Accelerating pandas with GPU: Sort the count of rows grouped on columns

Access_GitHub_Link.ipynb

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+{
+  "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": []
+    }
+  ]
+}