Updated Creation folder with more examples and a markdown fike

AayushKotwani3 · AayushKotwani3 · commit bdc27efa6386 · 2025-05-04T03:21:38.000+05:30
diff --git a/Creation/README.md b/Creation/README.md
@@ -0,0 +1,103 @@
+# NumPy Array Creation Guide
+
+This guide provides examples for creating NumPy arrays of various dimensions, initializing arrays with specific values, generating sequences, identity matrices, and using random number generation.
+
+---
+
+## 1. Creating Arrays
+
+### 1.1 One-Dimensional Array
+```python
+one_d_array = np.array([1, 2, 3, 4])
+```
+
+### 1.2 Two-Dimensional Array (Matrix)
+```python
+two_d_array = np.array([[1, 2, 3, 4],
+                        [5, 6, 7, 8]])
+```
+
+---
+
+## 2. Arrays with Constant Values
+
+### 2.1 Zeros
+```python
+zero_array_1d = np.zeros(3)           # [0. 0. 0.]
+zero_array_2d = np.zeros((3, 4))      # 3x4 matrix of zeros
+```
+
+### 2.2 Ones
+```python
+ones_array = np.ones((2, 3))          # 2x3 matrix of ones
+```
+
+### 2.3 Filled with a Specific Value
+```python
+filled_array = np.full((2, 3), 5)     # 2x3 matrix filled with 5
+```
+
+---
+
+## 3. Sequence Arrays
+
+### 3.1 Using `np.arange()`
+```python
+sequence_array = np.arange(1, 11, 2)   # [1 3 5 7 9]
+```
+
+### 3.2 Identity Matrix
+```python
+identity_matrix = np.eye(5)           # 5x5 Identity matrix
+```
+
+---
+
+## 4. Multidimensional Arrays
+
+### 4.1 Three-Dimensional Array
+```python
+# Shape: 4 blocks, each with 3 rows and 4 columns
+arr_3d = np.ones((4, 3, 4))
+```
+
+### 4.2 Four-Dimensional Array
+```python
+# Shape: 2 blocks × 3 sub-blocks × 4 rows × 5 elements
+arr_4d = np.ones((2, 3, 4, 5))
+```
+
+You can continue this pattern to create arrays with even higher dimensions (5D, 6D, etc.).
+
+---
+
+## 5. Arrays with Random Values
+
+### 5.1 Random Float Arrays
+```python
+np.random.rand(2, 3)              # Random floats in [0, 1)
+np.random.random((3, 2))          # Same as rand, alternative syntax
+```
+
+### 5.2 Random Integers
+```python
+np.random.randint(1, 10, size=7)      # 1D array of 7 random ints [1, 10)
+np.random.randint(1, 10, (3, 4))      # 2D array of random ints
+```
+
+### 5.3 Random Choice
+```python
+np.random.choice([10, 20, 30, 40], size=5)  # Picks 5 values randomly from list
+```
+
+### 5.4 Reproducibility with Seed
+```python
+np.random.seed(37)
+np.random.randint(1, 100, 5)  # Same output every time when seed is set
+```
+
+Setting a seed ensures your results are reproducible for testing or demonstration purposes.
+
+---
+
+✅ With these methods, you can efficiently create and initialize arrays in NumPy across different shapes, dimensions, and value types!
diff --git a/Creation/creation.py b/Creation/creation.py
@@ -1,44 +1,85 @@
 import numpy as np
 
-# ------------------------------------------
+# -----------------------------------------------------
 # 1. Creating a One-Dimensional Array
 one_d_array = np.array([1, 2, 3, 4])
 # print(one_d_array)
 
-# ------------------------------------------
+# -----------------------------------------------------
 # 2. Creating a Two-Dimensional Array (Matrix)
 two_d_array = np.array([[1, 2, 3, 4],
                         [5, 6, 7, 8]])
 # print(two_d_array)
 
-# ------------------------------------------
-# 3. Creating a One-Dimensional Array Filled with Zeros
-zero_array_1d = np.zeros(3)   # 3 elements, all zeros
+# -----------------------------------------------------
+# 3. One-Dimensional Array Filled with Zeros
+zero_array_1d = np.zeros(3)  # Creates a 1D array of 3 elements, all set to 0
 # print(zero_array_1d)
 
