Planet Python

Time 2020-11-01 19:05:34

Web Name: Planet Python

WebSite: http://planetpython.org

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IntroductionMany of the Django tips articles that I see online are geared towards beginners not intermediate or advanced Django developers.I hope to demonstrate some of Django s depth, specifically around the ORM, and you ll need to have an intermediateunderstanding of Django. Let s start by looking at the example models.from django.db import modelsclass Ticker(models.Model):symbol = models.CharField(max_length=50, unique=True)class TickerPrice(models.Model):ticker = models.ForeignKey(Ticker, on_delete=models.CASCADE, related_name= ticker_prices price = models.DecimalField(max_digits=7, decimal_places=2)close_date = models.DateField()For this article, we ll use a stock price tracking application as our example. We have a Ticker model to store eachstock ticker with its symbol, and a TickerPrice model with a many to one relationship to a Ticker where we ll storethe ticker s price and close date.Using Q Objects for Complex QueriesWhen you filter a queryset, you re ANDing the keyword arguments together.Q objects allow Django developersto perform lookups with OR. Q objects can be combined together with , representing AND, or |, representing OR. Let slook at an example query.today_and_yesterday_prices = TickerPrice.objects.filter(models.Q(close_date=today) | models.Q(close_date=yesterday)In this query, we re fetching the ticker prices with close dates of today or yesterday. We wrap our close_date keywordarguments with a Q object and join them together with the OR operator, |. We can also combine the OR and AND operators.today_and_yesterday_greater_than_1000 = TickerPrice.objects.filter(models.Q(price__gt=1000),(models.Q(close_date=today) | models.Q(close_date=yesterday)),For this query, we re getting all prices with a close date of today or yesterday with a price greater than 1000. Bydefault, Q objects, similar to keyword arguments, are ANDed together. We can also use the ~ operator to negate Qobjects.today_and_yesterday_greater_than_1000_without_BRK = (TickerPrice.objects.filter(models.Q(price__gt=1000),~models.Q(ticker__symbol__startswith= BRK ),(models.Q(close_date=today) | models.Q(close_date=yesterday)),In this query, we re fetching all ticker prices greater than 1000 that don t start with BRK with closedates of either today or yesterday. We added the condition that the ticker s symbol does not start with BRK(Berkshire Hathaway), which will exclude those from the query.Optimize Database Calls with Prefetch Related and Select RelatedPrefetch related andselect related provide us with a mechanismto look up objects related to the model that we re querying. We use prefetch related when we want to fetch a reverseforeign key or a many to many relationship. We use select related when we want to fetch a foreign key or a one to onerelationship.apple_with_all_prices = Ticker.objects.prefetch_related( ticker_prices ).get(symbol= AAPL )In this example, we re fetching a single ticker, AAPL, and with this ticker, we re fetching all of the related prices.This helps us optimize our database queries by loading all the related ticker prices instead of fetching them one by one.Without prefetch related, if we looped over ticker_prices.all(), each iteration would result ina database query, but with prefetch related, a loop would result in one database query.latest_prices = TickerPrice.objects.filter(close_date=today).select_related( ticker )Select related works similarly to prefetch related except we use select related for different types of relationships.For this case, we re fetching all ticker prices for today and also fetching the associated ticker. Once again if we loopover latest_prices, referencing a price s ticker won t result in an extra database query.Annotate Querysets to Fetch Specific ValuesAnnotating a queryset enables us to add attributesto each object in the queryset. Annotations can be a reference to a value on the model or related model or an expressionsuch as a sum or count.tickers_with_latest_price = Ticker.objects.annotate(latest_price=TickerPrice.objects.filter(ticker=models.OuterRef( pk ).order_by( -close_date ).values( price )[:1]This queryset fetches all the tickers and annotates each ticker object with a latest_price attribute. The latest pricecomes from the most recent related ticker price. The OuterRef allows us to reference the primary key of the tickerobject. We use order_by to get the most recent price and use