no commit message

parent e9589700
......@@ -16,15 +16,35 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 91,
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [],
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'ipympl'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-91-fbc14d9ed2fa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Importation des librairies\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'widget'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2315\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2316\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2317\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\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[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2318\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2319\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m</opt/conda/lib/python3.6/site-packages/decorator.py:decorator-gen-108>\u001b[0m in \u001b[0;36mmatplotlib\u001b[0;34m(self, line)\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\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;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\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/IPython/core/magics/pylab.py\u001b[0m in \u001b[0;36mmatplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Available matplotlib backends: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mbackends_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m \u001b[0mgui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_matplotlib\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 100\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_show_matplotlib_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36menable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3417\u001b[0m \u001b[0mgui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_gui_and_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpylab_gui_select\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3418\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3419\u001b[0;31m \u001b[0mpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivate_matplotlib\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3420\u001b[0m \u001b[0mpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfigure_inline_support\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mactivate_matplotlib\u001b[0;34m(backend)\u001b[0m\n\u001b[1;32m 320\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 321\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswitch_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_needmain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mswitch_backend\u001b[0;34m(newbackend)\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewbackend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 232\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackends\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpylab_setup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m \u001b[0m_backend_mod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_figure_manager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdraw_if_interactive\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_show\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpylab_setup\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 234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/backends/__init__.py\u001b[0m in \u001b[0;36mpylab_setup\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0;31m# imports. 0 means only perform absolute imports.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m backend_mod = __import__(backend_name, globals(), locals(),\n\u001b[0;32m---> 62\u001b[0;31m [backend_name], 0)\n\u001b[0m\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;31m# Things we pull in from all backends\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'ipympl'"
]
}
],
"source": [
"# Importation des librairies\n",
"%matplotlib inline\n",
"%matplotlib widget\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
......@@ -1189,22 +1209,22 @@
},
{
"cell_type": "code",
"execution_count": 81,
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f3a684c1eb8>"
"<matplotlib.colorbar.Colorbar at 0x7f3a68705a58>"
]
},
"execution_count": 81,
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
......@@ -1232,6 +1252,7 @@
"graphique10 = plt.figure()\n",
"ax_10_1 = graphique10.add_subplot(111)\n",
"ax_10_1.set(xbound=[20,120],ybound=[0,35])\n",
"ax_10_1.set(xlabel=\"Wheat Price (shilling per 1/4 bushel)\",ylabel=\"Wages (shilling per week)\")\n",
"# Create a continuous norm to map from data points to colors\n",
"norm = plt.Normalize(raw_data[\"Year\"].min(), raw_data[\"Year\"].max())\n",
"lc = LineCollection(segments, cmap='viridis', norm=norm)\n",
......@@ -1245,14 +1266,44 @@
{
"cell_type": "markdown",
"metadata": {},
"source": []
"source": [
"Enfin, pour terminer, je vais tracer cette courbe en 3D en utilisant le temps pour l'axe Z."
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"data": {
"text/plain": [
"[<mpl_toolkits.mplot3d.art3d.Line3D at 0x7f3a68327b70>]"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from mpl_toolkits.mplot3d import Axes3D\n",
"graphique_11= plt.figure()\n",
"ax_11_1 = graphique_11.add_subplot(111, projection='3d')\n",
"ax_11_1.plot(y_wheat,y_wages,x)"
]
},
{
"cell_type": "code",
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