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diff --git a/dev/sitemap.xml.gz b/dev/sitemap.xml.gz
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diff --git a/en/1-Experiments/Notebooks/DTale_EN.html b/en/1-Experiments/Notebooks/DTale_EN.html
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "76784523-1230-4c92-8cac-753cc0f8613e",
+   "metadata": {},
+   "source": [
+    "# D-Tale: A Seamless Data Exploration Tool for Python\n",
+    "\n",
+    "D-Tale, born out of a SAS to Python conversion, transforms the data exploration process into a breeze. Originally a Perl script wrapper for SAS's insight function, it has evolved into a lightweight web client seamlessly integrated with Pandas data structures.\n",
+    "\n",
+    "Built on a Flask back-end and a React front-end, D-Tale offers a straightforward method to view and analyze Pandas data structures. Its seamless integration with Jupyter notebooks and Python/IPython terminals makes it a versatile tool. Currently, it supports various Pandas objects, including DataFrame, Series, MultiIndex, DatetimeIndex, and RangeIndex.\n",
+    "\n",
+    "D-Tale is a solution that simplifies data exploration. Acting as a lightweight web client over Pandas data structures, D-Tale offers an intuitive user interface for performing various data exploration tasks without the need to write any code.\n",
+    "\n",
+    "\n",
+    "![](dtale.png)\n",
+    "\n",
+    "![](dtale-menu.png)\n",
+    "\n",
+    "![](dtale-2.png)\n",
+    "\n",
+    "## Installation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1a92ee3-40b7-4a8c-b51b-4e007efe6593",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "! pip install -r requirements.txt\n",
+    "! pip install -U dtale"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b7fffba-5e2e-4381-973b-6da1a9dc1bb2",
+   "metadata": {},
+   "source": [
+    "## Usage:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31bec97e-6100-43ba-b41e-68fb3f837116",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import dtale\n",
+    "import pandas as pd\n",
+    "from ydata_profiling.utils.cache import cache_file\n",
+    "\n",
+    "# Fetching Pokemon dataset\n",
+    "file_name = cache_file(\n",
+    "    \"pokemon.csv\",\n",
+    "    \"https://raw.githubusercontent.com/bryanpaget/html/main/pokemon.csv\"\n",
+    ")\n",
+    "\n",
+    "# Reading dataset using Pandas\n",
+    "pokemon_df = pd.read_csv(file_name)\n",
+    "\n",
+    "# Displaying dataset with D-Tale\n",
+    "dtale.show(pokemon_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "863aff9f-8be9-4231-ade4-21377856b1a0",
+   "metadata": {},
+   "source": [
+    "D-Tale comes to the rescue by providing a user-friendly interface for essential data exploration tasks, eliminating the need for repetitive code and saving valuable time in the process.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}