values to select only the price. Finally, the [:1]ensures we retrieve only one TickerPrice object.We could also query against our annotation.tickers_with_latest_price = (Ticker.objects.annotate(latest_price=TickerPrice.objects.filter(ticker=models.OuterRef( pk )).order_by( -close_date ).values( price )[:1].filter(latest_price__gte=50)We added an extra filter statement after our annotation. In this query, we fetch all tickers where the latest priceis greater than or equal to fifty.Use Prefetch Objects to Control Your Prefetch RelatedPrefetch objects enable Djangodevelopers to control the operation of prefetch related. When we pass in a string argument to prefetch related, we re sayingfetch all of the related objects. A prefetch object lets us pass in a custom queryset to fetch a subset of the relatedobjects.tickers_with_prefetch = Ticker.objects.all().prefetch_related(models.Prefetch( ticker_prices ,queryset=TickerPrice.objects.filter(models.Q(close_date=today)| models.Q(close_date=yesterday)In this example, we combine a previous query we made for ticker prices from today or yesterday and pass that as the queryset of our prefetch object. We fetch all tickers and with them we fetch all related ticker prices from today and yesterday.Define Custom Query Sets and Model Managers for Code ReuseCustommodel managers and customquerysetslet Django developers add extra methods to or modify the initial queryset for a model. Using these promotes the don trepeat yourself (DRY) principle in software development and promotes reuse of common queries.import datetimefrom django.db import modelsclass TickerQuerySet(models.QuerySet):def annotate_latest_price(self):return self.annotate(latest_price=TickerPrice.objects.filter(ticker=models.OuterRef( pk ).order_by( -close_date ).values( price )[:1]def prefetch_related_yesterday_and_today_prices(self):today = datetime.datetime.today()yesterday = today - datetime.timedelta(days=1)return self.prefetch_related(models.Prefetch( ticker_prices ,queryset=TickerPrice.objects.filter(models.Q(close_date=today)| models.Q(close_date=yesterday)class TickerManager(models.Manager):def get_queryset(self):return TickerQuerySet(self.model, using=self._db)class Ticker(models.Model):symbol = models.CharField(max_length=50, unique=True)objects = TickerManager()class TickerPrice(models.Model):ticker = models.ForeignKey(Ticker, on_delete=models.CASCADE, related_name= ticker_prices price = models.DecimalField(max_digits=7, decimal_places=2)close_date = models.DateField()In the above code, we ve created a custom queryset with some of the previously demonstrated queries as methods. We addedthis new queryset to our custom manager and overrode the default objects manager on the Ticker model with our custommanager. With the custom manager and queryset, we can do the following.tickers_with_prefetch = (Ticker.objects.all().prefetch_related_yesterday_and_today_prices()tickers_with_latest_price = Ticker.objects.all().annotate_latest_price()Instead of having to write the actual query for each of these examples, we call the methods defined in the custom queryset.This is especially useful if we use these queries in multiple places throughout the codebase.Final ThoughtsI hope these Django tips shed some light on the more advanced Django features. The Django ORM has a large set of featuresthat can be overwhelming at the beginning, but once you re past the beginner level, the ORM contains a lot of greatfunctionality that helps maintain a clean codebase and enable complex queries. I encourage you to dive into Django sdocumentation, especially around the ORM, as it is well written with good examples.November 01, 2020 12:00 AM UTCTranslating Web Page while ScrapingSuppose you need to scrape data from a website after translating the web page in R and Python. In google chrome, there is an option (or functionality) to translate any foreign language. If you are an english speaker and don't know any other foreign language and you want to extract data from the website which does not have option to convert language to English, this article would help you how to perform translation of a webpage. What is Selenium?You may not familiar with Selenium so it is important to understand the background. Selenium is an open-source tool which is very popular in testing domain and used for automating web browsers. It allows you to write test scripts in several programming languages. Selenium is available in both R and Python. Translate Page in Web Scraping in R and PythonIn R there is a package named