diff --git a/.gitignore b/.gitignore
index ee525a1..e3bfca8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
 # password files:
+experiments/
 mysql_password.txt
 
 # Byte-compiled / optimized / DLL files
diff --git a/experiments/experiments.ipynb b/experiments/experiments.ipynb
index 66d706e..6d92b8c 100644
--- a/experiments/experiments.ipynb
+++ b/experiments/experiments.ipynb
@@ -344,6 +344,471 @@
     "df5"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# New version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import pandas as pd\n",
+    "# connect to csv:\n",
+    "# Connect to csv:\n",
+    "class CSV:\n",
+    "\n",
+    "    # load csv file:\n",
+    "    def load_csv(self, filepath: Path, delimiter: str) -> pd.DataFrame:\n",
+    "        filepath = Path(filepath)\n",
+    "        return pd.read_csv(filepath, delimiter=delimiter)\n",
+    "    \n",
+    "    # convert dataframe to csv file:\n",
+    "    def to_csv(self, data:pd.DataFrame, filepath: Path):\n",
+    "        filepath = Path(filepath)\n",
+    "        data.to_csv(filepath, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Animals</th>\n",
+       "      <th>Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Tiger</td>\n",
+       "      <td>Wild</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Lion</td>\n",
+       "      <td>Wild</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Cow</td>\n",
+       "      <td>Domestic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dog</td>\n",
+       "      <td>Domestic</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Animals      Type\n",
+       "0   Tiger      Wild\n",
+       "1    Lion      Wild\n",
+       "2     Cow  Domestic\n",
+       "3     Dog  Domestic"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "obj =CSV()\n",
+    "data = obj.load_csv(\"sample.csv\", \",\")\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obj.to_csv(data, \"sample2.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Connect to Excel Sheet:\n",
+    "class Excel:\n",
+    "    # load excel sheet:\n",
+    "    def load_excelsheet(self, filepath:Path, sheet_name:str) -> pd.DataFrame:\n",
+    "        filepath = Path(filepath)\n",
+    "        return pd.read_excel(filepath, sheet_name=sheet_name)\n",
+    "    \n",
+    "    # convert dataframe to excel sheet:\n",
+    "    def to_excel(self, data:pd.DataFrame, filepath:Path, sheet_name:str):\n",
+    "        filepath = Path(filepath)\n",
+    "        data.to_excel(filepath, sheet_name, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Animals</th>\n",
+       "      <th>Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Tiger</td>\n",
+       "      <td>Wild</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Lion</td>\n",
+       "      <td>Wild</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Cow</td>\n",
+       "      <td>Domestic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dog</td>\n",
+       "      <td>Domestic</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Animals      Type\n",
+       "0   Tiger      Wild\n",
+       "1    Lion      Wild\n",
+       "2     Cow  Domestic\n",
+       "3     Dog  Domestic"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "obj = Excel()\n",
+    "data = obj.load_excelsheet(\"sample.xlsx\", \"sample_sheet\")\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ADMIN\\AppData\\Local\\Temp\\ipykernel_41452\\3802458429.py:11: FutureWarning: Starting with pandas version 3.0 all arguments of to_excel except for the argument 'excel_writer' will be keyword-only.\n",
+      "  data.to_excel(filepath, sheet_name, index=False)\n"
+     ]
+    }
+   ],
+   "source": [
+    "obj.to_excel(data, \"yuv.xlsx\", \"yuv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Connect to Google Sheet:\n",
+    "class GSheet:\n",
+    "    # load google sheet\n",
+    "    def load_gsheet(self, gsheet_id:str, sheet_name:str) -> pd.DataFrame:\n",
