vivek081166/heatmap.ipynb Secret

## heatmap.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "heatmap.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "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/gist/vivek081166/5162e831c56c5b11e307339b93b8967b/heatmap.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "55u3erlZa41Z",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Heatmap"
      ]
    },
    {
      "metadata": {
        "id": "s1MBeaOyakGs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "0516641d-aab6-46d7-ae58-3a9cbc007b9f"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "\n",
        "# Make a 10 x 10 heatmap of some random data\n",
        "side_length = 10\n",
        "# Start with a 10 x 10 matrix with values randomized around 5\n",
        "data = 5 + np.random.randn(side_length, side_length)\n",
        "# The next two lines make the values larger as we get closer to (9, 9)\n",
        "data += np.arange(side_length)\n",
        "data += np.reshape(np.arange(side_length), (side_length, 1))\n",
        "# Generate the heatmap\n",
        "sns.heatmap(data)\n",
        "plt.show()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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W22+/rYaGBi1evNjRb+zdu3fXmDFjNGnSJElSTk6O45+rsrIydenSxdEMkpSZ\nmakFCxYoKytLjY2NWrx4saN5+vXrJ8uydMMNNyg2NtbRDT1ux9sDAQA8gR+/AQCeQOEBADyBwgMA\neAKFBwDwBAoPAOAJFB4AwBMoPACAJ1B4AABP+D/Ym92lubyxAgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "J8OZTmObamKQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}
	{
	"nbformat": 4,
	"nbformat_minor": 0,
	"metadata": {
	"colab": {
	"name": "heatmap.ipynb",
	"version": "0.3.2",
	"provenance": [],
	"include_colab_link": true
	},
	"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/gist/vivek081166/5162e831c56c5b11e307339b93b8967b/heatmap.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
	]
	},
	{
	"metadata": {
	"id": "55u3erlZa41Z",
	"colab_type": "text"
	},
	"cell_type": "markdown",
	"source": [
	"### Heatmap"
	]
	},
	{
	"metadata": {
	"id": "s1MBeaOyakGs",
	"colab_type": "code",
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 349
	},
	"outputId": "0516641d-aab6-46d7-ae58-3a9cbc007b9f"
	},
	"cell_type": "code",
	"source": [
	"import matplotlib.pyplot as plt\n",
	"import numpy as np\n",
	"import seaborn as sns\n",
	"\n",
	"# Make a 10 x 10 heatmap of some random data\n",
	"side_length = 10\n",
	"# Start with a 10 x 10 matrix with values randomized around 5\n",
	"data = 5 + np.random.randn(side_length, side_length)\n",
	"# The next two lines make the values larger as we get closer to (9, 9)\n",
	"data += np.arange(side_length)\n",
	"data += np.reshape(np.arange(side_length), (side_length, 1))\n",
	"# Generate the heatmap\n",
	"sns.heatmap(data)\n",
	"plt.show()"
	],
	"execution_count": 2,
	"outputs": [
	{
	"output_type": "display_data",
	"data": {
	"image/png": 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W22+/rYaGBi1evNjRb+zdu3fXmDFjNGnSJElSTk6O45+rsrIydenSxdEMkpSZ\nmakFCxYoKytLjY2NWrx4saN5+vXrJ8uydMMNNyg2NtbRDT1ux9sDAQA8gR+/AQCeQOEBADyBwgMA\neAKFBwDwBAoPAOAJFB4AwBMoPACAJ1B4AABP+D/Ym92lubyxAgAAAABJRU5ErkJggg==\n",
	"text/plain": [
	"<Figure size 576x396 with 2 Axes>"
	]
	},
	"metadata": {
	"tags": []
	}
	}
	]
	},
	{
	"metadata": {
	"id": "J8OZTmObamKQ",
	"colab_type": "code",
	"colab": {}
	},
	"cell_type": "code",
	"source": [
	""
	],
	"execution_count": 0,
	"outputs": []
	}
	]
	}