_temp)
else if file_format == 'parquet':
dataframe.coalesce(1).write.format("parquet").mode("overwrite") \
.save(path_temp)
# 2)Une todas las particiones en un único archivo
file_part = [file.path for file in dbutils.fs.ls(path_temp) if os.path.basename(file.path).startswith('part')][0]
# 3) Mueve este archivo al directorio anterior
dbutils.fs.mv(file_part, path + file_name + '.' + file_format)
# 4) Borra la carpeta temporal
dbutils.fs.rm(path_temp, True)
I hope this work for you as well :)

Is there a way to extract part of a dataframe using pandas (or other library) in Python?

Hi I am new on Python and pandas. So please excuse my mistakes.
I want to know if it's possible to use a dataframe and sort it with pandas.
I'm still in class, but I'd like to advance in my projects.
I want / I'd like to extract part of the JSON file.
I want all the system_name =Métro de Paris to be in a JSON file.
Following are the current code I have (JSON and script).
Thanks in advance for your help!
import pandas as pd
# parse according to "system_name":"Métro de Paris"
data = pd.read_json(r'paris_lines_systems_and_modes.json')
[{"id":406,"name":"Ancien M 2nord","url_name":"406-ancien-m-2nord","color":"#000","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":405,"name":"Ancien M 2sud","url_name":"405-ancien-m-2sud","color":"#000","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":399,"name":"Ancien M 14","url_name":"399-ancien-m-14","color":"#000","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":460,"name":"Ancienne ligne A Nord-Sud","url_name":"460-ancienne-ligne-a-nord-sud","color":"#000","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":459,"name":"Ancienne ligne B Nord-Sud","url_name":"459-ancienne-ligne-b-nord-sud","color":"#000","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":411,"name":"Austerlitz-Auteuil","url_name":"411-austerlitz-auteuil","color":"#000","system_id":303,"system_name":"Navettes fluviales","transport_mode_id":0,"transport_mode_name":"default"},{"id":471,"name":"CDG Express","url_name":"471-cdgexpress","color":"#f0871f","system_id":299,"system_name":"Dessertes aéroportuaires","transport_mode_id":7,"transport_mode_name":"people_mover"},{"id":465,"name":"CDGVal 1","url_name":"465-cdgval-1","color":"#0a3071","system_id":299,"system_name":"Dessertes aéroportuaires","transport_mode_id":7,"transport_mode_name":"people_mover"},{"id":466,"name":"CDGVal 2","url_name":"466-cdgval-2","color":"#0a3071","system_id":299,"system_name":"Dessertes aéroportuaires","transport_mode_id":7,"transport_mode_name":"people_mover"},{"id":410,"name":"Charenton-Auteuil","url_name":"410-charenton-auteuil","color":"#000","system_id":303,"system_name":"Navettes fluviales","transport_mode_id":0,"transport_mode_name":"default"},{"id":407,"name":"Funiculaire de Monmartre","url_name":"407-funiculaire","color":"#601e92","system_id":302,"system_name":"Funiculaire","transport_mode_id":0,"transport_mode_name":"default"},{"id":381,"name":"H","url_name":"381-h","color":"#7b4339","system_id":298,"system_name":"Transilien","transport_mode_id":2,"transport_mode_name":"inter_city_rail"},{"id":382,"name":"J","url_name":"382-j","color":"#cdcd00","system_id":298,"system_name":"Transilien","transport_mode_id":2,"transport_mode_name":"inter_city_rail"},{"id":383,"name":"K","url_name":"383-k","color":"#c7b300","system_id":298,"system_name":"Transilien","transport_mode_id":2,"transport_mode_name":"inter_city_rail"},{"id":384,"name":"L","url_name":"384-l","color":"#7584bc","system_id":298,"system_name":"Transilien","transport_mode_id":2,"transport_mode_name":"inter_city_rail"},{"id":472,"name":"Le Bus Direct 1","url_name":"472-le-bus-direct-1","color":"#b20066","system_id":299,"system_name":"Dessertes aéroportuaires","transport_mode_id":8,"transport_mode_name":"bus"},{"id":473,"name":"Le Bus Direct 2","url_name":"473-le-bus-direct-2","color":"#7bbb58","system_id":299,"system_name":"Dessertes aéroportuaires","transport_mode_id":8,"transport_mode_name":"bus"},{"id":474,"name":"Le Bus Direct 3","url_name":"474-le-bus-direct-3","color":"#0098d8","system_id":299,"system_name":"Dessertes aéroportuaires","transport_mode_id":8,"transport_mode_name":"bus"},{"id":475,"name":"Le Bus Direct 4","url_name":"475-le-bus-direct-4","color":"#ff9b00","system_id":299,"system_name":"Dessertes aéroportuaires","transport_mode_id":8,"transport_mode_name":"bus"},{"id":412,"name":" Le Louvre-Ablon","url_name":"412-le-louvre-ablon","color":"#000","system_id":303,"system_name":"Navettes