RSelenium whereas Selenium can be installed by installing selenium package in Python. Following is a list of languages chrome supports along with their code. You need this code in making chrome understand from which language to what language you want to translate the web page. NameCode AmharicamArabicarBasqueeuBengalibnEnglish (UK)en-GBPortuguese (Brazil)pt-BRBulgarianbgCatalancaCherokeechrCroatianhrCzechcsDanishdaDutchnlEnglish (US)enEstonianetFilipinofilFinnishfiFrenchfrGermandeGreekelGujaratiguHebrewiwHindihiHungarianhuIcelandicisIndonesianidItalianitJapanesejaKannadaknKoreankoLatvianlvLithuanianltMalaymsMalayalammlMarathimrNorwegiannoPolishplPortuguese (Portugal)pt-PTRomanianroRussianruSerbiansrChinese (PRC)zh-CNSlovakskSlovenianslSpanishesSwahiliswSwedishsvTamiltaTeluguteThaithChinese (Taiwan)zh-TWTurkishtrUrduurUkrainianukVietnameseviWelshcyREAD MORE »October 31, 2020 10:49 PM UTCPython 3.9.0 : Testing twisted python module - part 001 .Today I tested two python modules named: twisted and twisted[tls].Twisted is an event-driven network programming framework written in Python and licensed under the MIT License.Twisted projects variously support TCP, UDP, SSL/TLS, IP multicast, Unix domain sockets, many protocols (including HTTP, XMPP, NNTP, IMAP, SSH, IRC, FTP, and others), and much more. Twisted is based on the event-drivenOctober 31, 2020 10:44 AM UTCPython 3.8.5 : Testing with openpyxl - part 001 .The Python executes the code line by line because is an interpreter language.This allows users to solve issues in the programming area, fast and easy.I use python versiono 3.8.5 build on Aug 12 2020 at 00:00:00, see the result of interactive mode:[mythcat@desk ~]$ pythonPython 3.8.5 (default, Aug 12 2020, 00:00:00) [GCC 10.2.1 20200723 (Red Hat 10.2.1-1)] on linuxType "help", "copyright", "October 31, 2020 10:33 AM UTC#288 10 tips to move from Excel to PythonExcel is one of the most used and most empowering piece of software out there. But that doesn't make it a good fit for every data processing need. And when you outgrow Excel, a really good option for a next step is Python and the data science tech stack: Pandas, Jupyter, and friends. br/ br/ Chris Moffitt is back on Talk Python to give us concrete tips and tricks for moving from Excel to Python! br/ br/ strong Links from the show /strong br/ br/ div b Chris on Twitter /b : a href="https://twitter.com/chris1610" target="_blank" rel="noopener" @chris1610 /a br/ b Practical Business Python /b : a href="https://pbpython.com" target="_blank" rel="noopener" pbpython.com /a br/ b Escaping Excel Hell with Python and Pandas Episode 200 /b : a href="https://talkpython.fm/200" target="_blank" rel="noopener" talkpython.fm /a br/ b SideTable package /b : a href="https://pbpython.com/sidetable.html" target="_blank" rel="noopener" pbpython.com /a br/ br/ b Learn more and go deeper /b br/ b Move from Excel to Python with Pandas Course /b : a href="talkpython.fm/excel" target="_blank" rel="noopener" training.talkpython.fm /a br/ b Excel to Python webcast /b : a href="https://talkpython.fm/excel-webcast" target="_blank" rel="noopener" crowdcast.io /a br/ /div br/ strong Sponsors /strong br/ br/ a href='https://talkpython.fm/voyager' Voyager game /a br a href='https://talkpython.fm/linode' Linode /a br a href='https://talkpython.fm/training' Talk Python Training /a October 31, 2020 08:00 AM UTC#205 This is going to be a little bit awkward p Sponsored by us! Support our work through: /p li Our a href="https://training.talkpython.fm/" strong courses at Talk Python Training /strong /a /li li a href="https://testandcode.com/" strong Test amp; Code /strong /a Podcast /li li a href="https://www.patreon.com/pythonbytes" strong Patreon Supporters /strong /a /li /ul p strong Michael #1: /strong a href="https://awkward-array.org" strong Awkward arrays /strong /a /p li via Simon Thor /li li Awkward Array is a library for strong nested, variable-sized data /strong , including arbitrary-length lists, records, mixed types, and missing data, using strong NumPy-like idioms /strong . /li li This makes it better than numpy at handling data where e.g. the rows in a 2D array have different lengths. It can even be used together with numba to jit-compile the code to make it even faster. /li li Arrays are strong dynamically typed /strong , but operations on them are strong compiled and fast /strong . Their behavior coincides with NumPy when array dimensions are regular and generalizes when they’re not. /li li Recently rewritten in C++ for the 1.0 release and