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Co-authored-by: Ankit Mahato <ankmahato@gmail.com>
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- [Installing NumPy](installing-numpy.md)
- [Introduction](introduction.md)
- [NumPy Data Types](datatypes.md)
- [Operations on Arrays in NumPy](operations-on-arrays.md)

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# Operations on Arrays
## NumPy Arithmetic Operations
NumPy offers a broad array of operations for arrays, including arithmetic functions.
The arithmetic operations in NumPy are popular for their simplicity and efficiency in handling array calculations.
**Addition**
we can use the `+` operator to perform element-wise addition between two or more NumPy arrays.
**Code**
```python
import numpy as np
array_1 = np.array([9, 10, 11, 12])
array_2 = np.array([1, 3, 5, 7])
result_1 = array_1 + array_2
print("Utilizing the + operator:", result_1)
```
**Output:**
```
Utilizing the + operator: [10 13 16 19]
```
**Subtraction**
we can use the `-` operator to perform element-wise subtraction between two or more NumPy arrays.
**Code**
```python
import numpy as np
array_1 = np.array([9, 10, 11, 12])
array_2 = np.array([1, 3, 5, 7])
result_1 = array_1 - array_2
print("Utilizing the - operator:", result_1)
```
**Output:**
```
Utilizing the - operator: [8 7 6 5]
```
**Multiplication**
we can use the `*` operator to perform element-wise multiplication between two or more NumPy arrays.
**Code**
```python
import numpy as np
array_1 = np.array([9, 10, 11, 12])
array_2 = np.array([1, 3, 5, 7])
result_1 = array_1 * array_2
print("Utilizing the * operator:", result_1)
```
**Output:**
```
Utilizing the * operator: [9 30 55 84]
```
**Division**
we can use the `/` operator to perform element-wise division between two or more NumPy arrays.
**Code**
```python
import numpy as np
array_1 = np.array([9, 10, 11, 12])
array_2 = np.array([1, 3, 5, 7])
result_1 = array_1 / array_2
print("Utilizing the / operator:", result_1)
```
**Output:**
```
Utilizing the / operator: [9. 3.33333333 2.2 1.71428571]
```
**Exponentiation**
we can use the `**` operator to perform element-wise exponentiation between two or more NumPy arrays.
**Code**
```python
import numpy as np
array_1 = np.array([9, 10, 11, 12])
array_2 = np.array([1, 3, 5, 7])
result_1 = array_1 ** array_2
print("Utilizing the ** operator:", result_1)
```
**Output:**
```
Utilizing the ** operator: [9 1000 161051 35831808]
```
**Modulus**
We can use the `%` operator to perform element-wise modulus operations between two or more NumPy arrays.
**Code**
```python
import numpy as np
array_1 = np.array([9, 10, 11, 12])
array_2 = np.array([1, 3, 5, 7])
result_1 = array_1 % array_2
print("Utilizing the % operator:", result_1)
```
**Output:**
```
Utilizing the % operator: [0 1 1 5]
```
<br>
## NumPy Comparision Operations
<br>
NumPy provides various comparison operators that can compare elements across multiple NumPy arrays.
**less than operator**
The `<` operator returns `True` if the value of operand on left is less than the value of operand on right.
**Code**
```python
import numpy as np
array_1 = np.array([12,15,20])
array_2 = np.array([20,15,12])
result_1 = array_1 < array_2
print("array_1 < array_2:",result_1)
```
**Output:**
```
array_1 < array_2 : [True False False]
```
**less than or equal to operator**
The `<=` operator returns `True` if the value of operand on left is lesser than or equal to the value of operand on right.
**Code**
```python
import numpy as np
array_1 = np.array([12,15,20])
array_2 = np.array([20,15,12])
result_1 = array_1 <= array_2
print("array_1 <= array_2:",result_1)
```
**Output:**
```
array_1 <= array_2: [True True False]
```
**greater than operator**
The `>` operator returns `True` if the value of operand on left is greater than the value of operand on right.
**Code**
```python
import numpy as np
array_1 = np.array([12,15,20])
array_2 = np.array([20,15,12])
result_2 = array_1 > array_2
print("array_1 > array_2:",result_2)
```
**Output:**
```
array_1 > array_2 : [False False True]
```
**greater than or equal to operator**
The `>=` operator returns `True` if the value of operand on left is greater than or equal to the value of operand on right.
**Code**
```python
import numpy as np
array_1 = np.array([12,15,20])
array_2 = np.array([20,15,12])
result_2 = array_1 >= array_2
print("array_1 >= array_2:",result_2)
```
**Output:**
```
array_1 >= array_2: [False True True]
```
**equal to operator**
The `==` operator returns `True` if the value of operand on left is same as the value of operand on right.
**Code**
```python
import numpy as np
array_1 = np.array([12,15,20])
array_2 = np.array([20,15,12])
result_3 = array_1 == array_2
print("array_1 == array_2:",result_3)
```
**Output:**
```
array_1 == array_2: [False True False]
```
**not equal to operator**
The `!=` operator returns `True` if the value of operand on left is not equal to the value of operand on right.
**Code**
```python
import numpy as np
array_1 = np.array([12,15,20])
array_2 = np.array([20,15,12])
result_3 = array_1 != array_2
print("array_1 != array_2:",result_3)
```
**Output:**
```
array_1 != array_2: [True False True]
```
<br>
## NumPy Logical Operations
Logical operators perform Boolean algebra. A branch of algebra that deals with `True` and `False` statements.
It illustrates the logical operations of AND, OR, and NOT using np.logical_and(), np.logical_or(), and np.logical_not() functions, respectively.
**Logical AND**
Evaluates the element-wise truth value of `array_1` AND `array_2`
**Code**
```python
import numpy as np
array_1 = np.array([True, False, True])
array_2 = np.array([False, False, True])
print(np.logical_and(array_1, array_2))
```
**Output:**
```
[False False True]
```
**Logical OR**
Evaluates the element-wise truth value of `array_1` OR `array_2`
**Code**
```python
import numpy as np
array_1 = np.array([True, False, True])
array_2 = np.array([False, False, True])
print(np.logical_or(array_1, array_2))
```
**Output:**
```
[True False True]
```
**Logical NOT**
Evaluates the element-wise truth value of `array_1` NOT `array_2`
**Code**
```python
import numpy as np
array_1 = np.array([True, False, True])
array_2 = np.array([False, False, True])
print(np.logical_not(array_1))
```
**Output:**
```
[False True False]
```