fluviales","transport_mode_id":0,"transport_mode_name":"default"},{"id":643,"name":"Ligne Horizon","url_name":"643-ligne-horizon","color":"#0092e2","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":352,"name":"M 1","url_name":"352-ligne-1","color":"#ffcd00","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":353,"name":"M 2","url_name":"353-ligne-2","color":"#006db8","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":354,"name":"M 3","url_name":"354-ligne-3","color":"#9b993b","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":355,"name":"M 3bis","url_name":"355-ligne-3bis","color":"#88d3df","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":356,"name":"M 4","url_name":"356-ligne-4","color":"#ba4a9a","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":357,"name":"M 5","url_name":"357-ligne-5","color":"#f78e4b","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":358,"name":"M 6","url_name":"358-ligne-6","color":"#77c596","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":303,"name":"M 7","url_name":"303-ligne-7","color":"#f49eb2","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":359,"name":"M 7bis","url_name":"359-ligne-7bis","color":"#77c596","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":360,"name":"M 8","url_name":"360-ligne-8","color":"#c5a3cc","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":361,"name":"M 9","url_name":"361-ligne-9","color":"#cdc92b","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":362,"name":"M 10","url_name":"362-ligne-10","color":"#dfb03a","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":363,"name":"M 11","url_name":"363-ligne-11","color":"#8c6539","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":364,"name":"M 12","url_name":"364-ligne-12","color":"#008c59","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":365,"name":"M 13","url_name":"365-ligne-13","color":"#87d2df","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":366,"name":"M 14","url_name":"366-ligne-14","color":"#662c91","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":395,"name":"M 15","url_name":"395-m-15","color":"#a60f31","system_id":300,"system_name":"Grand Paris Express","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":396,"name":"M 16","url_name":"396-m-16","color":"#ec7cae","system_id":300,"system_name":"Grand Paris Express","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":397,"name":"M 17","url_name":"397-m-17","color":"#ec7cae","system_id":300,"system_name":"Grand Paris Express","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":398,"name":"M 18","url_name":"398-m-18","color":"#95bf32","system_id":300,"system_name":"Grand Paris Express","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":385,"name":"N","url_name":"385-n","color":"#00a092","system_id":298,"system_name":"Transilien","transport_mode_id":2,"transport_mode_name":"inter_city_rail"},{"id":476,"name":"Navette","url_name":"476-navette","color":"#000","system_id":83,"system_name":"Métro de Paris","transport_mode_id":4,"transport_mode_name":"heavy_rail"},{"id":394,"name":"Orlyval","url_name":"394-orlyval","color":"#7d7e7e","system_id":299,"system_name":"Dessertes aéroportuaires","transport_mode_id":7,"transport_mode_name":"people_mover"},{"id":386,"name":"P","url_name":"386-p","color":"#f0b600","system_id":298,"system_name":"Transilien","transport_mode_id":2,"transport_mode_name":"inter_city_rail"},{"id":387,"name":"R","url_name":"387-r","color":"#e4b4d1","system_id":298,"system_name":"Transilien","transport_mode_id":2,"transport_mode_name":"inter_city_rail"},{"id":376,"name":"RER A","url_name":"376-rer-a","color":"#ec1b2a","system_id":297,"system_name":"RER","transport_mode_id":3,"transport_mode_name":"commuter_rail"},{"id":377,"name":"RER B","url_name":"377-rer-b","color":"#4c90cc","system_id":297,"system_name":"RER","transport_mode_id":3,"transport_mode_name":"commuter_rail"},{"id":378,"name":"RER C","url_name":"378-rer-c","color":"#ffdc1d","system_id":297,"system_name":"RER","transport_mode_id":3,"transport_mode_name":"commuter_rail"},{"id":379,"name":"RER D","url_name":"379-rer-d","color":"#00ab66","system_id":297,"system_name":"RER","transport_mode_id":3,"transport_mode_name":"commuter_rail"},{"id":380,"name":"RER E","url_name":"380-rer-e","color":"#c67db4","system_id":297,"system_name":"RER","transport_mode_id":3,"transport_mode_name":"commuter_rail"},{"id":414,"name":"STCRP","url_name":"414-stcrp","color":"#000","system_id":303,"system_name":"Navettes