A Python project with coding framework and unit testing

Explore Python basics within main method, covering indentation, variables, data type inference, lists, loops, functions, string handling, and file writing with open, write mode, append mode, and overwrite.

Learn how to create and manipulate Python dictionaries with key-value pairs, including adding, updating, deleting, and using get to handle missing keys.

Explore Python collections by comparing lists and tuples, printing elements and indices, and noting that lists are mutable with append and pop, while tuples remain immutable.

learn to structure a python project with modules for ingesting and persisting data, use classes and methods, and call one file from another to build an end-to-end file processor.

initialize class level variables with constructors in Python, pass file type through the driver, and process CSV files before persisting data to Postgres.

Learn how to use Python logging to replace print statements, control log levels at runtime with basicConfig, and apply debug, info, warning, and error across classes.

Configure log labels via a configuration file, define a root logger and per-file loggers with a console handler and a sample formatter, and control log levels through the configuration.

Learn to configure and use multiple loggers for modules like ingest and persist, set module-specific log levels via config, and prevent duplicate output by disabling propagation.

Learn Python error handling by using a try block in the store data function, handling exceptions with an accept block and logger.error to log division by zero.

Read properties from a config file with Python's ConfigParser, using an ini file's database configs section to fetch the PG table value at runtime.

install PostgreSQL on windows, set up PgAdmin, create a Futurex schema with a Futurex course catalog table, and insert records such as Hadoop Spark with json course sections.

Learn to read from and write to Postgres with Python using psycopg2, establishing connections, executing select and insert queries, fetching results, and configuring table names via a property file.

Organize your code by moving ingest and persist into a processor subdirectory, update imports, and adjust logging and config paths to run the driver successfully.

Handle the unique key constraint error by using a select max query and fetchone to determine the next course id, then insert the record into the course catalog table.

Learn how to read data from a JSON file in Python, use json.load with open, print the content, and prepare its insertion into a quotes catalog table in the database.

Read data from a JSON file in Python and insert it into a Postgres table by extracting values from the course JSON and using json.dumps for the course section.

Build a rest web service with python and learn what a web service is, how rest enables data exchange via json or xml over http.

Expose read and write access to your database through restful endpoints over http without exposing credentials, and learn to use get and post methods for secure Python APIs.

Build a rest api with flask to fetch all course information via a get /courses endpoint, read from pg through a db object, and return the courses to clients.

Create a post endpoint to store course information in the catalog, using a persist data class to insert JSON into the database and verify with Postman.

Unit test a Python application using the unit test package by creating test_persist.py, a test class with methods and asserts, and running tests in PyCharm or from the command line.

Write unit tests for the read from pg function by importing persist, creating a purchase data object, and asserting the tuple length or undefined table errors.

Learn to unit test a Python application with pytest, including configuring PyCharm, writing simple pytest tests with assert, converting from unittest, and running all tests successfully.

Explore next steps after finishing a Python project with a coding framework and unit testing, and access resources for exclusive coupons, YouTube educational content, and blogs at futurex.skills.com.

Master data pipelines with Hadoop and Spark using Python and Scala in free cloud environments, and validate Spark apps with unit testing while preparing data from raw sources to HDFS.

Explore data engineering with Spark and the Databricks lakehouse through a hands-on, beginner-friendly pipeline. Learn how lakehouse differs from traditional data warehouses using Python, Scala, and SQL in Databricks.

deploy machine learning and deep learning models to production, package for serving with MLflow, build serverless REST APIs, and explore NLP sentiment analysis using Python, scikit-learn, PyTorch, and TensorFlow.