I'm doing a mapping of data. I have a lot of tables, one of them is "abonnement" for example, here I have multiple columns (id, datemajaudit, etc.).
Example of col dictionary;
{'abonnement': ['id',
'datemajaudit',
'objversion',
'profilmajaudit',
'supprime',
'userconnected',
'categorieabonnement',
'code',
'isaffichable',
'isextranetunsubscribed',
'libelle',
'media'],
'abonnement_carte_paiement': ['id',
'datemajaudit',
'objversion',
for every table, I created a variable that is named d_{table}, d_abonnement for example that contains some information about every column of the table.
Example of d_constab_assoc_equipement_infos_etat
#out[9]:
#[{1000: 3, 1002: 3}, {1022: 1, 1044: 1, 1059: 1, 1049: 1, 1051: 1, 1061: 1}]
Now I want to put this information on a txt, in this format:
abonnement
id
1000: 3, |
1002: 3, |
datemajaudit
1022: 1,
1044: 1,
1059: 1,
1049: 1,
1051: 1,
1061: 1
I do it with this code :
ab = open("premier.txt","w")
t=[0]
exec(f'''for i, zones in list(col.items())[0:300]:
print(f'\t%s'%i, file=ab)
for t, zone in enumerate(zones):
print(f'\t\t%s'%zone, file=ab)
print("\\t\t\t\t")
for q, v in d_{i}[t].items():
print("èèèèè", q, "(", v, '),', end = " ", file=ab)
print("|", file=ab)''')
ab.close()
The issue is that I have some difficulties to call the variable that I created, I want to dynamically call the variable with this name: d_abonnement[1], d_abonnement[2], d_abonnement[3], and want to do it for all tables so also d_constab_assoc_equipement_infos_etat[0], d_constab_assoc_equipement_infos_etat[1], constab_assoc_equipement_infos_etat[2], etc, d_devis[0], d_devis[1], d_devis[2]
But with this part of my code for q, v in d_{i}[t].items(): I have some issues, because it returns me on my file txt all the table, for each table, the good columns, but for all tables I have the values of the first table. In other words I cannot have some other tables names d_constab_assoc_equipement_infos_etat[0], d_devis[0], etc, but only one table name, the first one that turns with the code, and it fullfils the data for all the others.
Related
[{'id': 2, 'Registered Address': 'Line 1: 1 Any Street Line 2: Any locale City: Any City Region / State: Any Region Postcode / Zip code: BA2 2SA Country: GB Jurisdiction: Any Jurisdiction'}]
I have the above read into a dataframe and that is the output so far. The issue is I need to break out the individual elements - due to names of places etc the values may or may not have spaces in them - looking at the above my keys are Line 1, Line 2, City, Region / State, Postcode / Zip, Country, Jurisdiction.
Output required for the "Registered Address"-'key'is the keys and values
"Line 1": "1 Any Street"
"Line 2": "Any locale"
"City": "Any City"
"Region / State": "Any Region"
"Postcode / Zip code": "BA2 2SA"
"Country": "GB"
"Jurisdiction": "Any Jurisdiction"
Just struggling to find a way to get to the end result.I have tried to pop out and use urllib.prse but fell short - is anypone able to point me in the best direction please?
Tried to write a code that generalizes your question, but there were some limitations, regarding your data format. Anyway I would do this:
def address_spliter(my_data, my_keys):
address_data = my_data[0]['Registered Address']
key_address = {}
for i,k in enumerate(keys):
print(k)
if k == 'Jurisdiction:':
key_address[k] = address_data.split('Jurisdiction:')[1].removeprefix(' ').removesuffix(' ')
else:
key_address[k] = address_data.split(k)[1].split(keys[i+1])[0].removeprefix(' ').removesuffix(' ')
return key_address
were you can call this function like this:
my_data = [{'id': 2, 'Registered Address': 'Line 1: 1 Any Street Line 2: Any locale City: Any City Region / State: Any Region Postcode / Zip code: BA2 2SA Country: GB Jurisdiction: Any Jurisdiction'}]
and
my_keys = ['Line 1:','Line 2:','City:', 'Region / State:', 'Postcode / Zip code:', 'Country:', 'Jurisdiction']
As you can see It'll work if only the sequence of keys is not changed. But anyway, you can work around this idea and change it base on your problem accordingly if it doesn't go as expected.
I am trying to create an invoice from a custom object but I am getting errors from on validation .When I post, i get the following error: "
ValueError: Wrong value for account.move.line_ids: {'display_type': 'line_section', 'name': 'Phone Bill', 'product_id': 11783, 'product_uom_id': 19, 'current_reading': 66.0, 'current_date': datetime.date(2020, 11, 3), 'quantity': 17.0, 'price_unit': 565.0, 'account_id': 19, 'debit': 9605.0, 'credit': 0.0}
current_date and current_reading are custom fields i created. I am aware that Odoo automatically creates values for line_ids from invoice_line_ids if line_ids is not provided, so I am really stuck about this error.