fluviales","transport_mode_id":0,"transport_mode_name":"default"},{"id":367,"name":"T 1","url_name":"367-ligne-1","color":"#14609e","system_id":296,"system_name":"Tramway d'Île-de-France","transport_mode_id":5,"transport_mode_name":"light_rail"},{"id":368,"name":"T 2","url_name":"368-ligne-2","color":"#ba498d","system_id":296,"system_name":"Tramway d'Île-de-France","transport_mode_id":5,"transport_mode_name":"light_rail"},{"id":369,"name":"T 3a","url_name":"369-ligne-3a","color":"#ef7d2d","system_id":296,"system_name":"Tramway d'Île-de-France","transport_mode_id":5,"transport_mode_name":"light_rail"},{"id":370,"name":"T 3b","url_name":"370-ligne-3b","color":"#009b41","system_id":296,"system_name":"Tramway d'Île-de-France","transport_mode_id":5,"transport_mode_name":"light_rail"},{"id":371,"name":"T 4","url_name":"371-ligne-4","color":"#d9922b","system_id":296,"system_name":"Tramway d'Île-de-France","transport_mode_id":5,"transport_mode_name":"light_rail"},{"id":372,"name":"T 5","url_name":"372-ligne-5","color":"#914e8e","system_id":296,"system_name":"Tramway d'Île-de-France","transport_mode_id":5,"transport_mode_name":"light_rail"},{"id":373,"name":"T 6","url_name":"373-ligne-7","color":"#e95c36","system_id":296,"system_name":"Tramway d'Île-de-France","transport_mode_id":5,"transport_mode_name":"light_rail"},{"id":375,"name":"T 7","url_name":"375-t-7","color":"#885f30","system_id":296,"system_name":"Tramway d'Île-de-France","transport_mode_id":5,"transport_mode_name":"light_rail"},{"id":374,"name":"T 8","url_name":"374-t-8","color":"#8c8235","system_id":296,"system_name":"Tramway d'Île-de-France","transport_mode_id":5,"transport_mode_name":"light_rail"},{"id":389,"name":"T 9","url_name":"389-t-9","color":"#009cb4","system_id":296,"system_name":"Tramway d'Île-de-France","transport_mode_id":5,"transport_mode_name":"light_rail"},{"id":390,"name":"T 10","url_name":"390-t-10","color":"#8c8235","system_id":296,"system_name":"Tramway d'Île-de-France","transport_mode_id":5,"transport_mode_name":"light_rail"},{"id":391,"name":"T 11","url_name":"391-t-11","color":"#df5f3d","system_id":296,"system_name":"Tramway d'Île-de-France","transport_mode_id":5,"transport_mode_name":"light_rail"},{"id":506,"name":"T 11 express","url_name":"506-t-11-express","color":"#e2562e","system_id":296,"system_name":"Tramway d'Île-de-France","transport_mode_id":5,"transport_mode_name":"light_rail"},{"id":392,"name":"T 12","url_name":"392-t-12","color":"#a02a3a","system_id":296,"system_name":"Tramway d'Île-de-France","transport_mode_id":5,"transport_mode_name":"light_rail"},{"id":393,"name":"T 13","url_name":"393-t-13","color":"#885f30","system_id":296,"system_name":"Tramway d'Île-de-France","transport_mode_id":5,"transport_mode_name":"light_rail"},{"id":400,"name":"T Zen 1","url_name":"400-t-zen-1","color":"#c6cd01","system_id":301,"system_name":"TZen","transport_mode_id":6,"transport_mode_name":"brt"},{"id":401,"name":"T Zen 2","url_name":"401-t-zen-2","color":"#c6cd01","system_id":301,"system_name":"TZen","transport_mode_id":6,"transport_mode_name":"brt"},{"id":402,"name":"T Zen 3","url_name":"402-t-zen-3","color":"#c6cd01","system_id":301,"system_name":"TZen","transport_mode_id":6,"transport_mode_name":"brt"},{"id":403,"name":"T Zen 4","url_name":"403-t-zen-4","color":"#c6cd01","system_id":301,"system_name":"TZen","transport_mode_id":6,"transport_mode_name":"brt"},{"id":404,"name":"T Zen 5","url_name":"404-t-zen-5","color":"#c6cd01","system_id":301,"system_name":"TZen","transport_mode_id":6,"transport_mode_name":"brt"},{"id":409,"name":"Tuileries-Suresnes","url_name":"409-tuileries-suresnes","color":"#000","system_id":303,"system_name":"Navettes fluviales","transport_mode_id":0,"transport_mode_name":"default"},{"id":388,"name":"U","url_name":"388-u","color":"#d60058","system_id":298,"system_name":"Transilien","transport_mode_id":2,"transport_mode_name":"inter_city_rail"},{"id":408,"name":"Voguéo","url_name":"408-vogueo","color":"#99d420","system_id":303,"system_name":"Navettes fluviales","transport_mode_id":0,"transport_mode_name":"default"}]
So I managed to find how do extract part of the data you want to use / analyse.
You should simply call the column you are trying to extract (in my case system_name) and give its value (in my case "Métro de Paris"). Here The dataframe, data, will now have the data with value "Métro de Paris" form the column "system_name".
data = data[data.system_name == "Métro de Paris"]

