Choosing the right Python packages for your project can feel like navigating a maze with countless paths. Whether you’re building machine learning models, analyzing data, or developing web applications, selecting the optimal combination of packages is crucial for success. Yet many developers find themselves asking: “Which packages should I install for my specific needs?”

This guide will help you answer that question by exploring effective strategies for package selection and introducing Anaconda’s quick start environments, designed to eliminate the guesswork and get you coding faster.

The Package Selection Dilemma

Starting a new Python project often begins with a blank slate. You know your objective—whether it’s analyzing financial data, building a natural language processing model, or creating a web application—but the path to get there isn’t always clear.

Common challenges include:

  • Identifying which packages are essential for your specific use case
  • Guaranteeing security of the packages being used
  • Ensuring compatibility between different packages and versions
  • Keeping up with rapidly evolving tools and libraries
  • Balancing comprehensive functionality with environment bloat
  • Spending excessive time on environment setup rather than actual development

 

These challenges are particularly frustrating when you’re halfway through writing code and realize a package is missing, forcing you to pause your workflow, install the missing component, and potentially rerun your entire notebook.

Enter Quick Start Environments

What if you could skip the package selection process entirely and jump straight into coding? That’s the idea behind Anaconda’s quick start environments.

Quick start environments are preconfigured Python environments, offered via Anaconda Toolbox and enabled within your preferred IDE (e.g., JupyterLab, VSCode), containing carefully curated package combinations for specific use cases, industries, and technologies. They eliminate the friction of environment setup, allowing you to focus on solving problems rather than managing dependencies.

How Quick Start Environments Work

When you choose a quick start environment, you’re getting a pre-built, tested, and optimized collection of packages designed for a specific purpose. For example:

  • AI/ML Environment: Core packages for building and training ML/AI models
  • NLP Environment: Natural language processing with transformers, spaCy, NLTK, and text analysis tools
  • Finance Environment: Essential packages for financial analysis, modeling, and visualization of market data

 

Each environment comes with packages most relevant for its use case, significantly reducing setup time and potential conflicts. This approach is particularly valuable for practitioners who may not know which packages are essential for specific analyses or who lose time recreating environments for different projects.

Get started by installing the Anaconda Toolbox (v4.20+) from Anaconda Navigator.

The Quick Start Environments Collection

Choose from a collection of 14 quick start environments spanning various industries and use cases:

Available for free:

  • Python Starter
  • Anaconda Distribution
  • AI/ML
  • Finance

 

Available for business customers:

  • Life Sciences
  • Manufacturing
  • Banking
  • Insurance
  • Snowflake
  • PyTorch
  • Web Development
  • Natural Language Processing
  • DevOps
  • ETL (Extract, Transform, Load)

How to Find and Install Additional Packages

Inevitably, there will be a time when the package you need isn’t included in a quick start environment. In the scenario where you already know the name of the package (e.g., the datetime module used to work with dates), you can install it using the following command: 

conda install datetime

However, it is also highly likely that you will need to use a package that you’ve never heard of before. Perhaps you’ve built a linear regression model of sales, and now need to build a forecast for next week’s transactions. How do you find the right Python package you need to do this, you might ask.

Get Help from the Anaconda AI Assistant

There’s no need to leave where you are coding in a Jupyter Notebook; you can open the AI Assistant by clicking on the green sparkles (just make sure to install the Anaconda Toolbox first). Simply explain what you are trying to do, and the AI Assistant will suggest the packages and even give you a code snippet example to get you started right away! 

In this example it suggests using the Prophet python package and explains the reasons for suggesting this package with the context of the code you have written in your Notebook. 

!conda install prophet

from prophet import Prophet

# Initialize and fit the Prophet model

model = Prophet()

model.fit(df)

# Create a dataframe for future predictions (e.g., next 30 days)

future = model.make_future_dataframe(periods=30)

# Make predictions

forecast = model.predict(future)

With this workflow, you can iterate to an environment that contains all the packages you need to complete a specific task.

Channels, Package Versions, and Machine Compatibility

Unfortunately, it’s not always as simple as “find the package, install the package.” Python has a complex dependency structure where every package has a strict set of requirements in order to be installed. These requirements can be related to the hardware or the operating system (OS) of your computer, the other packages you have installed, or the conda channels that you are using. 

Fortunately, Anaconda.org has all the answers to these questions; simply search for the package you are trying to install. It will tell you which channels the package is available on (likely Anaconda and conda-forge), the latest version, and the OS platforms that it’s available on.

Tip: “noarch” means “no architecture”; it indicates that the package works identically across any operating system so you don’t have to worry about it functioning in other locations if you switch machines. 

Conclusion: Focus on Building, Not Setting Up

The time you spend configuring environments is time not spent solving problems. With quick start environments, you can shift your focus from package management to actual development—turning what was once hours of setup into minutes of productive coding.

With the Anaconda AI Assistant and Anaconda.org, you have all the tools you need to discover, assess, and install all the latest and greatest Python packages from anywhere in the ecosystem, all while maintaining rigid security protocols by using Anaconda’s secure distribution. 

If you’re ready to learn more about environment management, continue reading our blog series where we take you from start (beginner) to finish (expert). 

Ready to stop worrying about which packages to install? Install Anaconda Toolbox and explore quick start environments in your next Jupyter session—your first environment is just one click away.