Got it 👍 — let’s go step by step and make dill clear and practical.

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#🔎 What is dill?

• dill is a Python library that extends the standard pickle module.

• pickle can serialize (“save”) Python objects into a file and later deserialize (“load”) them back.

• But pickle has limitations: it cannot handle things like:

  • user-defined functions
  • lambdas
  • classes defined inside functions
  • some iterators, generators, closures

👉 dill overcomes many of these limitations. It can serialize almost anything in Python—including entire interactive sessions.

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#🚀 How to Install

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#🧪 Basic Usage

⚡ Notice: Even the function greet is restored — this wouldn’t work with pickle.

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#🧩 What Can dill Save?

✅ Numbers, strings, lists, dicts (like pickle) ✅ Functions (including lambdas and closures) ✅ Classes, methods, and instances ✅ Generators, iterators ✅ Entire interactive sessions

❌ What it may still struggle with:

• Open file handles, sockets, database connections (not serializable in principle)
• OS-level resources (threads, processes)

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#🧰 Core APIs

1. Save one object

   python

   dill.dump(obj, open("file.pkl", "wb"))

2. Load one object

   python

   obj = dill.load(open("file.pkl", "rb"))

3. Save entire session (all variables)

   python

   dill.dump_session("session.pkl")

4. Load entire session

   python

   dill.load_session("session.pkl")

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#🆚 pickle vs dill

So:

• If you just need simple data (numbers, arrays, dicts), pickle is fine.
• If you want to checkpoint your whole working environment (like Jupyter does), dill is the tool.

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#⚡ Practical Tip

You can make a quick utility file session_utils.py:

Then in any script:

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👉 Would you like me to show you how to use dill specifically for a step-by-step experiment workflow (like Jupyter Notebook, where you checkpoint after each cell), or just for a one-shot "save everything at once" style?