diff --git a/Access_GitHub_Link.ipynb b/Access_GitHub_Link.ipynb
new file mode 100644
index 00000000..c6fa84ee
--- /dev/null
+++ b/Access_GitHub_Link.ipynb
@@ -0,0 +1,145 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/aka76bm/0xDeCA10B/blob/main/Access_GitHub_Link.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SKG_3zy_AhjA"
+      },
+      "source": [
+        "# Setup\n",
+        "\n",
+        "Please ensure you have imported a Gemini API key from AI Studio.\n",
+        "You can do this directly in the Secrets tab on the left.\n",
+        "\n",
+        "After doing so, please run the setup cell below."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "swM2wHGtAhjI"
+      },
+      "source": [
+        "!pip install -U -q \"google\"\n",
+        "!pip install -U -q \"google.genai\"\n",
+        "\n",
+        "import os\n",
+        "from google.colab import userdata\n",
+        "from google.colab import drive\n",
+        "os.environ[\"GEMINI_API_KEY\"] = userdata.get(\"GOOGLE_API_KEY\")\n",
+        "\n",
+        "drive.mount(\"/content/drive\")\n",
+        "# Please ensure that uploaded files are available in the AI Studio folder or change the working folder.\n",
+        "os.chdir(\"/content/drive/MyDrive/Google AI Studio\")"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DWbbJywhAhjL"
+      },
+      "source": [
+        "# Generated Code"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VywpA5h0AhjM"
+      },
+      "source": [
+        "# To run this code you need to install the following dependencies:\n",
+        "# pip install google-genai\n",
+        "\n",
+        "import base64\n",
+        "import mimetypes\n",
+        "import os\n",
+        "from google import genai\n",
+        "from google.genai import types\n",
+        "\n",
+        "\n",
+        "def save_binary_file(file_name, data):\n",
+        "    f = open(file_name, \"wb\")\n",
+        "    f.write(data)\n",
+        "    f.close()\n",
+        "    print(f\"File saved to to: {file_name}\")\n",
+        "\n",
+        "\n",
+        "def generate():\n",
+        "    client = genai.Client(\n",
+        "        api_key=os.environ.get(\"GEMINI_API_KEY\"),\n",
+        "    )\n",
+        "\n",
+        "    model = \"gemini-2.5-flash-image\"\n",
+        "    contents = [\n",
+        "        types.Content(\n",
+        "            role=\"user\",\n",
+        "            parts=[\n",
+        "                types.Part.from_text(text=\"\"\"INSERT_INPUT_HERE\"\"\"),\n",
+        "            ],\n",
+        "        ),\n",
+        "    ]\n",
+        "    generate_content_config = types.GenerateContentConfig(\n",
+        "        response_modalities=[\n",
+        "            \"IMAGE\",\n",
+        "            \"TEXT\",\n",
+        "        ],\n",
+        "    )\n",
+        "\n",
+        "    file_index = 0\n",
+        "    for chunk in client.models.generate_content_stream(\n",
+        "        model=model,\n",
+        "        contents=contents,\n",
+        "        config=generate_content_config,\n",
+        "    ):\n",
+        "        if (\n",
+        "            chunk.candidates is None\n",
+        "            or chunk.candidates[0].content is None\n",
+        "            or chunk.candidates[0].content.parts is None\n",
+        "        ):\n",
+        "            continue\n",
+        "        if chunk.candidates[0].content.parts[0].inline_data and chunk.candidates[0].content.parts[0].inline_data.data:\n",
+        "            file_name = f\"ENTER_FILE_NAME_{file_index}\"\n",
+        "            file_index += 1\n",
+        "            inline_data = chunk.candidates[0].content.parts[0].inline_data\n",
+        "            data_buffer = inline_data.data\n",
+        "            file_extension = mimetypes.guess_extension(inline_data.mime_type)\n",
+        "            save_binary_file(f\"{file_name}{file_extension}\", data_buffer)\n",
+        "        else:\n",
+        "            print(chunk.text)\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    generate()\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/notebooks/accelerating_pandas_with_gpu.ipynb b/notebooks/accelerating_pandas_with_gpu.ipynb