Here's my code for creating the invoice:
class ReadingCorrection(models.TransientModel):
_name = 'reading.upload.wizard'
_description = 'Validate reading uploads'
def validate_entry(self):
active_ids = self._context.get('active_ids', []) or []
company = self.env.user.company_id
journal = self.env['account.move'].with_context(force_company=company.id, type='out_invoice')._get_default_journal()
for reads in self.env['reading.upload'].browse(active_ids):
if reads.reading >= reads.previous_reading: # and reads.state == 'draft':
account = reads.product_id.product_tmpl_id._get_product_accounts()['income']
if not account:
raise UserError(_('No account defined for product "%s".') % reads.product_id.name)
invoice = {
'type': 'out_invoice',
'invoice_date':reads.read_date,
'narration': reads.remark,
'invoice_user_id': reads.current_user.id,
'partner_id': reads.meter_id.customer_id.id,
'journal_id': 1,#journal.id,
'currency_id': reads.meter_id.customer_id.currency_id.id,
'doc_type': 'bill',
'invoice_line_ids':[(0,0, {
'name': reads.product_id.name,
'product_id': reads.product_id.id,
'product_uom_id': reads.product_id.uom_id.id,
'current_reading': reads.reading,
'previous_reading': reads.previous_reading,
'current_date': reads.read_date,
'quantity': reads.reading - reads.previous_reading,
'price_unit': reads.product_id.product_tmpl_id.lst_price,
'account_id': account.id,
})]
}
moves = self.env['account.move'].with_context(default_type='out_invoice').create(invoice)
#invoice = self.env['account.move'].sudo().create(invoice)
reads.write({'state':'uploaded'})
Any help given will be appreciated. Thanks
If you want to create invoices, in the lines you should not use the debit and credit fields since these are calculated, as it is a product line, you should not use display_type, since the line_section type is treated as an annotation and not as a price calculation line.
In the invoice data, when linking the lines 'invoice_line_ids': inv_line_ids an instruction must be specified to process the lines in your case it would be as follows 'invoice_line_ids': (0, 0, inv_line_ids) for greater information visit this page.
I have this csv file with only two entries. Here it is:
Meat One,['Abattoirs', 'Exporters', 'Food Delivery', 'Butchers Retail', 'Meat Dealers-Retail', 'Meat Freezer', 'Meat Packers']
First one is title and second is a business headings.
Problem lies with entry two.
Here is my code:
import csv
with open('phonebookCOMPK-Directory.csv', "rt") as textfile:
reader = csv.reader(textfile)
for row in reader:
row5 = row[5].replace("[", "").replace("]", "")
listt = [(''.join(row5))]
print (listt[0])
it prints:
'Abattoirs', 'Exporters', 'Food Delivery', 'Butchers Retail', 'Meat Dealers-Retail', 'Meat Freezer', 'Meat Packers'
What i need to do is that i want to create a list containing these words and then print them like this using for loop to print every item separately:
Abattoirs
Exporters
Food Delivery
Butchers Retail
Meat Dealers-Retail
Meat Freezer
Meat Packers
Actually I am trying to reformat my current csv file and clean it so it can be more precise and understandable.
Complete 1st line of csv is this:
Meat One,+92-21-111163281,Al Shaheer Corporation,Retailers,2008,"['Abattoirs', 'Exporters', 'Food Delivery', 'Butchers Retail', 'Meat Dealers-Retail', 'Meat Freezer', 'Meat Packers']","[[' Outlets Address : Shop No. Z-10, Station Shopping Complex, MES Market, Malir-Cantt, Karachi. Landmarks : MES Market, Station Shopping Complex City : Karachi UAN : +92-21-111163281 '], [' Outlets Address : Shop 13, Ground Floor, Plot 14-D, Sky Garden, Main Tipu Sultan Road, KDA Scheme No.1, Karachi. Landmarks : Nadra Chowrangi, Sky Garden, Tipu Sultan Road City : Karachi UAN : +92-21-111163281 '], ["" Outlets Address : Near Jan's Broast, Boat Basin, Khayaban-e-Roomi, Block 5, Clifton, Karachi. Landmarks : Boat Basin, Jans Broast, Khayaban-e-Roomi City : Karachi UAN : +92-21-111163281 View Map ""], [' Outlets Address : Gulistan-e-Johar, Karachi. Landmarks : Perfume Chowk City : Karachi UAN : +92-21-111163281 '], [' Outlets Address : Tee Emm Mart, Creek Vista Appartments, Khayaban-e-Shaheen, Phase VIII, DHA, Karachi. Landmarks : Creek Vista Appartments, Nueplex Cinema, Tee Emm Mart, The Place City : Karachi Mobile : 0302-8333666 '], [' Outlets Address : Y-Block, DHA, Lahore. Landmarks : Y-Block City : Lahore UAN : +92-42-111163281 '], [' Outlets Address : Adj. PSO, Main Bhittai Road, Jinnah Supermarket, F-7 Markaz, Islamabad. Landmarks : Bhittai Road, Jinnah Super Market, PSO Petrol Pump City : Islamabad UAN : +92-51-111163281 ']]","Agriculture, fishing & Forestry > Farming equipment & services > Abattoirs in Pakistan"
First column is Name
Second column is Number
Third column is Owner
Forth column is Business type
Fifth column is Y.O.E
Sixth column is Business Headings
Seventh column is Outlets (List of lists containing every branch address)
Eighth column is classification
There is no restriction of using csv.reader, I am open to any technique available to clean my file.