-# ------------------------------------------
-# 4. Creating a Two-Dimensional Array Filled with Zeros
-zero_array_2d = np.zeros((3, 4))   # Shape = 3 rows × 4 columns
+# -----------------------------------------------------
+# 4. Two-Dimensional Array Filled with Zeros
+zero_array_2d = np.zeros((3, 4))  # 3 rows × 4 columns of zeros
 # print(zero_array_2d)
 
-# ------------------------------------------
-# 5. Creating a Two-Dimensional Array Filled with Ones
-ones_array = np.ones((2, 3))    # Shape = 2 rows × 3 columns
+# -----------------------------------------------------
+# 5. Two-Dimensional Array Filled with Ones
+ones_array = np.ones((2, 3))  # 2 rows × 3 columns filled with 1s
 # print(ones_array)
 
-# ------------------------------------------
-# 6. Creating a Two-Dimensional Array Filled with a Specific Value
-filled_array = np.full((2, 3), 5)   # Shape = 2x3, filled with 5
+# -----------------------------------------------------
+# 6. Two-Dimensional Array Filled with a Specific Value
+filled_array = np.full((2, 3), 5)  # 2×3 matrix filled with 5s
 # print(filled_array)
 
-# ------------------------------------------
-# 7. Creating a Sequence of Numbers
-# Using arange(start, stop, step)
-sequence_array = np.arange(1, 11, 2)   # From 1 to 10 (exclusive 11), step by 2
+# -----------------------------------------------------
+# 7. Sequence of Numbers Using arange()
+sequence_array = np.arange(1, 11, 2)  # From 1 to 10 (excluding 11), step size 2
 # print(sequence_array)
 
-# ------------------------------------------
-# 8. Creating an Identity Matrix
-# Identity matrix = square matrix with 1s on the diagonal and 0s elsewhere
-identity_matrix = np.eye(5)   # 5x5 Identity matrix
+# -----------------------------------------------------
+# 8. Identity Matrix (square matrix with 1s on diagonal)
+identity_matrix = np.eye(5)  # 5x5 identity matrix
 print(identity_matrix)
+
+# -----------------------------------------------------
+# 9. Creating a 3D Array with np.ones()
+# Shape (4, 3, 4) = 4 blocks, each with 3 rows and 4 columns
+arr_3d = np.ones((4, 3, 4))
+# print(arr_3d)
+
+# -----------------------------------------------------
+# 10. Creating a 4D Array with np.ones()
+# Shape (2, 3, 4, 5):
+# 2 blocks → each has 3 sub-blocks → each has 4 rows → each row has 5 elements
+arr_4d = np.ones((2, 3, 4, 5))
+# print(arr_4d)
+
+# -----------------------------------------------------
+# 11. Creating Arrays with Random Module
+
+'''
+np.random.rand(shape): Random floats in range [0.0, 1.0)
+np.random.random(shape): Similar to rand(), returns random floats in [0.0, 1.0)
+np.random.randint(low, high, size): Random integers from low (inclusive) to high (exclusive)
+np.random.choice(list, size): Random samples picked from the provided list
+np.random.seed(value): Sets the seed for reproducibility (same output each run)
+'''
+
+# Random floats between 0 and 1 with shape (2, 3)
+print(np.random.rand(2, 3))
+
+# Random floats between 0 and 1 with shape (3, 2)
+print(np.random.random((3, 2)))
+
+# Random integers from 1 to 9 (7 values in 1D array)
+print(np.random.randint(1, 10, size=7))
+
+# Random integers from 1 to 9 in a 3×4 array
+print(np.random.randint(1, 10, (3, 4)))
+
+# Picking 5 random values from the list [10, 20, 30, 40]
+print(np.random.choice([10, 20, 30, 40], size=5))
+
+# Setting seed ensures consistent random values every run (important for reproducibility)
+np.random.seed(37)
+print(np.random.randint(1, 100, 5))