can even be used from C++ as well as Python. /li li Careful on installation: code pip install awkward1 /code ← Notice the 1. /li /ul p strong Brian #2: /strong a href="https://nedbatchelder.com/blog/202010/ordered_dict_surprises.html" strong Ordered dict surprises /strong /a /p li Ned Batchelder /li li “Since Python 3.6, regular dictionaries retain their insertion order: when you iterate over a dict, you get the items in the same order they were added to the dict. Before 3.6, dicts were unordered: the iteration order was seemingly random.” /li li The surprises: li You can’t get the first item, like d[0], since that’s just the value matching key 0, if key 0 exists. (I’m not actually surprised by this.) /li li equality and order (this I am surprised by) li Python 3.6+ dicts ignores order when testing for equality /li li code {"a": 1, "b": 2} == {"b": 2, "a": 1} /code /li /ul /li li OrderdDicts care about order when testing for equality li code OrderedDict([("a", 1), ("b", 2)]) != OrderedDict([("b", 2), ("a", 1)]) /code /li /ul /li /ul /li /ul p strong Michael #3: /strong a href="https://github.com/krassowski/jupyterlab-lsp" jupyter lab autocomplete and more /a /p li via Anders Källmar /li li Examples show Python code, but most features also work in R, bash, typescript, and many other languages. /li li strong Hover: /strong Hover over any piece of code; if an underline appears, you can press Ctrl to get a tooltip with function/class signature, module documentation or any other piece of information that the language server provides /li li strong Diagnostics: /strong Critical errors have red underline, warnings are orange, etc. Hover over the underlined code to see a more detailed message /li li strong Jump to Definition: /strong Use the context menu entries to jump to definitions /li li strong Highlight References: /strong Place your cursor on a variable, function, etc and all the usages will be highlighted /li li strong Automatic Completion: /strong Certain characters, for example '.' (dot) in Python, will automatically trigger completion /li li strong Automatic Signature Suggestions: /strong Function signatures will automatically be displayed /li li strong Rename: /strong Rename variables, functions and more, in both: notebooks and the file editor. /li /ul p strong Brian #4: /strong a href="https://source-separation.github.io/tutorial/landing.html" strong Open Source Tools amp; Data for Music Source Separation /strong /a /p li An online “book” powered by Jupyter Book /li li By Ethan Manilow, Prem Seetharaman, and Justin Salamon /li li A tutorial intended to guide people “through modern, open-source tooling and datasets for running, evaluating, researching, and deploying source separation approaches. We will pay special attention to musical source separation, though we will note where certain approaches are applicable to a wider array of source types.” /li li Uses Python and interactive demos with visualizations. /li li Section “basics of source separation” that includes a primer on digitizing audio signals, a look time frequency representations, what phase is, and some evaluations and measurements. /li li Includes li use of a library called nussl /li li deep learning approaches /li li datasets /li li training deep networks /li /ul /li li Brian’s comments: li Very cool this is an open source book /li li Even if you don’t care about source separation, the primer on waveform digitization is amazing. /li li The interactive features are great. /li /ul /li /ul p strong Michael #5: /strong a href="https://realpython.com/python-pass-by-reference/" strong Pass by Reference in Python: Background and Best Practices /strong /a /p li Does Python have pointers? /li li Some languages handle function arguments as strong references /strong to existing variables, which is known as strong pass by reference /strong . Other languages handle them as strong independent values /strong , an approach known as strong pass by value /strong . /li li Python uses pass by assignment, very similar to pass by ref. /li li In languages that default to passing by value, you may find performance benefits from passing the variable by reference instead /li li If you actually want to change the value, consider li Returning multiple values with tuple unpacking /li li A mutable data type /li li Returning optional “value” types /li li For example, how would we recreate this in Python?public static bool TryParse (string s, out int result); /li /ul /li li Tuple unpacking /li /ul pre code def tryparse(string, base=10): try: