kopia lustrzana https://github.com/animator/learn-python
Create numpy_array_iteration.md
Added Introduction Added Methods Added examples Added Conclusionpull/674/head
rodzic
b31bfc6149
commit
5280fb09a8
|
@ -0,0 +1,109 @@
|
|||
# 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.
|
Ładowanie…
Reference in New Issue