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Update array-iteration.md

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@ -7,7 +7,7 @@ Understanding these methods is crucial for performing operations on array elemen
- Iterating using basic `for` loop. - Iterating using basic `for` loop.
**Single-dimensional array iteration**: ### Single-dimensional array
Iterating over a single-dimensional array is straightforward using a basic `for` loop Iterating over a single-dimensional array is straightforward using a basic `for` loop
@ -18,11 +18,18 @@ arr = np.array([1, 2, 3, 4, 5])
for i in arr: for i in arr:
print(i) print(i)
``` ```
**Output** :
#### Output
```python ```python
[ 1 2 3 4 5 ] 1
2
3
4
5
``` ```
**Multi-dimensional array**:
### Multi-dimensional array
Iterating over multi-dimensional arrays, each iteration returns a sub-array along the first axis. Iterating over multi-dimensional arrays, each iteration returns a sub-array along the first axis.
@ -32,14 +39,16 @@ marr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
for arr in marr: for arr in marr:
print(arr) print(arr)
``` ```
**Output** :
#### Output
```python ```python
[1 2 3] [1 2 3]
[4 5 6] [4 5 6]
[7 8 9] [7 8 9]
``` ```
## 2. Iterating with nditer ## 2. Iterating with `nditer`
- `nditer` is a powerful iterator provided by NumPy for iterating over multi-dimensional arrays. - `nditer` is a powerful iterator provided by NumPy for iterating over multi-dimensional arrays.
- In each interation it gives each element. - In each interation it gives each element.
@ -51,7 +60,9 @@ arr = np.array([[1, 2, 3], [4, 5, 6]])
for i in np.nditer(arr): for i in np.nditer(arr):
print(i) print(i)
``` ```
**Output** :
#### Output
```python ```python
1 1
2 2
@ -61,7 +72,7 @@ for i in np.nditer(arr):
6 6
``` ```
## 3. Iterating with ndenumerate ## 3. Iterating with `ndenumerate`
- `ndenumerate` allows you to iterate with both the index and the value of each element. - `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 - It gives index and value as output in each iteration
@ -74,7 +85,7 @@ for index,value in np.ndenumerate(arr):
print(index,value) print(index,value)
``` ```
**Output** : #### Output
```python ```python
(0, 0) 1 (0, 0) 1
@ -86,7 +97,6 @@ for index,value in np.ndenumerate(arr):
## 4. Iterating with flat ## 4. Iterating with flat
- The `flat` attribute returns a 1-D iterator over the array. - The `flat` attribute returns a 1-D iterator over the array.
-
```python ```python
import numpy as np import numpy as np
@ -96,7 +106,7 @@ for element in arr.flat:
print(element) print(element)
``` ```
**Output** : #### Output
```python ```python
1 1
@ -105,5 +115,6 @@ for element in arr.flat:
4 4
``` ```
Understanding the various ways to iterate over NumPy arrays can significantly enhance your data processing efficiency. 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. Whether you are working with single-dimensional or multi-dimensional arrays, NumPy provides versatile tools to iterate and manipulate array elements effectively.