Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Using Community Code
2 students

Using Community Code

Concepts of community code
Created byStone River eLearning
Last updated 4/2024
English
English

What you'll learn

  • Understand the significance of community code in Python programming and its role in enhancing problem-solving efficiency.
  • Learn about PYPI as a common source for Python libraries and explore popular libraries addressing common programming challenges.
  • Master the installation and management of external dependencies using tools like pip, pipenv, and conda.
  • Gain proficiency in installing pip, managing system-level Python packages, and creating virtual environments to isolate project dependencies.
  • Explore advanced topics such as managing virtual environments centrally or locally, installing packages into virtual environments, and using tools like pipenv
  • Apply practical demonstrations to utilize virtual environments for image manipulation, web scraping, and data analysis tasks.
  • Learn to navigate between multiple project environments and leverage conda for environment cloning and data analysis with libraries like pandas.
  • Acquire a comprehensive understanding of utilizing community code, managing dependencies, and streamlining Python development workflows.
  • Develop essential skills and techniques for success in Python programming, whether solving complex problems or exploring new areas of interest.

Explore related topics

Course content

1 section • 23 lectures • 8h 17m total length

  • Overview4:15

    Explore how to leverage community code in Python using pip, PyPi, virtual environments, pipenv, and conda across Mac, Linux, and Windows to solve problems without reinventing the wheel.

  • Community Code: What is that30:54

    Explore how the Python community's 'community code' packages deliver ready-made tools for date handling, requests, and data analysis, and learn to install and manage them via PyPI and pip.

  • Install pip20:14

    Install and upgrade pip and verify its version. Use pip and pip3 to manage Python packages, while exploring PyPI and PyPA guidance.

  • Install System Packages21:14

    Learn how to install system level Python packages and assess risks of system level pollution. Explore ptpython and bppython as REPL tools and consider virtual environments.

  • Virtual Environments: Create25:54

    Learn to create and use Python virtual environments with venv, activate and deactivate, isolating sandboxes that copy system Python and manage packages via pip without affecting the host.

  • Virtual Environment: Manage20:15

    Learn to manage python virtual environments for projects, including local to the project and centralized setups, using venv, activate, gitignore, and generating reproducible dependencies with pip3 freeze to requirements.txt.

  • Virtual Environment: Install Packages21:59

    Learn to manage virtual environments and install packages with pip. Create project-specific environments for tools like pandas, export a requirements.txt, and track changes with gitignore.

  • Project: Resizing Images31:22

    Learn to resize images in Python using Pillow, creating thumbnails that preserve aspect ratio, and manage the workflow with virtual environments, Git, and requirements.txt.

  • Install pipenv18:37

    Install pipenv to manage dependencies with Pipfile and Pipfile.lock, create deterministic virtual environments, replace manual requirements.txt workflows, and support Windows, Mac, and Linux.

  • Create pipenv Environments12:56

    Learn to create and manage pipenv environments for Python projects, activate with pipenv shell, and install packages using Pipfile and Pipfile.lock.

  • Manage pipenv Environments24:36

    Learn to manage pipenv environments by creating, removing, and recreating environments, installing dependencies from Pipfile and Pipfile.lock, and using pipenv shell for project workflows.

  • Use pipenv Environments21:13

    Explore managing multiple Python projects with pipenv environments, converting requirements.txt to Pipfile, locking dependencies, and navigating shells and directory-specific installs.

  • Project: Image Links on a Page28:32

    Learn to scrape image links from a web page using requests and Beautiful Soup, assemble full image URLs, and download or save them locally in a pipenv virtual environment.

  • Another one? Why conda?21:23

    Explore how Conda, the Python package manager from the Anaconda distribution, simplifies creating virtual environments, managing packages, and running Python versions for data science workflows.

  • Install conda24:01

    Install miniconda to use conda, manage environments, and set PATH; learn installer steps, path updates, and essential conda commands.

  • Install conda: Windows12:28

    Install Miniconda on Windows using the 64-bit installer and understand path and environment variable options, then verify with the Anaconda prompt and conda commands.

  • Create conda Environments27:32

    Create and manage Conda environments with Miniconda, naming environments, activating and deactivating them, and installing packages while understanding dependencies and environment directories.

  • Manage conda Environments26:08

    Learn to manage conda environments by creating, copying, renaming, and removing envs, switching Python versions (2.7 and 3.x), and using activate and deactivate commands to test across projects.

  • Manage conda Environments: Reproduce30:11

    Learn to reproduce conda environments by exporting to environment.yml, handling python versions, and sharing environment records with others for reliable, version-controlled project setups.

  • Manage conda Environments: Clone23:54

    Clone a base conda environment to create read-netcdfs and read-gribs, install netCDF4 and pygrib from conda-forge, and export the environment for version control.

  • Project: Analyze Population Data27:54

    Analyze population data using community code by loading a csv from GitHub, setting up a conda environment, and exploring with pandas, matplotlib, and Jupyter to plot trends by country.

  • Uninstall conda12:22

    Uninstall Conda and clean up your environment by removing miniconda, deleting .conda, updating your path in the bash profile, and rebooting to restore the original Python setup.

  • Uninstall conda: Windows9:26

    Uninstall conda on Windows by using Control Panel to remove Miniconda, then clean user and system PATH variables, verify no residual Anaconda entries, and reset Python to the system default.

Requirements

  • Basic understanding of Python programming concepts and syntax.
  • Familiarity with installing and managing Python packages using tools like pip is helpful but not required.
  • Some experience with virtual environments and external dependencies is beneficial but not mandatory.

Description

This course provides an in-depth exploration of utilizing community code in Python programming to enhance problem-solving efficiency. Hosted by Justin and Vonne, the episodes cover essential tools and methodologies for leveraging community-written code to tackle various programming challenges effectively.

The course begins by introducing the concept of community code and its significance in problem-solving. Viewers learn about PYPI, a common source for Python libraries, and explore popular Python libraries addressing common problems.

Episodes then delve into the installation and management of external dependencies using tools like pip, pipenv, and conda. Viewers are guided through installing pip, managing system-level Python packages, and creating virtual environments to isolate project dependencies.

Furthermore, the course explores advanced topics such as managing virtual environments centrally or locally, installing packages into virtual environments, and using tools like pipenv and conda for efficient environment management.

Practical demonstrations include utilizing virtual environments for image manipulation, web scraping, and data analysis tasks. Additionally, viewers learn to navigate between multiple project environments and leverage conda for environment cloning and data analysis with libraries like pandas.

By the end of the course, participants will have acquired a comprehensive understanding of utilizing community code, managing dependencies, and leveraging tools like pip, pipenv, and conda to streamline Python development workflows. Whether aiming to solve complex problems or explore new areas of interest, this course equips viewers with essential skills and techniques for success in Python programming.

Who this course is for:

  • Beginner to intermediate Python programmers who want to enhance their problem-solving skills and efficiency.
  • Programmers interested in learning how to leverage community-written code to tackle various programming challenges effectively.
  • Individuals aiming to streamline their Python development workflows and improve their coding practices.

Report abuse