pickle

pickle.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Understanding the Python `pickle` Module\n",
+    "\n",
+    "The `pickle` module in Python implements binary protocols for serializing and de-serializing a Python object structure. \n",
+    "\"\"Pickling\"\" is the process of converting a Python object hierarchy into a byte stream, and \"\"unpickling\"\" is the inverse operation, converting a byte stream back into a Python object hierarchy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Why use `pickle`?\n",
+    "\n",
+    "- **Persistence**: Save Python objects to a file so they can be loaded later.\n",
+    "- **Transfer**: Send Python objects over a network (though JSON is often preferred for cross-language compatibility)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Key Functions: `pickle.dump()` and `pickle.load()`\n",
+    "\n",
+    "### `pickle.dump(obj, file, protocol=None)`\n",
+    "Writes the pickled representation of `obj` to the open file object `file`.\n",
+    "\n",
+    "### `pickle.load(file)`\n",
+    "Reads the pickled representation of an object from the open file object `file` and returns the reconstructed object.\n",
+    "\n",
+    "Let's see some examples."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 1: Pickling a Dictionary and a List"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "data_dict = {\"name\": \"Alice\", \"age\": 30, \"city\": \"New York\"}\n",
+    "data_list = [1, 2, 3, {'a': 10, 'b': 20}, 'hello']\n",
+    "\n",
+    "# Pickling (serializing) the objects\n",
+    "with open('data.pickle', 'wb') as f:\n",
+    "    pickle.dump(data_dict, f)\n",
+    "    pickle.dump(data_list, f) # You can dump multiple objects to the same file\n",
+    "\n",
+    "print(\"Objects pickled successfully to data.pickle\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 2: Unpickling the Objects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "# Unpickling (de-serializing) the objects\n",
+    "with open('data.pickle', 'rb') as f:\n",
+    "    loaded_dict = pickle.load(f)\n",
+    "    loaded_list = pickle.load(f)\n",
+    "\n",
+    "print(f\"Loaded Dictionary: {loaded_dict}\")\n",
+    "print(f\"Loaded List: {loaded_list}\")\n",
+    "\n",
+    "# Verify types\n",
+    "print(f\"Type of loaded_dict: {type(loaded_dict)}\")\n",
+    "print(f\"Type of loaded_list: {type(loaded_list)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 3: Pickling a Custom Class Instance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "class MyClass:\n",
+    "    def __init__(self, name, value):\n",
+    "        self.name = name\n",
+    "        self.value = value\n",
+    "\n",
+    "    def greet(self):\n",
+    "        return f\"Hello, my name is {self.name} and my value is {self.value}\"\n",
+    "\n",
+    "obj = MyClass(\"TestObject\", 123)\n",
+    "print(f\"Original Object Greeting: {obj.greet()}\")\n",
+    "\n",
+    "# Pickle the custom object\n",
+    "with open('myclass.pickle', 'wb') as f:\n",
+    "    pickle.dump(obj, f)\n",
+    "\n",
+    "print(\"Custom object pickled successfully\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 4: Unpickling a Custom Class Instance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "# The class definition must be available in the environment where you unpickle\n",
+    "class MyClass:\n",
+    "    def __init__(self, name, value):\n",
+    "        self.name = name\n",
+    "        self.value = value\n",
+    "\n",
+    "    def greet(self):\n",
+    "        return f\"Hello, my name is {self.name} and my value is {self.value}\"\n",
+    "\n",
+    "# Unpickle the custom object\n",
+    "with open('myclass.pickle', 'rb') as f:\n",
+    "    loaded_obj = pickle.load(f)\n",
+    "\n",
+    "print(f\"Loaded Object Greeting: {loaded_obj.greet()}\")\n",
+    "print(f\"Type of loaded_obj: {type(loaded_obj)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Security Warning!\n",
+    "\n",
+    "**Never unpickle data received from an untrusted or unauthenticated source!**\n",
+    "\n",
+    "The `pickle` module is not secure against maliciously constructed data. Maliciously constructed pickle data can execute arbitrary code upon unpickling. This is due to the fact that `pickle` can reconstruct arbitrary Python objects, including instances of custom classes, and can call methods on these objects during the unpickling process.\n",
+    "\n",
+    "For cross-language compatibility or when security is a concern, consider alternatives like JSON, XML, or Protocol Buffers, which are typically safer for data exchange because they only serialize data, not arbitrary code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "The `pickle` module is a powerful tool for Python object serialization, especially useful for saving and loading Python-specific data structures. However, its use requires caution regarding security, particularly when dealing with data from untrusted sources."
+   ]
+  }
+ ],
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