+    "        base_url = 'https://docs.google.com/spreadsheets/d'\n",
+    "        sheet_csv = 'gviz/tq?tqx=out:csv&sheet='\n",
+    "        url = f'{base_url}/{gsheet_id}/{sheet_csv}{sheet_name}'\n",
+    "        return pd.read_csv(url)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Dept</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Yuvanesh</td>\n",
+       "      <td>CSBS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Asgar</td>\n",
+       "      <td>CSBS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Aakash</td>\n",
+       "      <td>CSBS</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Name  Dept\n",
+       "0  Yuvanesh  CSBS\n",
+       "1     Asgar  CSBS\n",
+       "2    Aakash  CSBS"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "obj = GSheet()\n",
+    "x = obj.load_gsheet(\"1Brw0GNaeiI46RQejNOGnkjDdNHw62ppU8vFRYm1iJUM\", \"Sheet1\")\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List, Dict\n",
+    "from pymongo import MongoClient\n",
+    "\n",
+    "# Connect to MOngoDB:\n",
+    "class MongoDB:\n",
+    "    # initialize the mongo client:\n",
+    "    def __init__(self, host_url:str):\n",
+    "        self.mongoclinet = MongoClient(host_url)\n",
+    "\n",
+    "    # load data from mongodb:\n",
+    "    def load_data(self, database:str, collection_name:str) -> pd.DataFrame:\n",
+    "        database_connect = self.mongoclinet[database]\n",
+    "        collection = database_connect[collection_name]\n",
+    "        records = collection.find()\n",
+    "        data = list(records)\n",
+    "        df = pd.DataFrame(data)\n",
+    "        df.drop(\"_id\", axis=1, inplace=True)\n",
+    "        return df\n",
+    "    \n",
+    "    # upload data to mongodb:\n",
+    "    def upload_data(self, database:str, collection_name:str):\n",
+    "        database_connect = self.mongoclinet[database]\n",
+    "        collection = database_connect[collection_name]\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>City</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>25</td>\n",
+       "      <td>New York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>30</td>\n",
+       "      <td>Los Angeles</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Charlie</td>\n",
+       "      <td>35</td>\n",
+       "      <td>Chicago</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>25</td>\n",
+       "      <td>New York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>30</td>\n",
+       "      <td>Los Angeles</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Charlie</td>\n",
+       "      <td>35</td>\n",
+       "      <td>Chicago</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Name  Age         City\n",
+       "0    Alice   25     New York\n",
+       "1      Bob   30  Los Angeles\n",
+       "2  Charlie   35      Chicago\n",
+       "3    Alice   25     New York\n",
+       "4      Bob   30  Los Angeles\n",
+       "5  Charlie   35      Chicago"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = MongoDB(\"mongodb://localhost:27017\").load_data(\"dmy001\", \"people\")\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'Name': 'Alice', 'Age': 25, 'City': 'New York'},\n",
+       " {'Name': 'Bob', 'Age': 30, 'City': 'Los Angeles'},\n",
+       " {'Name': 'Charlie', 'Age': 35, 'City': 'Chicago'},\n",
+       " {'Name': 'Alice', 'Age': 25, 'City': 'New York'},\n",
+       " {'Name': 'Bob', 'Age': 30, 'City': 'Los Angeles'},\n",
+       " {'Name': 'Charlie', 'Age': 35, 'City': 'Chicago'}]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dic = data.to_dict(orient=\"records\")\n",
+    "dic"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/src/dbsconnector/databases.py b/src/dbsconnector/databases.py
index dbf3232..f798800 100644
--- a/src/dbsconnector/databases.py
+++ b/src/dbsconnector/databases.py
@@ -1,33 +1,92 @@
 # importing necessary libraries
 import pandas as pd
-from pymongo.mongo_client import MongoClient
-from typing import List, Dict