new file mode 100644
index 00000000..99aa807d
--- /dev/null
+++ b/notebooks/accelerating_pandas_with_gpu.ipynb
@@ -0,0 +1,365 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Accelerating pandas with GPU: Sort the count of rows grouped on columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Add `%load_ext cudf.pandas` before importing pandas to speed up operations using GPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext cudf.pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import random\n",
+    "\n",
+    "# Define the species categories\n",
+    "species_categories = ['setosa', 'versicolor', 'virginica']\n",
+    "flower_color_categories = ['red','yellow','green']\n",
+    "\n",
+    "# Define the range for each attribute based on typical iris flower measurements\n",
+    "sepal_length_range = (4.0, 8.0)\n",
+    "\n",
+    "# Create data for 1,000,000 samples\n",
+    "n = 1000000\n",
+    "data = {\n",
+    "    'sepal_length': [random.uniform(*sepal_length_range) for _ in range(n)],\n",
+    "    'flower_color': [random.choice(flower_color_categories) for _ in range(n)],\n",
+    "    'species': [random.choice(species_categories) for _ in range(n)]\n",
+    "}\n",
+    "\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "df.groupby(['species','flower_color']).size().sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Acceleration pandas with GPU: Merging / Joining dataframes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Add `%load_ext cudf.pandas` before importing pandas to speed up operations using GPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext cudf.pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# Define the number of rows\n",
+    "num_rows = 1000000\n",
+    "\n",
+    "states = ["NY", "NJ", "CA", "TX"]\n",
+    "violations = ["Double Parking", "Expired Meter", "No Parking", "Fire Hydrant",\n",
+    "              "Bus Stop"]\n",
+    "vehicle_types = ["SUBN", "SDN"]\n",
+    "\n",
+    "# Generate random data for Dataset 1\n",
+    "data1 = {\n",
+    "    "Registration State": np.random.choice(states, size=num_rows),\n",
+    "    "Ticket Number": np.random.randint(1000000000, 9999999999, size=num_rows)\n",
+    "}\n",
+    "\n",
+    "# Generate random data for Dataset 2\n",
+    "data2 = {\n",
+    "    "Ticket Number": np.random.choice(data1['Ticket Number'], size=num_rows),  # Reusing ticket numbers to ensure matches\n",
+    "    "Violation Description": np.random.choice(violations, size=num_rows)\n",
+    "}\n",
+    "\n",
+    "# Create DataFrames\n",
+    "df1 = pd.DataFrame(data1)\n",
+    "df2 = pd.DataFrame(data2)\n",
+    "\n",
+    "# Perform an inner join on 'Ticket Number'\n",
+    "merged_df = pd.merge(df1, df2, on="Ticket Number", how="inner")\n",
+    "\n",
+    "# Display some of the joined data\n",
+    "print(merged_df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Accelerating pandas with GPU: Groupby aggregate on timeseries data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Add `%load_ext cudf.pandas` before importing pandas to speed up operations using GPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext cudf.pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# Define the number of rows\n",
+    "num_rows = 1000000\n",
+    "\n",
+    "# Define the possible values\n",
+    "states = ["NY", "NJ", "CA", "TX"]\n",
+    "violations = ["Double Parking", "Expired Meter", "No Parking", "Fire Hydrant", "Bus Stop"]\n",
+    "vehicle_types = ["SUBN", "SDN"]\n",
+    "\n",
+    "start_date = "2022-01-01"\n",
+    "end_date = "2022-12-31"\n",
+    "# Create a date range\n",
+    "dates = pd.date_range(start=start_date, end=end_date, freq='D')\n",
+    "\n",
+    "# Generate random data\n",
+    "data = {\n",
+    "    "Registration State": np.random.choice(states, size=num_rows),\n",
+    "    "Violation Description": np.random.choice(violations, size=num_rows),\n",
+    "    "Vehicle Body Type": np.random.choice(vehicle_types, size=num_rows),\n",
+    "    "Issue Date": np.random.choice(dates, size=num_rows),\n",
+    "    "Ticket Number": np.random.randint(1000000000, 9999999999, size=num_rows)\n",
+    "}\n",
+    "\n",
+    "# Create a DataFrame\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "# Adding issue weekday based on the "Issue Date"\n",
+    "weekday_names = {\n",
+    "    0: "Monday",\n",
+    "    1: "Tuesday",\n",
+    "    2: "Wednesday",\n",
+    "    3: "Thursday",\n",
+    "    4: "Friday",\n",
+    "    5: "Saturday",\n",
+    "    6: "Sunday",\n",
+    "}\n",
+    "\n",
+    "df["issue_weekday"] = df["Issue Date"].dt.weekday.map(weekday_names)\n",
+    "\n",
+    "# Grouping by issue_weekday and counting the Summons Number\n",
+    "df.groupby(["Issue Date"])["Ticket Number"\
+",
+    "].count().sort_values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Accelerating pandas with GPU: Count of values and GroupBy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Add `%load_ext cudf.pandas` before importing pandas to speed up operations using GPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext cudf.pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# Randomly generated dataset of parking violations-\n",
+    "# Define the number of rows\n",
+    "num_rows = 1000000\n",
+    "\n",
+    "states = ["NY", "NJ", "CA", "TX"]\n",
+    "violations = ["Double Parking", "Expired Meter", "No Parking",\n",
+    "              "Fire Hydrant", "Bus Stop"]\n",
+    "vehicle_types = ["SUBN", "SDN"]\n",
+    "\n",
+    "# Create a date range\n",
+    "start_date = "2022-01-01"\n",
+    "end_date = "2022-12-31"\n",
+    "dates = pd.date_range(start=start_date, end=end_date, freq='D')\n",
+    "\n",
+    "# Generate random data\n",
+    "data = {\n",
+    "    "Registration State": np.random.choice(states, size=num_rows),\n",
+    "    "Violation Description": np.random.choice(violations, size=.ipynb_checkpoints/num_rows),\n",
+    "    "Vehicle Body Type": np.random.choice(vehicle_types, size=num_rows),\n",
+    "    "Issue Date": np.random.choice(dates, size=num_rows),\n",
+    "    "Ticket Number": np.random.randint(1000000000, 9999999999, size=num_rows)\n",
+    "}\n",
+    "\n",
+    "# Create a DataFrame\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "# Which parking violation is most commonly committed by vehicles from various U.S states?\n",
+    "\n",
+    "("df[["Registration State", "Violation Description"]]  # get only these two columns\n",
+    " .value_counts()  # get the count of offences per state and per type of offence\n",
+    " .groupby("Registration State")  # group by state\n",
+    " .head(1)  # get the first row in each group (the type of offence with the largest count)\n",
+    " .sort_index()  # sort by state name\n",
+    " .reset_index()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Accelerating pandas with GPU: Rolling Window Average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext cudf.pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Randomly generated dataset of parking violations-\n",
+    "# Define the number of rows\n",
+    "num_rows = 1000000\n",
+    "\n",
+    "states = ["NY", "NJ", "CA", "TX"]\n",
+    "violations = ["Double Parking", "Expired Meter", "No Parking",\n",
+    "              "Fire Hydrant", "Bus Stop"]\n",
+    "vehicle_types = ["SUBN", "SDN"]\n",
+    "\n",
+    "# Create a date range\n",
+    "start_date = "2022-01-01"\n",
+    "end_date = "2022-12-31"\n",
+    "dates = pd.date_range(start=start_date, end=end_date, freq='D')\n",
+    "\n",
+    "# Generate random data\n",
+    "data = {\n",
+    "    "Registration State": np.random.choice(states, size=num_rows),\n",
+    "    "Violation Description": np.random.choice(violations, size=num_rows),\n",
+    "    "Vehicle Body Type": np.random.choice(vehicle_types, size=num_rows),\n",
+    "    "Issue Date": np.random.choice(dates, size=num_rows),\n",
+    "    "Ticket Number": np.random.randint(1000000000, 9999999999, size=num_rows)\n",
+    "}\n",
+    "\n",
+    "# Create a DataFrame\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "# How does the parking violations change from day to day segmented by vehicle type\n",
+    "# Averaged using a 7-day rolling mean\n",
+    "\n",
+    "daily_counts = df.groupby(['Issue Date', 'Vehicle Body Type']\n",
+    "                          ).size().unstack(fill_value=0)\n",
+    "\n",
+    "# Calculate a 7-day rolling mean of daily violations for each vehicle type\n",
+    "rolling_means = daily_counts.rolling(window=7).mean()\n",
+    "\n",
+    "# Display the rolling means for each vehicle type over time\n",
+    "rolling_means.tail(100).plot(figsize=(14, 7),\n",
+    "                             title="7-Day Rolling Average of Parking Violations by Vehicle Type")\n",
+    "plt.ylabel("Average Number of Violations")\n",
+    "plt.xlabel("Date")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pandas_acceleration_with_gpu.ipynb b/pandas_acceleration_with_gpu.ipynb
new file mode 100644
index 00000000..6b149423
--- /dev/null
+++ b/pandas_acceleration_with_gpu.ipynb
@@ -0,0 +1,351 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/aka76bm/0xDeCA10B/blob/main/pandas_acceleration_with_gpu.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "N_63kkzYSrX1"
+      },
+      "source": [
+        "# Accelerating pandas with GPU: Sort the count of rows grouped on columns"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "igTJBL0vUb4F"
+      },
+      "source": [
+        "Add `%load_ext cudf.pandas` before importing pandas to speed up operations using GPU\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8fuXnmHmSBzq"
+      },
+      "outputs": [],
+      "source": [
+        "%load_ext cudf.pandas\n",
+        "import pandas as pd\n",
+        "import random\n",
+        "\n",
+        "# Define the species categories\n",
+        "species_categories = ['setosa', 'versicolor', 'virginica']\n",
+        "flower_color_categories = ['red','yellow','green']\n",
+        "\n",
+        "# Define the range for each attribute based on typical iris flower measurements\n",
+        "sepal_length_range = (4.0, 8.0)\n",
+        "\n",
+        "# Create data for 1,000,000 samples\n",
+        "n = 1000000\n",
+        "data = {\n",
+        "    'sepal_length': [random.uniform(*sepal_length_range) for _ in range(n)],\n",
+        "    'flower_color': [random.choice(flower_color_categories) for _ in range(n)],\n",
+        "    'species': [random.choice(species_categories) for _ in range(n)]\n",
+        "}\n",
+        "\n",
+        "df = pd.DataFrame(data)\n",
+        "\n",
+        "df.groupby(['species','flower_color']).size().sort_values(ascending=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t2DVoM7ifOAq"
+      },
+      "source": [
+        "# Acceleration pandas with GPU: Merging / Joining dataframes-"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k6MWgjQRfZl-"
+      },
+      "source": [
+        "Add `%load_ext cudf.pandas` before importing pandas to speed up operations using GPU"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WW3bxTL3f5Pd"
+      },
+      "outputs": [],
+      "source": [
+        "%load_ext cudf.pandas\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# Define the number of rows\n",
+        "num_rows = 1000000\n",
+        "\n",
+        "states = [\"NY\", \"NJ\", \"CA\", \"TX\"]\n",
+        "violations = [\"Double Parking\", \"Expired Meter\", \"No Parking\", \"Fire Hydrant\",\n",
+        "              \"Bus Stop\"]\n",
+        "vehicle_types = [\"SUBN\", \"SDN\"]\n",
+        "\n",
+        "# Generate random data for Dataset 1\n",
+        "data1 = {\n",
+        "    \"Registration State\": np.random.choice(states, size=num_rows),\n",
+        "    \"Ticket Number\": np.random.randint(1000000000, 9999999999, size=num_rows)\n",
+        "}\n",
+        "\n",
+        "# Generate random data for Dataset 2\n",
+        "data2 = {\n",
+        "    \"Ticket Number\": np.random.choice(data1['Ticket Number'], size=num_rows),  # Reusing ticket numbers to ensure matches\n",
+        "    \"Violation Description\": np.random.choice(violations, size=num_rows)\n",
+        "}\n",
+        "\n",
