srakaincorporated.blogg.se - Postgres rename column

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We then created an _init_() method that will run the first time we create a new result and, finally, a _repr_() method to represent the object when we query for it. a list of words that we counted minus stop words (more on this later).We then set the attributes that we want to store for a result. Next we created a Result() class and assigned it a table name of results. JSON columns are fairly new to Postgres and are not available in every database supported by SQLAlchemy so we need to import it specifically. We first import the database connection that we created in our app.py file as well as JSON from SQLAlchemy’s PostgreSQL dialects. Here we created a table to store the results of the word counts. result_no_stop_words = result_no_stop_words def _repr_ ( self ): return ''. Column ( JSON ) def _init_ ( self, url, result_all, result_no_stop_words ): self. Column ( JSON ) result_no_stop_words = db. Model ): _tablename_ = 'results' id = db. Once you have Postgres installed and running, create a database called wordcount_dev to use as our local development database:įrom app import db from import JSON class Result ( db.

#Postgres rename column for mac os x

If you don’t have Postgres installed, Postgres.app is an easy way to get up and running for Mac OS X users. Since Heroku uses Postgres, it will be good for us to develop locally on the same database.

#Postgres rename column install

To get started, install Postgres on your local computer, if you don’t have it already. Flask-Migrate ( 2.5.2) - extension that supports SQLAlchemy database migrations via Alembic.Flask-SQLAlchemy ( 2.4.1) - Flask extension that provides SQLAlchemy support.Psycopg2 ( 2.8.4) - a Python adapter for Postgres.Part Eight: Create a custom Angular Directive to display a frequency distribution chart using JavaScript and D3.Part Seven: Update the front-end to make it more user-friendly.

#Postgres rename column how to

Part Six: Push to the staging server on Heroku - setting up Redis and detailing how to run two processes (web and worker) on a single Dyno.

Part Five: Set up Angular on the front-end to continuously poll the back-end to see if the request is done processing.

Part Four: Implement a Redis task queue to handle the text processing.

Part Three: Add in the back-end logic to scrape and then process the word counts from a webpage using the requests, BeautifulSoup, and Natural Language Toolkit (NLTK) libraries.

Part Two: Set up a PostgreSQL database along with SQLAlchemy and Alembic to handle migrations.

Part One: Set up a local development environment and then deploy both a staging and a production environment on Heroku.

Remember: Here’s what we’re building - A Flask app that calculates word-frequency pairs based on the text from a given URL.

: Upgraded to Python version 3.5.1 as well as the latest versions of Psycopg2, Flask-SQLAlchemy, and Flask-Migrate.

Explicitly install and use Flask-Script due to change of Flask-Migrate internal interface. : Upgraded to Python version 3.8.1 as well as the latest versions of Psycopg2, Flask-SQLAlchemy, and Flask-Migrate.Free Bonus: Click here to get access to a free Flask + Python video tutorial that shows you how to build Flask web app, step-by-step.