dtale_library_dataset2.ipynb

{
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  "metadata": {
    "colab": {
      "name": "dtale_library_dataset2.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_5jAb5Cx5SYH",
        "outputId": "ca01fe98-7728-4b2b-df82-95b1033a5899"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Downloading Brotli-1.0.9-cp37-cp37m-manylinux1_x86_64.whl (357 kB)\n",
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            "  Building wheel for dash-colorscales (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Successfully built dash-colorscales dash-daq\n",
            "Installing collected packages: brotli, scipy, Flask-Compress, dash-table, dash-html-components, dash-core-components, dash, strsimpy, statsmodels, squarify, packaging, missingno, lz4, kaleido, flask-ngrok, dash-daq, dash-colorscales, dash-bootstrap-components, dtale\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.4.1\n",
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            "    Found existing installation: statsmodels 0.10.2\n",
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            "  Attempting uninstall: packaging\n",
            "    Found existing installation: packaging 21.3\n",
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            "  Attempting uninstall: missingno\n",
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            "      Successfully uninstalled missingno-0.5.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
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            "Successfully installed Flask-Compress-1.12 brotli-1.0.9 dash-2.4.1 dash-bootstrap-components-1.1.0 dash-colorscales-0.0.4 dash-core-components-2.0.0 dash-daq-0.5.0 dash-html-components-2.0.0 dash-table-5.0.0 dtale-2.4.0 flask-ngrok-0.0.25 kaleido-0.2.1 lz4-4.0.0 missingno-0.4.2 packaging-21.0 scipy-1.7.3 squarify-0.4.3 statsmodels-0.12.2 strsimpy-0.2.1\n"
          ]
        }
      ],
      "source": [
        "pip install dtale"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv(\"Dataset 2.csv\")"
      ],
      "metadata": {
        "id": "H21awDbc5c3l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import dtale\n",
        "d = dtale.show(df)\n",
        "d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vo73-k1X6Sp7",
        "outputId": "64c29d80-492c-4d8d-d4f2-bbe13b4246a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "http://c69d99a38825:40000/dtale/main/2"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_uhbqXL6dbm",
        "outputId": "6a79933f-dde1-430c-864e-cb37e19cc48f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "http://c69d99a38825:40000/dtale/main/1"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d = dtale.show(df,ignore_duplicate=True)\n",
        "d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JUPCDAF66uxL",
        "outputId": "4e50d521-6c8f-48ae-8851-13904a67b561"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "http://c69d99a38825:40000/dtale/main/4"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "import dtale\n",
        "import dtale.app as dtale_app\n",
        "\n",
        "dtale_app.USE_COLAB = True"
      ],
      "metadata": {
        "id": "ObD1IHby7Vvy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dtale.show(df)"
      ],
      "metadata": {
        "id": "OsoY_4Fr8J9-",
        "outputId": "a7c60b70-e43d-4f08-f5b8-7df171532894",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "https://z10b9mm3cs-496ff2e9c6d22116-40000-colab.googleusercontent.com/dtale/main/5"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "a5WBa3zH8MhY"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}