return True, int(string, base=base) except ValueError: return False, None /code /pre pre code success, result = tryparse("123") /code /pre li Optional types: /li /ul pre code def tryparse(string, base=10) - Optional[int]: try: return int(string, base=base) except ValueError: return None /code /pre pre code if (n := tryparse("123")) is not None: print(n) /code /pre li Best Practice: Return and Reassign /li /ul p strong Brian #6: /strong a href="https://onlywei.github.io/explain-git-with-d3/" strong Visualizing Git Concepts /strong /a /p li by a href="https://github.com/onlywei" onlywei /a Wei Wang /li li a href="https://git-scm.com/book/en/v2/Git-Basics-Recording-Changes-to-the-Repository" Git Basics /a is good, and important, but hard to get all these concepts to sink in well until you play with it. /li li a href="https://onlywei.github.io/explain-git-with-d3/" Visualizing Git Concepts with D3 /a solidifies the concepts /li li Practice using git commands without any code, just visualizing the changes to the repository (and sometimes the remote origin repository) while typing commands. li commit, branch, checkout, checkout -b /li li reset, revert /li li merge, rebase /li li tag /li li fetch, pull, push /li /ul /li li Incredibly powerful to be able to play around with these concepts without using any code or possibly mucking up your repo. /li /ul p Extras: /p p Brian: /p li a href="https://microbit.org/new-microbit/" micro:bit now has a speaker and a microphone /a - available in November /li /ul p Michael: /p li a href="https://twitter.com/mkennedy/status/1316837032916705280" strong Firefox containers /strong /a /li li a href="https://twitter.com/ChristosMatskas/status/1318551481021272064" strong Twitch! /strong /a /li /ul p Joke: /p p strong Q: /strong Where do developers drink? strong A: /strong The Foo bar /p p strong - /strong Knock Knock! strong - /strong An async function strong - /strong Who's there? /p October 31, 2020 08:00 AM UTCAlembic migration errors on SQLiteWe use SQLite3 as the database in SecureDrop. We useSQLAlchemy to talk the database and Alembic formigrations. Some of those migrations are written by hand.Most of my work time in the last month went to getting things ready for Ubuntu Focal 20.04. We currently use Ubuntu Xenial 16.04. During this, I noticed 17test failures related to the Alembic on Focal but works fine on Xenial. Afterdigging a bit more, these are due to the missing reference to temporary tableswe used during migrations. With some more digging, I found this entry on theSQLite website:Compatibility Note: The behavior of ALTER TABLE when renaming a table was enhanced in versions 3.25.0 (2018-09-15) and 3.26.0 (2018-12-01) in order to carry the rename operation forward into triggers and views that reference the renamed table. This is considered an improvement. Applications that depend on the older (and arguably buggy) behavior can use the PRAGMA legacy_alter_table=ON statement or the SQLITE_DBCONFIG_LEGACY_ALTER_TABLE configuration parameter on sqlite3_db_config() interface to make ALTER TABLE RENAME behave as it did prior to version 3.25.0.This is what causing the test failures as SQLite upgraded to 3.31.1 onFocal from 3.11.0 on Xenial.According to the docs, we can fix the error by adding the following in theenv.py.diff --git a/securedrop/alembic/env.py b/securedrop/alembic/env.pyindex c16d34a5a..d6bce65b5 100644--- a/securedrop/alembic/env.py+++ b/securedrop/alembic/env.py@@ -5,6 +5,8 @@ import sys from alembic import context from sqlalchemy import engine_from_config, pool+from sqlalchemy.engine import Engine+from sqlalchemy import event from logging.config import fileConfig from os import path@@ -16,6 +18,12 @@ fileConfig(config.config_file_name) sys.path.insert(0, path.realpath(path.join(path.dirname(__file__), '..'))) from db import db # noqa+@event.listens_for(Engine, connect )+def set_sqlite_pragma(dbapi_connection, connection_record):+ cursor = dbapi_connection.cursor()+ cursor.execute( PRAGMA legacy_alter_table=ON )+ cursor.close() try: # These imports are only needed for offline generation of automigrations. # Importing them in a prod-like environment breaks things.Later, John found an even simpler wayto do the same for only the migrations impacted. October 31, 2020 05:15 AM UTCPublic Apology to Jeremy HowardWe, the NumFOCUS Code of Conduct Enforcement Committee, issue a public apology to Jeremy Howard for our handling of the JupyterCon 2020 reports. We should have done better. We thank you for sharing your experience and