{ "cells": [ { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "fig, ax = plt.subplots()\n", "\n", "datas = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n", "\n", "x = np.linspace(0, 100, np.size(datas))\n", "\n", "plt.plot(x, datas, 'b')\n", "plt.grid()\n", "#plt.show()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax.hist(datas, bins=10)\n", "\n", "plt.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }