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@vivek081166 vivek081166/heatmap.ipynb Secret
Created Feb 27, 2019

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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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ODwDgCa7epQkAQGsZ3ncUHgDAHqZPeGxaAQB4AoUHAPAEljQBALYwfEWTwgMA2MP0c3gU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\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": []
}
]
}
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