we will use it to improve our policies going forward. We acknowledge that it was an extremely stressful experience, [ ]The post Public Apology to Jeremy Howard appeared first on NumFOCUS.October 30, 2020 06:51 PM UTCFazendo backup do banco de dados no DjangoApresentaçãoEm algum momento, durante o seu processo de desenvolvimento com Django, pode ser que surja a necessidade de criar e restaurar o banco de dados da aplicação. Pensando nisso, resolvi fazer um pequeno tutorial, básico, de como realizar essa operação.Nesse tutorial, usaremos o django-dbbackup, um pacote desenvolvido especificamente para isso.Configurando nosso ambientePrimeiro, partindo do início, vamos criar uma pasta para o nosso projeto e, nela, isolar o nosso ambiente de desenvolvimento usando uma virtualenv:mkdir projeto_db cd projeto_db #criando a pasta do nosso projetovirtualenv -p python3.8 env source env/bin/activate #criando e ativando a nossa virtualenvDepois disso e com o nosso ambiente já ativo, vamos realizar os seguintes procedimentos:pip install -U pip #com isso, atualizamos a verão do pip instaladoInstalando as dependênciasAgora, vamos instalar o Django e o pacote que usaremos para fazer nossos backups.pip install Django==3.1.2 #instalando o Djangopip install django-dbbackup #instalando o django-dbbackupCriando e configurando projetoDepois de instaladas nossas dependências, vamos criar o nosso projeto e configurar o nosso pacote nas configurações do Django.django-admin startproject django_db . #dentro da nossa pasta projeto_db, criamos um projeto Django com o nome de django_db.Depois de criado nosso projeto, vamos criar e popular o nosso banco de dados.python manage.py migrate #com isso, sincronizamos o estado do banco de dados com o conjunto atual de modelos e migrações.Criado nosso banco de dados, vamos criar um superusuário para podemos o painel admin do nosso projeto.python manage.py createsuperuserPerfeito. Já temos tudo que precisamos para executar nosso projeto. Para execução dele, é só fazermos:python manage.py runserverVocê terá uma imagem assim do seu projeto:Configurando o django-dbbackupDentro do seu projeto, vamos acessar o arquivo settings.py, como expresso abaixo:django_db/├── settings.pyDentro desse arquivos iremos, primeiro, adiconar o django-dbbackup às apps do projeto:INSTALLED_APPS = ( 'dbbackup', # adicionando django-dbbackupDepois de adicionado às apps, vamos dizer para o Django o que vamos salvar no backup e, depois, indicar a pasta para onde será encaminhado esse arquivo. Essa inserção deve ou pode ser feita no final do arquivo settings.py:DBBACKUP_STORAGE = 'django.core.files.storage.FileSystemStorage' #o que salvarDBBACKUP_STORAGE_OPTIONS = {'location': 'backups/'} # onde salvarPercebam que dissemos para o Django salvar o backup na pasta backups, mas essa pasta ainda não existe no nosso projeto. Por isso, precisamos criá-la [fora da pasta do projeto]:mkdir backupsCriando e restaurando nosso backupJá temos tudo pronto. Agora, vamos criar o nosso primeiro backup:python manage.py dbbackupDepois de exetudado, será criado um arquivo -- no nosso exemplo, esse arquivo terá uma extensão .dump --, salvo na pasta backups. Esse arquivo contem todo backup do nosso banco de dados.Para recuperarmos nosso banco, vamos supor que migramos nosso sistema de um servidor antigo para um novo e, por algum motivo, nossa base de dados foi corrompida, inviabilizando seu uso. Ou seja, estamos com o sistema/projeto sem banco de dados -- ou seja, exlua ou mova a a sua base dados .sqlite3 para que esse exemplo seja útil --, mas temos os backups. Com isso, vamos restaurar o banco:python manage.py dbrestoreProntinho, restauramos nosso banco de dados. O interessante do django-dbbackup, dentre outras coisas, é que ele gera os backups com datas e horários específicos, facilitando o processo de recuperação das informações mais recentes.Por hoje é isso, pessoal. Até a próxima. ;) October 30, 2020 01:40 PM UTCThe Real Python Podcast – Episode #33: Going Beyond the Basic Stuff With Python and Al SweigartYou probably have heard of the bestselling Python book, "Automate the Boring Stuff with Python." What are the next steps after starting to dabble in the Python basics? Maybe you've completed some tutorials, created a few scripts, and automated repetitive tasks in your life. This week on the show, we have author Al Sweigart to talk about his new book, "Beyond the Basic Stuff with Python: Best Practices for Writing Clean Code." [ Improve Your Python With

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