Think of it in terms of two separate tasks:
Collect some data items from a ‘dirty’ source (this CSV file)
Store that data somewhere so that it’s easy to access and manipulate programmatically (according to what you want to do with it)
Processing dirty CSV
One way to do this is to have a function deserialize_business() to distill structured business information from each incoming line in your CSV. This function can be complex because that’s the nature of the task, but still it’s advisable to split it into self-containing smaller functions (such as get_outlets(), get_headings(), and so on). This function can return a dictionary but depending on what you want it can be a [named] tuple, a custom object, etc.
This function would be an ‘adapter’ for this particular CSV data source.
Example of deserialization function:
def deserialize_business(csv_line):
"""
Distills structured business information from given raw CSV line.
Returns a dictionary like {name, phone, owner,
btype, yoe, headings[], outlets[], category}.
"""
pieces = [piece.strip("[[\"\']] ") for piece in line.strip().split(',')]
name = pieces[0]
phone = pieces[1]
owner = pieces[2]
btype = pieces[3]
yoe = pieces[4]
# after yoe headings begin, until substring Outlets Address
headings = pieces[4:pieces.index("Outlets Address")]
# outlets go from substring Outlets Address until category
outlet_pieces = pieces[pieces.index("Outlets Address"):-1]
# combine each individual outlet information into a string
# and let ``deserialize_outlet()`` deal with that
raw_outlets = ', '.join(outlet_pieces).split("Outlets Address")
outlets = [deserialize_outlet(outlet) for outlet in raw_outlets]
# category is the last piece
category = pieces[-1]
return {
'name': name,
'phone': phone,
'owner': owner,
'btype': btype,
'yoe': yoe,
'headings': headings,
'outlets': outlets,
'category': category,
}
Example of calling it:
with open("phonebookCOMPK-Directory.csv") as f:
lineno = 0
for line in f:
lineno += 1
try:
business = deserialize_business(line)
except:
# Bad line formatting?
log.exception(u"Failed to deserialize line #%s!", lineno)
else:
# All is well
store_business(business)
Storing the data
You’ll have the store_business() function take your data structure and write it somewhere. Maybe it’ll be another CSV that’s better structured, maybe multiple CSVs, a JSON file, or you can make use of SQLite relational database facilities since Python has it built-in.
It all depends on what you want to do later.
Relational example
In this case your data would be split across multiple tables. (I’m using the word “table” but it can be a CSV file, although you can as well make use of an SQLite DB since Python has that built-in.)
Table identifying all possible business headings:
business heading ID, name
1, Abattoirs
2, Exporters
3, Food Delivery
4, Butchers Retail
5, Meat Dealers-Retail
6, Meat Freezer
7, Meat Packers
Table identifying all possible categories:
category ID, parent category, name
1, NULL, "Agriculture, fishing & Forestry"
2, 1, "Farming equipment & services"
3, 2, "Abattoirs in Pakistan"
Table identifying businesses:
business ID, name, phone, owner, type, yoe, category
1, Meat One, +92-21-111163281, Al Shaheer Corporation, Retailers, 2008, 3
Table describing their outlets:
business ID, city, address, landmarks, phone
1, Karachi UAN, "Shop 13, Ground Floor, Plot 14-D, Sky Garden, Main Tipu Sultan Road, KDA Scheme No.1, Karachi", "Nadra Chowrangi, Sky Garden, Tipu Sultan Road", +92-21-111163281
1, Karachi UAN, "Near Jan's Broast, Boat Basin, Khayaban-e-Roomi, Block 5, Clifton, Karachi", "Boat Basin, Jans Broast, Khayaban-e-Roomi", +92-21-111163281
Table describing their headings:
business ID, business heading ID
1, 1
1, 2
1, 3
…
Handling all this would require a complex store_business() function. It may be worth looking into SQLite and some ORM framework, if going with relational way of keeping the data.