+from pymongo import MongoClient
 from pathlib import Path
+import warnings
 
-# load csv file:
-def load_csv(filepath:Path, delimiter:str):
-    filepath = Path(filepath)
-    return pd.read_csv(str(filepath), delimiter=delimiter)
-    
-# load excel sheet
-def load_excelsheet(filepath:Path, sheet_name:str):
-    filepath = Path(filepath)
-    return pd.read_excel(str(filepath), sheet_name=sheet_name)
-
-# load google sheet 
-def load_gsheet(gsheet_id:str, sheet_name:str):
-    base_url = 'https://docs.google.com/spreadsheets/d'
-    sheet_csv = 'gviz/tq?tqx=out:csv&sheet='
-    url = f'{base_url}/{gsheet_id}/{sheet_csv}{sheet_name}'
-    return pd.read_csv(url)
-
-# load mongodb data:
-def load_mongodbdata(host:str, database:str, collection:str):
-    client: MongoClient = MongoClient(host)
-    db = client[database]
-    col = db[collection]
-    records = col.find()
-    data: List[Dict] = list(records)
-    df = pd.DataFrame(data)
-    df.drop('_id', axis=1, inplace=True)
-    return df
+warnings.filterwarnings("ignore")
+
+
+# Connect to csv:
+class CSV:
+    def __init__(self):
+        pass
+
+    # load csv file:
+    def load_csv(self, filepath: Path, delimiter: str) -> pd.DataFrame:
+        filepath = Path(filepath)
+        return pd.read_csv(filepath, delimiter=delimiter)
+
+    # convert dataframe to csv file:
+    def to_csv(self, data: pd.DataFrame, filepath: Path):
+        filepath = Path(filepath)
+        data.to_csv(filepath, index=False)
+
+
+# Connect to Excel Sheet:
+class Excel:
+    def __init__(self):
+        pass
+
+    # load excel sheet:
+    def load_excelsheet(self, filepath: Path, sheet_name: str) -> pd.DataFrame:
+        filepath = Path(filepath)
+        return pd.read_excel(filepath, sheet_name=sheet_name)
+
+    # convert dataframe to excel sheet:
+    def to_excel(self, data: pd.DataFrame, filepath: Path, sheet_name: str):
+        filepath = Path(filepath)
+        data.to_excel(filepath, sheet_name, index=False)
+
+
+# Connect to Google Sheet:
+class GSheet:
+    def __init__(self):
+        pass
+
+    # load google sheet
+    def load_gsheet(self, gsheet_id: str, sheet_name: str) -> pd.DataFrame:
+        base_url = "https://docs.google.com/spreadsheets/d"
+        sheet_csv = "gviz/tq?tqx=out:csv&sheet="
+        url = f"{base_url}/{gsheet_id}/{sheet_csv}{sheet_name}"
+        return pd.read_csv(url)
+
+
+# Connect to MOngoDB:
+class MongoDB:
+    # initialize the mongo client:
+    def __init__(self, host_url: str):
+        self.mongoclinet: MongoClient = MongoClient(host_url)
+
+    # load data from mongodb:
+    def load_data(self, database: str, collection_name: str) -> pd.DataFrame:
+        database_connect = self.mongoclinet[database]
+        collection = database_connect[collection_name]
+        records = collection.find()
+        data = list(records)
+        df = pd.DataFrame(data)
+        df.drop("_id", axis=1, inplace=True)
+        return df
+
+    # upload data to mongodb:
+    def upload_data(self, database: str, collection_name: str, data: pd.DataFrame):
+        database_connect = self.mongoclinet[database]
+        collection = database_connect[collection_name]
+        data_dict = data.to_dict(orient="records")
+        collection.insert_many(data_dict)
+
+    # upload object to mongodb:
+    def upload_object(
+        self, database: str, collection_name: str, object_name: str, object_
+    ):
+        database_connect = self.mongoclinet[database]
+        collection = database_connect[collection_name]
+        record = {"object_name": object_name, "object": object_}
+        collection.insert_one(record)
+
+    # load object from mongodb:
+    def load_object(self, database: str, collection_name: str, object_name: str):
+        database_connect = self.mongoclinet[database]
+        collection = database_connect[collection_name]
+        object_ = collection.find_one({"obajct_name": object_name})
+        return object_