0 records when importing a JSON file in mysql WorkBench

I'm trying to import a .json into a database using MySQL workbench, but the "Table data import wizard" always end up with 0 records.
here's the first line of my .json:
{
"Title":"Fort de Villiers",
"Keywords":"Histoire",
"Descripion":"Le fort de Villiers est un ancien fort du système défensif de Paris, situé sur la commune de Noisy-le-Grand, dans le département français de Seine-Saint-Denis. Construit à la fin du XIXᵉ siècle, il faisait partie du système Séré de Rivières, destiné a établir un rideau défensif autour de Paris.",
"Images":["https://lh3.googleusercontent.com/proxy/J-7ut85_OfCJn33_EZcooBkGyjxGBXt5CifUJ_bGzHaZUHlT4XVYwNaIKpZ4hScn3ICcUMI12RZ4xzTxUGmN1LCK694hynag4zh68dLypDwQ_Gy9NwCIFweGTjkh3Wft4IMr0No1tFWbTQ6oBnmLgRaomsWzvoJrTa8=w296-h202-n-k-no","https://lh5.googleusercontent.com/p/AF1QipNs2wi72SiKVlLr1WHIPfAaATrXOfhswYWEIaOw=w296-h202-n-k-no","https://lh5.googleusercontent.com/p/AF1QipMIY16VgeazmwQAP7PB5biaq5p-9uE3OZ-zVYZB=w296-h202-n-k-no"],
"Lattitude":"48.833573",
"Longitude":"2.5463771",
"Map_url":"https://www.google.com/maps/place/Fort+de+Villiers/#48.833573,2.5463771,15z/data=!4m5!3m4!1s0x47e60e0d72beac57:0xd4b8549d2165eef7!8m2!3d48.8335733!4d2.5551319"
}
when i try to import it, i got those fields proposed:
Title : Text
Description : Text
Longitude : Double
Images : json
Map_url : Text
Keywords : Text
Lattitude : Double
With some research i found out that it may be that the "text" columns countains characters that doesn't fill the requiring format, but i didn't manage to get the right format.
Do i need to modify my .json to get a correct utf8 format?
I'm actually using mysql 8.

Python decoding String with json

I am trying to decode this string with python by using the JSON module but I keep getting this error I do not know what is the problem I will appreciate any help with this
here is my code
print(json.loads(data))
i get this error raise JSONDecodeError("Extra data", s, end)
json.decoder.JSONDecodeError: Extra data: line 1 column 38284 (char 38283)
here is the string
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Your string is not a valid JSON, it is missing many closing brackets. You can use a JSON validator online such as https://jsonformatter.curiousconcept.com that will show you all the errors.
To be more specific add ]}}]}}}}]}} at the end of your string and you are good to go.

Need help on an ER Diagram for a “Wireless Internet Service Provider”

I have this diagram.
Diagram (1/2)
Diagram (2/2)
Just in case : Equipo (Device), Servicio (Service) , Categoria(Category), Persona (Person), Comprobante (Voucher,Receipt)
I need to be able to do the following:
"Cuando un cliente informa de un equipo al cual se le suministrará alguno de los
servicios es necesario llevar registro de su fecha de alta, como también su fecha de baja si la hubiere.(Whenever a client informs of a device which is going to be given a service, it is needed to register the activation date and the deactivation date if there is one)
The first thing I tought was to just add attributes to the service table, for example ( act_date timestamp and dea_date timestamp), would that be okey ? Or maybe create another table, like the one that says "Categoria"(Category), but to save those dates. Any ideas ?
Thank you.