+        "# Create DataFrames\n",
+        "df1 = pd.DataFrame(data1)\n",
+        "df2 = pd.DataFrame(data2)\n",
+        "\n",
+        "# Perform an inner join on 'Ticket Number'\n",
+        "merged_df = pd.merge(df1, df2, on=\"Ticket Number\", how=\"inner\")\n",
+        "\n",
+        "# Display some of the joined data\n",
+        "print(merged_df.head())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "41DVaHZHS571"
+      },
+      "source": [
+        "# Accelerating pandas with GPU: Groupby aggregate on timeseries data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5YDBGAMOWZoG"
+      },
+      "source": [
+        "Add `%load_ext cudf.pandas` before importing pandas to speed up operations using GPU"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zlhe0827S1J5"
+      },
+      "outputs": [],
+      "source": [
+        "%load_ext cudf.pandas\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# Define the number of rows\n",
+        "num_rows = 1000000\n",
+        "\n",
+        "# Define the possible values\n",
+        "states = [\"NY\", \"NJ\", \"CA\", \"TX\"]\n",
+        "violations = [\"Double Parking\", \"Expired Meter\", \"No Parking\", \"Fire Hydrant\", \"Bus Stop\"]\n",
+        "vehicle_types = [\"SUBN\", \"SDN\"]\n",
+        "\n",
+        "start_date = \"2022-01-01\"\n",
+        "end_date = \"2022-12-31\"\n",
+        "# Create a date range\n",
+        "dates = pd.date_range(start=start_date, end=end_date, freq='D')\n",
+        "\n",
+        "# Generate random data\n",
+        "data = {\n",
+        "    \"Registration State\": np.random.choice(states, size=num_rows),\n",
+        "    \"Violation Description\": np.random.choice(violations, size=num_rows),\n",
+        "    \"Vehicle Body Type\": np.random.choice(vehicle_types, size=num_rows),\n",
+        "    \"Issue Date\": np.random.choice(dates, size=num_rows),\n",
+        "    \"Ticket Number\": np.random.randint(1000000000, 9999999999, size=num_rows)\n",
+        "}\n",
+        "\n",
+        "# Create a DataFrame\n",
+        "df = pd.DataFrame(data)\n",
+        "\n",
+        "# Adding issue weekday based on the \"Issue Date\"\n",
+        "weekday_names = {\n",
+        "    0: \"Monday\",\n",
+        "    1: \"Tuesday\",\n",
+        "    2: \"Wednesday\",\n",
+        "    3: \"Thursday\",\n",
+        "    4: \"Friday\",\n",
+        "    5: \"Saturday\",\n",
+        "    6: \"Sunday\",\n",
+        "}\n",
+        "\n",
+        "df[\"issue_weekday\"] = df[\"Issue Date\"].dt.weekday.map(weekday_names)\n",
+        "\n",
+        "# Grouping by issue_weekday and counting the Summons Number\n",
+        "df.groupby([\"Issue Date\"])[\"Ticket Number\"\n",
+        "].count().sort_values()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Kr4Niz4tWmCW"
+      },
+      "source": [
+        "# Accelerating pandas with GPU: Count of values and GroupBy\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l_OzcTpBezh9"
+      },
+      "source": [
+        "Add `%load_ext cudf.pandas` before importing pandas to speed up operations using GPU"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hYWROytuURFH"
+      },
+      "outputs": [],
+      "source": [
+        "%load_ext cudf.pandas\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# Randomly generated dataset of parking violations-\n",
+        "# Define the number of rows\n",
+        "num_rows = 1000000\n",
+        "\n",
+        "states = [\"NY\", \"NJ\", \"CA\", \"TX\"]\n",
+        "violations = [\"Double Parking\", \"Expired Meter\", \"No Parking\",\n",
+        "              \"Fire Hydrant\", \"Bus Stop\"]\n",
+        "vehicle_types = [\"SUBN\", \"SDN\"]\n",
+        "\n",
+        "# Create a date range\n",
+        "start_date = \"2022-01-01\"\n",
+        "end_date = \"2022-12-31\"\n",
+        "dates = pd.date_range(start=start_date, end=end_date, freq='D')\n",
+        "\n",
+        "# Generate random data\n",
+        "data = {\n",
+        "    \"Registration State\": np.random.choice(states, size=num_rows),\n",
+        "    \"Violation Description\": np.random.choice(violations, size=num_rows),\n",
