Update numpy_basic_to_advance.ipynb

Author: bansalkanav

Module 2 - Python for Data Analysis/02. High Speed Computations with NumPy/numpy_basic_to_advance.ipynb

@@ -1989,6 +1989,24 @@
     "# print(arr[1:2, :])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise: Write a program to filter the values from the array based on below mentioned conditions:  \n",
+    "Either value should be divisible by 5.  \n",
+    "(or) value should be an odd number and factor of 7.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Code"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3855,24 +3873,6 @@
     "# Code"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise: Write a program to filter the values from the array based on below mentioned conditions:  \n",
-    "Either value should be divisible by 5.  \n",
-    "(or) value should be an odd number and factor of 7.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Code"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -4598,7 +4598,11 @@
    "source": [
     "### **argsort(), argmin() and argmax()**\n",
     "\n",
-    "In NumPy, functions like argsort(), argmin(), and argmax() are used to get the indices of elements rather than their values. These functions are extremely useful when you need to locate specific elements in an array."
+    "In NumPy, functions like argsort(), argmin(), and argmax() are used to get the indices of elements rather than their values. These functions are extremely useful when you need to locate specific elements in an array.\n",
+    "\n",
+    "- np.argsort() - Returns Indices of Sorted Elements\n",
+    "- np.argmin() - Returns Index of Minimum Element\n",
+    "- np.argmax() - Returns Index of Maximum Element"
    ]
   },
   {