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

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seaborn.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "seaborn.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/fac63b773491f432aaf2811da61624d6/seaborn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"id": "S1qCeH9pWvgH",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"### Seaborn"
]
},
{
"metadata": {
"id": "w3vVSq_wWt_G",
"colab_type": "code",
"outputId": "9a811dce-ad0b-47b1-851f-6f17f0fd1912",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 347
}
},
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns\n",
"\n",
"# Generate some random data\n",
"num_points = 20\n",
"# x will be 5, 6, 7... but also twiddled randomly\n",
"x = 5 + np.arange(num_points) + np.random.randn(num_points)\n",
"# y will be 10, 11, 12... but twiddled even more randomly\n",
"y = 10 + np.arange(num_points) + 5 * np.random.randn(num_points)\n",
"sns.regplot(x, y)\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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778Zzzz2HI0eOYPfu3ZVoL1FVpFKZGdPpfzLznowWdVwZxxJJvHtuBD19PlwO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\nCIYyERGRQvz/mlzliLsgJPMAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "vnu99Lk-W2s6",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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