From 02c7f2c3a202fc7234c5000dd010b1f3df6b9910 Mon Sep 17 00:00:00 2001
From: Ankit Mahato <ankmahato@gmail.com>
Date: Fri, 31 May 2024 07:19:32 +0530
Subject: [PATCH] Update reshape-array.md

---
 contrib/numpy/reshape-array.md | 53 ++++++++++++++++++----------------
 1 file changed, 28 insertions(+), 25 deletions(-)

diff --git a/contrib/numpy/reshape-array.md b/contrib/numpy/reshape-array.md
index d3d7f59..91da366 100644
--- a/contrib/numpy/reshape-array.md
+++ b/contrib/numpy/reshape-array.md
@@ -1,54 +1,57 @@
 # Numpy Array Shape and Reshape
+
 In NumPy, the primary data structure is the ndarray (N-dimensional array). An array can have one or more dimensions, and it organizes your data efficiently.
 
-Code to create a 2D array
+Let us create a 2D array
+
 ``` python
 import numpy as np
 
 numbers = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
 print(numbers)
-
-# Output:
-# array([[1, 2, 3, 4],[5, 6, 7, 8]])
 ```
 
-## Changing Array Shape using Reshape()
+#### Output:
+
+``` python
+array([[1, 2, 3, 4],[5, 6, 7, 8]])
+```
+
+## Changing Array Shape using `reshape()`
+
 The `reshape()` function allows you to rearrange the data within a NumPy array.
-It take 2 arguements, row and columns. The `reshape()` can add or remove the dimensions. For instance, array can convert a 1D array into a 2D array or vice versa.
+
+It take 2 arguments, row and columns. The `reshape()` can add or remove the dimensions. For instance, array can convert a 1D array into a 2D array or vice versa.
 
 ``` python
 arr_1d = np.array([1, 2, 3, 4, 5, 6]) # 1D array
-arr_2d = arr_1d.reshape(2, 3) # Reshaping with 2rows and 3cols
+arr_2d = arr_1d.reshape(2, 3) # Reshaping with 2 rows and 3 cols
 
 print(arr_2d)
-
-# Output:
-# array([[1, 2, 3],[4, 5, 6]])
-
 ```
 
-## Changing Array Shape using Resize()
+#### Output:
+
+``` python
+array([[1, 2, 3],[4, 5, 6]])
+```
+
+## Changing Array Shape using `resize()`
+
 The `resize()` function allows you to modify the shape of a NumPy array directly.
+
 It take 2 arguements, row and columns.
 
 ``` python
 import numpy as np
 arr_1d = np.array([1, 2, 3, 4, 5, 6])
 
-arr_1d.resize((2, 3)) # 2rows and 3cols
+arr_1d.resize((2, 3)) # 2 rows and 3 cols
 print(arr_1d)
-
-# Output:
-# array([[1, 2, 3],[4, 5, 6]])
-
 ```
 
-## Reshape() VS Resize()
+#### Output:
 
-| Reshape                                                | Resize |
-| -----------                                            | ----------- |
-| Does not modify the original array     | Modifies the original array in-place     |
-| Creates a new array | Changes the shape of the array |
-| Returns a reshaped array   | Doesn't return anything        |
-| Compatibility between dimensions | Does not compatibility between dimensions |
-| Syntax: reshape(row,col) | Syntax: resize((row,col)) |
+``` python
+array([[1, 2, 3],[4, 5, 6]])
+```