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+    "\n",
+    "## 🐍 Top 10 Python Tricks Every Data Scientist Should Know\n",
+    "\n",
+    "*Boost your productivity and write cleaner, faster, and more efficient code in data science!*\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 📌 **1. List Comprehensions**\n",
+    "\n",
+    "**Why:** Faster and cleaner than traditional loops.\n",
+    "\n",
+    "```python\n",
+    "squares = [x**2 for x in range(10)]\n",
+    "# Equivalent to:\n",
+    "# squares = []\n",
+    "# for x in range(10):\n",
+    "#     squares.append(x**2)\n",
+    "```\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 📌 **2. Enumerate for Index + Value**\n",
+    "\n",
+    "**Why:** Clean way to loop with index and item.\n",
+    "\n",
+    "```python\n",
+    "for idx, val in enumerate(['a', 'b', 'c']):\n",
+    "    print(f\"Index: {idx}, Value: {val}\")\n",
+    "```\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 📌 **3. Unpacking in Loops**\n",
+    "\n",
+    "**Why:** Makes code cleaner when working with tuples/lists.\n",
+    "\n",
+    "```python\n",
+    "pairs = [(1, 'a'), (2, 'b'), (3, 'c')]\n",
+    "for num, letter in pairs:\n",
+    "    print(num, letter)\n",
+    "```\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 📌 **4. Using `zip()` to Combine Lists**\n",
+    "\n",
+    "**Why:** Pair elements from multiple lists.\n",
+    "\n",
+    "```python\n",
+    "names = ['Alice', 'Bob', 'Charlie']\n",
+    "scores = [85, 90, 95]\n",
+    "for name, score in zip(names, scores):\n",
+    "    print(f\"{name} scored {score}\")\n",
+    "```\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 📌 **5. Dictionary Comprehensions**\n",
+    "\n",
+    "**Why:** Construct dictionaries concisely.\n",
+    "\n",
+    "```python\n",
+    "squares = {x: x*x for x in range(5)}\n",
+    "# Output: {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}\n",
+    "```\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 📌 **6. Lambda Functions**\n",
+    "\n",
+    "**Why:** Quick, inline anonymous functions.\n",
+    "\n",
+    "```python\n",
+    "double = lambda x: x * 2\n",
+    "print(double(5))  # Output: 10\n",
+    "```\n",
+    "\n",
+    "Use in pandas:\n",
+    "\n",
+    "```python\n",
+    "df['log_salary'] = df['salary'].apply(lambda x: np.log(x))\n",
+    "```\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 📌 **7. Using `any()` and `all()`**\n",
+    "\n",
+    "**Why:** Clean and fast checks across iterable values.\n",
+    "\n",
+    "```python\n",
+    "nums = [1, 2, 3, 0]\n",
+    "print(all(nums))  # False (because of 0)\n",
+    "print(any(nums))  # True (non-zero elements exist)\n",
+    "```\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 📌 **8. Swapping Variables Without Temp**\n",
+    "\n",
+    "**Why:** Clean, pythonic way to swap.\n",
+    "\n",
+    "```python\n",
+    "a, b = 10, 20\n",
+    "a, b = b, a\n",
+    "```\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 📌 **9. F-Strings for String Formatting (Python 3.6+)**\n",
+    "\n",
+    "**Why:** Cleaner and faster string formatting.\n",
+    "\n",
+    "```python\n",
+    "name = \"Abhishek\"\n",
+    "score = 95\n",
+    "print(f\"{name} scored {score} in the test.\")\n",
+    "```\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 📌 **10. Using `Counter` from `collections`**\n",
+    "\n",
+    "**Why:** Count frequencies with ease.\n",
+    "\n",
+    "```python\n",
+    "from collections import Counter\n",
+    "\n",
+    "words = ['data', 'science', 'data', 'AI']\n",
+    "word_counts = Counter(words)\n",
+    "print(word_counts)\n",
+    "# Output: Counter({'data': 2, 'science': 1, 'AI': 1})\n",
+    "```\n",
+    "\n",
+    "---\n"
+   ]
+  }
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