animator · Yodha-Sudarsi · May 11, 2024 · May 11, 2024 · May 13, 2024 · May 13, 2024
diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md
@@ -1,3 +1,4 @@
 # List of sections
 
-- [Section title](filename.md)
+- [Installing NumPy](installing_numpy.md)
+- [Operations on Arrays in NumPy](operations_on_arrays.md)
diff --git a/contrib/numpy/installing_numpy.md b/contrib/numpy/installing_numpy.md
@@ -0,0 +1,82 @@
+# Installing NumPy
+
+NumPy is the fundamental package for scientific computing in Python.
+NumPy is used for working with arrays. 
+
+The only prerequisite for installing NumPy is Python itself.
+#
+**Step 1: Check if PIP is Installed**
+
+Before installing NumPy, it's essential to ensure that PIP (Python Package Installer) is installed on your system. PIP is a package management system used to install and manage Python packages. You can verify if PIP is installed by running a simple command in your terminal or command prompt. 
+
+```bash
+pip --version
+```
+
+If PIP is not currently installed on your system, you can install it by visiting the [pypi.org](https://pypi.org/project/pip/) webpage. 
+
+#
+
+**Step 2: Installing PIP**
+
+**get-pip.py**
+
+This is a Python script that uses some bootstrapping logic to install pip.
+
+Open a terminal / command prompt and run:
+
+**Linux**
+```bash
+python get-pip.py
+```
+
+**Windows**
+```bash
+py get-pip.py
+```
+
+**MacOS**
+```bash
+python get-pip.py
+```
+
+#
+
+**Step 3: Installing NumPy**
+
+NumPy can be installed either through conda or pip.
+
+If you use pip, you can install NumPy with:
+
+```bash
+pip install numpy
+```
+
+If you use conda, you can install NumPy from the defaults or conda-forge channels:
+
+```
+# Best practice, use an environment rather than install in the base env
+conda create -n my-env
+conda activate my-env
+```
+
+```
+# If you want to install from conda-forge
+conda config --env --add channels conda-forge
+```
+
+```
+# The actual install command
+conda install numpy
+```
+
+You can find more information about how to install [NumPy](https://numpy.org/install/) on numpy.org.
+
+#
+
+**Step 4: Check if NumPy is Installed**
+
+We can utilize the "pip show" command not only to display the version but also to determine whether NumPy is installed on the system.
+```bash
+pip show numpy
+```
diff --git a/contrib/numpy/operations_on_arrays.md b/contrib/numpy/operations_on_arrays.md
@@ -0,0 +1,246 @@
+# Operations on Arrays
+
+## NumPy Arithmetic Operations
+
+<br>
+
+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.
+
+Here is a list of different arithmetic operations along with their corresponding operators:
+
+| Element-wise Operation | Operator |
+|------------------------|:--------:|
+| Addition               | +        |
+| Subtraction            | -        |
+| Multiplication         | *        |
+| Division               | /        |
+| Exponentiation         | **       |
+| Modulus                | %        |
+
+#
+
+### Code Initialization
+
+```python
+import numpy as np
+
+array_1 = np.array([9, 10, 11, 12])
+array_2 = np.array([1, 3, 5, 7])
+```
+
+### Addition
+```python
+# Utilizing the + operator
+result_1 = array_1 + array_2
+print("Utilizing the + operator:", result_1) 
+```
+
+Output:
+```
+Utilizing the + operator: [10 13 16 19]
+```
+
+### Subtraction
+```python
+# Utilizing the - operator
+result_1 = array_1 - array_2
+print("Utilizing the - operator:", result_1) 
+```
+
+Output:
+```
+Utilizing the - operator: [8 7 6 5]
+```
+
+### Multiplication
+```python
+# Utilizing the * operator
+result_1 = array_1 * array_2
+print("Utilizing the * operator:", result_1) 
+```
+
+Output:
+```
+Utilizing the * operator: [9 30 55 84]
+```
+
+### Division
+```python
+# Utilizing the / operator
+result_1 = array_1 / array_2
+print("Utilizing the / operator:", result_1) 
+```
+
+Output:
+```
+Utilizing the / operator: [9. 3.33333333 2.2 1.71428571]
+```
+
+### Exponentiation
+```python
+# Utilizing the ** operator
+result_1 = array_1 ** array_2
+print("Utilizing the ** operator:", result_1) 
+```
+
+Output:
+```
+Utilizing the ** operator: [9 1000 161051 35831808]
+```
+
+### Modulus
+```python
+# Utilizing the % operator
+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.
+
+| Operator | Description                                                                                                                   |
+|:--------:|-------------------------------------------------------------------------------------------------------------------------------|
+| <        | Evaluates to True if the element in the first array is less than the corresponding element in the second array                |
+| <=       | Evaluates to True if the element in the first array is less than or equal to the corresponding element in the second array    |
+| >        | Evaluates to True if the element in the first array is greater than the corresponding element in the second array             |
+| >=       | Evaluates to True if the element in the first array is greater than or equal to the corresponding element in the second array |
+| ==       | Evaluates to True if the element in the first array is equal to the corresponding element in the second array                 |
+| !=       | Evaluates to True if the element in the first array is not equal to the corresponding element in the second array             |
+
+#
+
+### Code Initialization
+
+```python
+import numpy as np
+
+array_1 = np.array([12,15,20])
+array_2 = np.array([20,15,12])
+```
+
+#### less than operator
+```python
+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
+```python
+result_1 = array_1 <= array_2
+print("array_1 <= array_2:",result_1)
+```
+Output:
+```
+array_1 <= array_2: [True True False]
+``` 
+
+#### greater than operator
+```python
+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
+```python
+result_2 = array_1 >= array_2
+print("array_1 >= array_2:",result_2)   
+```
+Output:
+```
+array_1 >= array_2: [False True True]
+```
+
+#### equal to operator
+```python
+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  
+```python
+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
+
+<br>
+
+Logical operators perform Boolean algebra. A branch of algebra that deals with True and False statements.
+
+| Operator      | Description                                         |
+|---------------|-----------------------------------------------------|
+| logical_and   | Evaluates the element-wise truth value of x1 AND x2 |
+| logical_or    | Evaluates the element-wise truth value of x1 OR x2  |
+| logical_not   | Evaluates the element-wise truth value of NOT x     |
+
+
+It illustrates the logical operations of AND, OR, and NOT using np.logical_and(), np.logical_or(), and np.logical_not() functions, respectively.
+
+#
+
+### Code Initialization
+
+```python
+import numpy as np
+
+array_1 = np.array([True, False, True])
+array_2 = np.array([False, False, True])
+```
+
+#### Logical AND
+```python
+print(np.logical_and(array_1, array_2))
+```
+Output: 
+```
+[False False True]
+```
+
+#### Logical OR
+```python
+print(np.logical_or(array_1, array_2))
+```
+Output:
+```
+[True False True]
+```
+
+#### Logical NOT
+```python
+print(np.logical_not(array_1)) 
+```
+Output:
+```
+[False True False]
+```