+        "    \"Vehicle Body Type\": np.random.choice(vehicle_types, size=num_rows),\n",
+        "    \"Issue Date\": np.random.choice(dates, size=num_rows),\n",
+        "    \"Ticket Number\": np.random.randint(1000000000, 9999999999, size=num_rows)\n",
+        "}\n",
+        "\n",
+        "# Create a DataFrame\n",
+        "df = pd.DataFrame(data)\n",
+        "\n",
+        "# Which parking violation is most commonly committed by vehicles from various U.S states?\n",
+        "\n",
+        "(df[[\"Registration State\", \"Violation Description\"]]  # get only these two columns\n",
+        " .value_counts()  # get the count of offences per state and per type of offence\n",
+        " .groupby(\"Registration State\")  # group by state\n",
+        " .head(1)  # get the first row in each group (the type of offence with the largest count)\n",
+        " .sort_index()  # sort by state name\n",
+        " .reset_index()\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4sWzpEvabvBW"
+      },
+      "source": [
+        "# Accelerating pandas with GPU: Rolling Window Average\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qbKJGeVnb6Uj"
+      },
+      "outputs": [],
+      "source": [
+        "%load_ext cudf.pandas\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Randomly generated dataset of parking violations-\n",
+        "# Define the number of rows\n",
+        "num_rows = 1000000\n",
+        "\n",
+        "states = [\"NY\", \"NJ\", \"CA\", \"TX\"]\n",
+        "violations = [\"Double Parking\", \"Expired Meter\", \"No Parking\",\n",
+        "              \"Fire Hydrant\", \"Bus Stop\"]\n",
+        "vehicle_types = [\"SUBN\", \"SDN\"]\n",
+        "\n",
+        "# Create a date range\n",
+        "start_date = \"2022-01-01\"\n",
+        "end_date = \"2022-12-31\"\n",
+        "dates = pd.date_range(start=start_date, end=end_date, freq='D')\n",
+        "\n",
+        "# Generate random data\n",
+        "data = {\n",
+        "    \"Registration State\": np.random.choice(states, size=num_rows),\n",
+        "    \"Violation Description\": np.random.choice(violations, size=num_rows),\n",
+        "    \"Vehicle Body Type\": np.random.choice(vehicle_types, size=num_rows),\n",
+        "    \"Issue Date\": np.random.choice(dates, size=num_rows),\n",
+        "    \"Ticket Number\": np.random.randint(1000000000, 9999999999, size=num_rows)\n",
+        "}\n",
+        "\n",
+        "# Create a DataFrame\n",
+        "df = pd.DataFrame(data)\n",
+        "\n",
+        "# How does the parking violations change from day to day segmented by vehicle type\n",
+        "# Averaged using a 7-day rolling mean\n",
+        "\n",
+        "daily_counts = df.groupby(['Issue Date', 'Vehicle Body Type']\n",
+        "                          ).size().unstack(fill_value=0)\n",
+        "\n",
+        "# Calculate a 7-day rolling mean of daily violations for each vehicle type\n",
+        "rolling_means = daily_counts.rolling(window=7).mean()\n",
+        "\n",
+        "# Display the rolling means for each vehicle type over time\n",
+        "rolling_means.tail(100).plot(figsize=(14, 7),\n",
+        "                             title=\"7-Day Rolling Average of Parking Violations by Vehicle Type\")\n",
+        "plt.ylabel(\"Average Number of Violations\")\n",
+        "plt.xlabel(\"Date\")\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "private_outputs": true,
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/simulation/decai/simulation/simulate_imdb_perceptron.py b/simulation/decai/simulation/simulate_imdb_perceptron.py
index ec093c26..a503f5de 100644
--- a/simulation/decai/simulation/simulate_imdb_perceptron.py
+++ b/simulation/decai/simulation/simulate_imdb_perceptron.py
@@ -1,4 +1,5 @@
 import os
+import re
 import sys
 from typing import Optional
 
@@ -82,5 +83,7 @@ def main():
 
 
 # Run with `bokeh serve PATH`.
-if __name__.startswith('bk_script_'):
+if re.match('bk_script_|bokeh_app_', __name__):
     main()
+else:
+    print("`__name__` didn't match the pattern. Bokeh app will not run.")