From 33b899d7799c94ad893763b890ccc299f35b2fbf Mon Sep 17 00:00:00 2001 From: b01eecdd10ebdeb5f670ee72492d7b64 Date: Thu, 9 Jun 2022 20:37:50 +0000 Subject: [PATCH] en cours --- module2/exo3/exercice.ipynb | 96 +++++++++++++++++++++++++++++++++++-- 1 file changed, 93 insertions(+), 3 deletions(-) diff --git a/module2/exo3/exercice.ipynb b/module2/exo3/exercice.ipynb index 0bbbe37..09b29fe 100644 --- a/module2/exo3/exercice.ipynb +++ b/module2/exo3/exercice.ipynb @@ -1,5 +1,96 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Représentation graphique : plot" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "x=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]\n", + "y=[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", + "plt.plot(x,y)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Représentation graphique : histogramme" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "shape mismatch: objects cannot be broadcast to a single shape", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;31m# Make args iterable too.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2238\u001b[0;31m np.atleast_1d(x), height, width, y, linewidth)\n\u001b[0m\u001b[1;32m 2239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2240\u001b[0m \u001b[0;31m# Now that units have been converted, set the tick locations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/numpy/lib/stride_tricks.py\u001b[0m in \u001b[0;36mbroadcast_arrays\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 250\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_m\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubok\u001b[0m\u001b[0;34m)\u001b[0m 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\u001b[0;36m_broadcast_shape\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;31m# use the old-iterator because np.nditer does not handle size 0 arrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;31m# consistently\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 188\u001b[0m \u001b[0;31m# unfortunately, it cannot handle 32 or more arguments directly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpos\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m31\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "x = [2.8, 5, 7.5, 10, 12.5, 15, 17.5, 20, 23.4]\n", + "y = [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", + "plt.bar(x,y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +107,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - -- 2.18.1