Create numpy_array_iteration.md

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contrib/numpy/numpy_array_iteration.md

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+# NumPy Array Iteration
+
+Iterating over arrays in NumPy is a common task when processing data. NumPy provides several ways to iterate over elements of an array efficiently. 
+Understanding these methods is crucial for performing operations on array elements effectively.
+
+## 1. Basic Iteration
+
+- Iterating using basic `for` loop.
+  
+**Single-dimensional array iteration**:
+
+Iterating over a single-dimensional array is straightforward using a basic `for` loop
+
+```python
+import numpy as np
+
+arr = np.array([1, 2, 3, 4, 5])
+for i in arr:
+    print(i)
+```
+**Output** :
+```python
+[ 1 2 3 4 5 ]
+```
+**Multi-dimensional array**:
+
+Iterating over multi-dimensional arrays, each iteration returns a sub-array along the first axis.
+
+```python
+marr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+
+for arr in marr:
+    print(arr)
+```
+**Output** :
+```python
+[1 2 3]
+[4 5 6]
+[7 8 9]
+```
+
+## 2. Iterating with nditer
+
+- `nditer` is a powerful iterator provided by NumPy for iterating over multi-dimensional arrays.
+- In each interation it gives each element.
+
+```python
+import numpy as np
+
+arr = np.array([[1, 2, 3], [4, 5, 6]])
+for i in np.nditer(arr):
+    print(i)
+```
+**Output** :
+```python
+1
+2
+3
+4
+5
+6
+```
+
+## 3. Iterating with ndenumerate
+
+- `ndenumerate` allows you to iterate with both the index and the value of each element.
+- It gives index and value as output in each iteration
+
+```python
+import numpy as np
+
+arr = np.array([[1, 2], [3, 4]])
+for index,value in np.ndenumerate(arr):
+    print(index,value)
+```
+
+**Output** : 
+
+```python
+(0, 0) 1
+(0, 1) 2
+(1, 0) 3
+(1, 1) 4
+```
+
+## 4. Iterating with flat
+
+- The `flat` attribute returns a 1-D iterator over the array.
+- 
+
+```python
+import numpy as np
+
+arr = np.array([[1, 2], [3, 4]])
+for element in arr.flat:
+    print(element)
+```
+
+**Output** :
+
+```python
+1
+2
+3
+4
+```
+
+Understanding the various ways to iterate over NumPy arrays can significantly enhance your data processing efficiency. 
+Whether you are working with single-dimensional or multi-dimensional arrays, NumPy provides versatile tools to iterate and manipulate array elements effectively.