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diff --git a/Pandas/Pandas_Apply_custom_styles_on_column.ipynb b/Pandas/Pandas_Apply_custom_styles_on_column.ipynb
index bff3b46435..1a3016877f 100644
--- a/Pandas/Pandas_Apply_custom_styles_on_column.ipynb
+++ b/Pandas/Pandas_Apply_custom_styles_on_column.ipynb
@@ -364,4 +364,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/Pandas/Pandas_Performing_mathematical_operations_on_dataframe.ipynb b/Pandas/Pandas_Performing_mathematical_operations_on_dataframe.ipynb
new file mode 100644
index 0000000000..1a747bcc85
--- /dev/null
+++ b/Pandas/Pandas_Performing_mathematical_operations_on_dataframe.ipynb
@@ -0,0 +1,398 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bda4f92a-5665-47de-af7c-a849f55131fa",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "<img width=\"8%\" alt=\"Pandas.png\" src=\"https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/.github/assets/logos/Pandas.png\" style=\"border-radius: 15%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ed72b90-de08-425a-8a76-f4175a522417",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "# Pandas - Performing mathematical operations on dataframe\n",
+    "<a href=\"https://app.naas.ai/user-redirect/naas/downloader?url=https://raw.githubusercontent.com/jupyter-naas/awesome-notebooks/master/Pandas/Pandas_Apply_custom_styles_on_column.ipynb\" target=\"_parent\"><img src=\"https://naasai-public.s3.eu-west-3.amazonaws.com/Open_in_Naas_Lab.svg\"/></a><br><br><a href=\"https://bit.ly/3JyWIk6\">Give Feedback</a> | <a href=\"https://github.com/jupyter-naas/awesome-notebooks/issues/new?assignees=&labels=bug&template=bug_report.md&title=Pandas+-+Apply+custom+styles+on+column:+Error+short+description\">Bug report</a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3513100b-5299-47f9-be21-a336f3972de1",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "**Tags:** #pandas #dataframe #style #column #apply #custom"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32fb4036-3916-4d3b-b0bf-859172a35938",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "**Author:** [Siddharth Goyal](https://www.linkedin.com/in/siddharth-goyal-8b1a4814b/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec10fa8a-dabc-4f5f-b4f5-72fd29771f97",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "**Last update:** 2023-11-27 (Created: 2023-11-22)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f751cdd-5e8a-447c-89e4-3a1bca2047ed",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "**Description:** This notebook will help the users to perform mathematical operation like, sum, median, mode, mean, standard deviation, count on the data present in a column of dataframe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cb0cf94-ae27-4aae-bece-e93d1a874a14",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "**References:**\n",
+    "- [Pandas Documentation - Sum](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sum.html)\n",
+    "- [Pandas Documentation - Mean](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.mean.html)\n",
+    "- [Pandas Documentation - Mode](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.mode.html)\n",
+    "- [Pandas Documentation - Median](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.median.html)\n",
+    "- [Pandas Documentation - Standard Deviation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.std.html)\n",
+    "- [Pandas Documentation - Count](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.count.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3076e4fe-2b09-44c5-adac-00a6d1f5a747",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "## Input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63b43b3a-7bc2-4529-b9f9-4698aadc35c6",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "### Import libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63fda2d7-d1d9-4fd3-a752-13d9214d9acc",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22d01f0c-e5f7-4b18-a72a-89d014c1850f",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8704a59c-d01d-4eba-94a7-2a16a37837df",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "### Create DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87d45a29-4fc3-4c06-af32-3536d91f6c55",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Sample DataFrame\n",
+    "data = {\n",
+    "    'Name': ['Alice', 'Bob', 'Charlie', 'Alice', 'Eva'],\n",
+    "    'Score': [95, -80, 70, -80, 85],\n",
+    "    'Age': [25, 32, 18, None, 28],\n",
+    "    'Sales': [1200, 980, 1500, 850, 1750]\n",
+    "}\n",
+    "\n",
+    "df = pd.DataFrame(data)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f55ae9b-848a-41dd-9481-81393017fb2b",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "### Sum\n",
+    "The `sum` function will help to get the sum of any column in the dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e05ee25-4089-44f4-9c8d-4ad1f151e397",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#We will do this the mathematical operations on column Age\n",
+    "\n",
+    "ageSum = df['Age'].sum()\n",
+    "ageSum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e137ae0-e639-4ec0-9ec0-0a1be9d75aac",
+   "metadata": {},
+   "source": [
+    "### Mean\n",
+    "The `mean` function will help to get the mean of any column in the dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bd08345-431b-4338-a764-6833eb06b51b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#We will do this the mathematical operations on column Age\n",
+    "\n",
+    "ageMean = df['Age'].mean()\n",
+    "ageMean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0799092-b280-4ec8-a261-ab08572a7984",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "### Mode\n",
+    "The `mode` function will help to get the unique values of a row in the dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6cfa95f-8f85-40a3-83cd-18edeb21692b",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#We will do this the mathematical operation on Age column\n",
+    "\n",
+    "NameMode = df['Name'].mode()\n",
+    "NameMode"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ed200d1-4ca5-437f-b5d6-4d226a229f26",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "### Median\n",
+    "The `median` function will help to get the median of any column in the dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27c05a6a-c1e5-4b4f-a575-9b4e3e6ace39",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#We will do this the mathematical operations on column Age\n",
+    "\n",
+    "ageMedian = df['Age'].median()\n",
+    "ageMedian"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2e5fbbf-c19e-4b50-aa43-ae0562fc90c3",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "source": [
+    "### Standard Deviation\n",
+    "The `std` function will help to get the Standard Deviation of any column in the dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffd0378b-42a0-4f59-8375-d6427ebc9094",
+   "metadata": {
+    "papermill": {},
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#We will do this the mathematical operations on column Age\n",
+    "\n",
+    "ageStandDev = df['Age'].std()\n",
+    "ageStandDev"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61067205-063e-4020-972a-81d40c65c918",
+   "metadata": {},
+   "source": [
+    "### Count\n",
+    "The `count` function will help to get the total number of non Null rows of any column in the dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46da4d29-4cf9-4a9e-94cb-1a3d3c60489d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#We will do this the mathematical operations on column Age\n",
+    "\n",
+    "notNullCount = df['Age'].count()\n",
+    "notNullCount"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df976c2f-9abb-41e8-8776-a0748d59bb9a",
+   "metadata": {},
+   "source": [
+    "## Output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69942bc8-9505-4a0f-86ef-fe552f8dd02d",
+   "metadata": {},
+   "source": [
+    "### Display result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afd28121-c410-4518-922d-fc866b55282b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "print(\"Sum of Age Column is: \", ageSum)\n",
+    "print(\"Mean of Age Column is: \", ageMean)\n",
+    "print(\"Mode of Name Column is:\\n\", NameMode)\n",
+    "print(\"Median of Age Column is: \",ageMedian)\n",
+    "print(\"Standard Deviation of Age Column is: \", ageStandDev)\n",
+    "print(\"Count of non-null rows in Name column: \", notNullCount)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  },
+  "naas": {
+   "notebook_id": "5d27e3bd7fdfda696205d0e879b9a00e31ece1a64cae3c864ab22b006a0ab495",
+   "notebook_path": "Pandas/Pandas_Apply_custom_styles_on_column.ipynb"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "environment_variables": {},
+   "parameters": {},
+   "version": "2.4.0"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}