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@mohite-abhi
Created January 17, 2022 13:26
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Untitled5.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Untitled5.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyPqgGtXAE0XyHZKZpFMbAUw",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/mohite-abhi/7e64b0d1b6e0cc8ae24b9f2ff6f5d7a6/untitled5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"source": [
"import numpy as np \n",
"from matplotlib import pyplot as plt \n",
"\n",
"x = np.arange(1,11) \n",
"y = 2 * x + 5 \n",
"plt.title(\"Matplotlib demo\") \n",
"plt.xlabel(\"x axis caption\") \n",
"plt.ylabel(\"y axis caption\") \n",
"plt.plot(x,y) \n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "o9-pkC_SHhE_",
"outputId": "9a0fbafe-d6ab-4a80-f689-35d48f24f82f"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
}
]
}
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