You can just replace the line :
print(listt[0])
with :
print(*listt[0], sep='\n')
I have two dictionaries,
MaleDict = {'Jason':[(2014, 394),(2013, 350)...],
'Stephanie':[(2014, 3), (2013, 21),..]....}
FemaleDict = {'Jason':[(2014, 56),(2013, 23)...],
'Stephanie':[(2014, 335), (2013, 217),..]....}
I am attempting to add them so that
CompleteDict = {'Jason':[(2014, 394, 56),(2013, 350, 23)...],
'Stephanie':[(2014, 3, 335), (2013, 21, 217),..]....}
I have created a list comprehension that completes the task when the each dictionary has that year present. However, I need the output to present even if the year is not present in one of the MaleDict or FemaleDict. For example, if one year was not in the MaleDict the code would read ...'Stephanie':[....., (1999, 0, 389), ....]...
my list comprehensions are
for name_key in name_keys:
for year_key in year_keys:
[BaseDict[name_key].append((year_key, a[1], b[1])) for a in MaleDict[name_key] for b in FemaleDict[name_key] if (year_key == a[0] == b[0])]
#This is where I am stuck. My list comprehensions dont work when there is no value for a specific year
[BaseDict[name_key].append((year_key, a[1], 0)) for a in MaleDict[name_key] for b in FemaleDict[name_key] if (year_key == a[0] != b[0])]
[BaseDict[name_key].append((year_key, 0, b[1])) for a in MaleDict[name_key] for b in FemaleDict[name_key] if (year_key != a[0] == b[0])]
print(BaseDict)
If your data format is not set in stone, i would consider using a defaultdict from collections:
Instead of [(2014, 394),(2013, 350)...]
use collections.defaultdict(int, {2014: 394, 2013: 350}) etc.
Then you can use
for name_key in name_keys:
for year_key in year_keys:
CompleteDict[name_key].update([FemaleDict[year_key], MaleDict[year_key]])
CompleteDict['Stephanie'][1999] will then be [0, 389]
I need to import an excel document into mathematica which has 2000 compounds in it, with each compound have 6 numerical constants assigned to it. The end goal is to type a compound name into mathematica and have the 6 numerical constants be outputted. So far my code is:
t = Import["Titles.txt.", {"Text", "Lines"}] (imports compound names)
n = Import["NA.txt.", "List"] (imports the 6 values for each compound)
n[[2]] (outputs the second compounds 6 values)
Instead of n[[#]] i would like to know how to type in a compound from the imported compound names and have the 6 values be outputted .
I'm not sure if I understand your question - you have two text files, rather than an Excel file, for example, and it's not clear what the data looks like. But there are probably plenty of ways to do this. Here's a suggestion (it might not be the best way):
Let's assume that you've got all your data into a table (a list of lists):
pt = {
{"Hydrogen", "H", 1, 1.0079, -259, -253, 0.09, 0.14, 1776, 1, 13.5984},
{"Helium", "He", 2, 4.0026, -272, -269, 0, 0, 1895, 18, 24.5874},
{"Lithium" , "Li", 3, 6.941, 180, 1347, 0.53, 0, 1817, 1, 5.3917}
}
To find the information associated with a particular string:
Cases[pt, {"Helium", rest__} -> rest]
{"He", 2, 4.0026, -272, -269, 0, 0, 1895, 18, 24.5874}
where the pattern rest__ holds everything that was found after "Helium".
To look for the second item:
Cases[pt, {_, "Li", rest__} -> rest]
{2, 4.0026, -272, -269, 0, 0, 1895, 18, 24.5874}
If you add more information to the patterns, you have more flexibility in how you choose elements from the table:
Cases[pt, {name_, symbol_, aNumber_, aWeight_, mp_, bp_, density_,
crust_, discovered_, rest__}
/; discovered > 1850 -> {name, symbol, discovered}]
{{"Helium", "He", 1895}}
For something interactive, you could knock up a Manipulate:
elements = pt[[All, 1]];
headings = {"symbol", "aNumber", "aWeight", "mp", "bp", "density", "crust", "discovered", "group", "ion"};
Manipulate[
Column[{
elements[[x]],
TableForm[{
headings, Cases[pt, {elements[[x]], rest__} -> rest]}]}],
{x, 1, Length[elements], 1}]