Update README.md

AayushKotwani3 · web-flow · commit 78d1005cc5cf · 2025-04-29T23:07:11.000+05:30
diff --git a/Indexing & Slicing/README.md b/Indexing & Slicing/README.md
@@ -26,84 +26,99 @@ NumPy is a foundational Python library used for efficient numerical operations.
 
 ---
 
-## 🧩 Indexing
+## 🧹 Indexing
 
 ### 1D Array  
 ```python
 arr[index]
-2D Array
-python
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-Edit
+```
+
+### 2D Array  
+```python
 arr[row, column]
-🔹 Example:
+```
 
-python
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+🔹 Example:  
+```python
 arr_1d[2]  # returns 3rd element  
 arr_2d[1, 2]  # returns element at 2nd row, 3rd column  
-✂️ Slicing
-Syntax
-python
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-arr[start:stop:step]
-start: starting index
+```
 
-stop: stopping index (excluded)
+---
 
-step: step size or skip
+## ✂️ Slicing
 
-🔹 Examples:
+### Syntax  
+```python
+arr[start:stop:step]
+```
 
-python
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+- `start`: starting index  
+- `stop`: stopping index (excluded)  
+- `step`: step size or skip  
+
+🔹 Examples:  
+```python
 arr[1:4]      # elements from index 1 to 3  
 arr[::2]      # every 2nd element  
 arr[::-1]     # reverse array  
-🧮 2D Array Slicing
+```
+
+---
+
+## �� 2D Array Slicing
+
 You can slice rows and columns simultaneously:
 
-python
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+```python
 arr[row_start:row_end, col_start:col_end]
-🔹 Example:
+```
 
-python
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+🔹 Example:  
+```python
 arr_2d[0:3, 1:3]  # selects specific rows and columns from a 2D array
-🎯 Fancy Indexing
-Fancy indexing lets you access multiple arbitrary indices at once.
+```
+
+---
 
-🔹 Example:
+## 🎯 Fancy Indexing
 
-python
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+Fancy indexing lets you access multiple arbitrary indices at once.
+
+🔹 Example:  
+```python
 arr[[0, 2, 4]]  # retrieves values at those indices
-🧪 Boolean Masking
-Use logical conditions to filter arrays.
+```
 
-🔹 Example:
+---
 
-python
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+## 🧪 Boolean Masking
+
+Use logical conditions to filter arrays.
+
+🔹 Example:  
+```python
 arr[arr > 25]  # returns all values greater than 25
-✅ Conclusion
-This guide covered the core techniques for working with subsets of NumPy arrays:
+```
 
-Indexing individual elements
+---
 
-Slicing ranges
+## ✅ Conclusion
 
-Fancy indexing for custom selection
+This guide covered the core techniques for working with subsets of NumPy arrays:
 
-Boolean masks for condition-based filtering
+- Indexing individual elements  
+- Slicing ranges  
+- Fancy indexing for custom selection  
+- Boolean masks for condition-based filtering  
 
 These skills are essential for data preprocessing and manipulation in any scientific or machine learning pipeline.
 
+---
+
+## 💡 Pro Tip
+
+Practice modifying arrays and chaining these techniques to prepare for real-world data workflows.
+
+---
+
