python-pour-finance/04-Visualisation-Matplotlib-Pandas/04-01-Matplotlib/Matplotlib Exercices.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib Exercices\n",
    "\n",
    "Bienvenue sur les exercices de révision de matplotlib! Prenez votre temps surtout, Matplotlib peut être difficile à comprendre au début. Ce sont des tracés relativement simples, mais ils peuvent être difficiles si c'est la première fois que vous utilisez matplotlib, n'hésitez pas à vous référer aux solutions au fur et à mesure que vous avancez.\n",
    "\n",
    "Ne vous inquiétez pas non plus si vous trouvez la syntaxe matplotlib frustrante, nous ne l'utiliserons pas souvent tout au long du cours, nous allons passer à l'utilisation des capacités de visualisation intégrées de pandas. Mais, ceux-ci sont construits à partir de matplotlib, c'est pourquoi il est toujours important d'y être exposé !\n",
    "\n",
    "**NOTE : TOUTES LES COMMANDES POUR TRACER UNE FIGURE DOIVENT ÊTRE PRESENTES DANS LA MÊME CELLULE. LES SÉPARER EN PLUSIEURS CELLULES PEUT NE RIEN FAIRE APPARAÎTRE.**\n",
    "\n",
    "# Exercices\n",
    "\n",
    "Suivez les instructions pour recréer les tracés à l'aide de ces données:\n",
    "\n",
    "## Données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = np.arange(0,100)\n",
    "y = x*2\n",
    "z = x**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importer matplotlib.pyplot en tant que plt et définir %matplotlib inline si vous utilisez le notebook jupyter. Quelle commande utilisez-vous si vous n'utilisez pas Jupyter Notebook?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercice 1\n",
    "\n",
    "**Suivre ces étapes**\n",
    "* **Créer un objet Figure qu'on nomme fig en utilisant plt.figure()**\n",
    "* **Utiliser add_axes pour ajouter un axe sur le canevas de la figure à [0,0,1,1]. Appeler ce nouvel axe ax.**\n",
    "* **Tracer (x,y) sur ces axes et définir les étiquettes et titres pour correspondre au graphique ci-dessous:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'title')"
      ]
     },
     "execution_count": 3,
     "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": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercice 2\n",
    "**Créez un objet Figure et placez-y deux axes, ax1 et ax2: situés à [0,0,1,1] et [0.2,0.5,.2,.2] respectivement.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tracer maintenant (x,y) sur les deux axes. Afficher le résultat.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercice 3\n",
    "\n",
    "**Créer le tracé ci-dessous en ajoutant deux axes à un objet figure à [0,0,1,1] et [0.2,0.5,.4,.4].**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Utiliser maintenant les tableaux x, y et z pour recréer le graphique ci-dessous. Notez les limites x et y sur le tracé inséré:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p0yNvDvXCUpCLhCH1qiXUHTwIN97ovqvz57vvoeRPQS4SotSrlnBlLQwY4GZvmzABWrf2u6LgpiAXCVLqVUukevppt6b4c89Bz55+VxP8FOQiPlKvWuS33n/fBfltt8Fjj/ldTWhQkIuUolPtVder5x6rVy2R4Isv3CH1q6+Gd97RH6aFpSAXOUXqVYucuv/9zw1ua9AAPv4Yypb1u6LQoSAXOQmdqxYpXRkZ0Lmzu0Z8xgyoXNnvikKLglwEzQEu4pf9+6FrV/cH8WefuX9HUjQKcokIJbmyVp06UL68P+9DJJzk5kK/fvDNN+5weqtWflcUmhTkEjZKqlddvz5UqaJetUhpGzwYJk92i6F07+53NaFLQS4hQ71qkfDx5pvwyivwwAPw0EN+VxPaFOQSVIraq65Rw4W1etUioWPKFLccadeurjeuf6enRkEu5OTkkJCQQI0aNZg+fTrr1q2jd+/eZGZm0rx5c95//33KltC1IMXpVdevDxdcoF61SDhYuBD69IFLL4UPP4SoKL8rCn0KcmH48OE0atSI3bt3A/Doo48yaNAgevfuzd13383o0aO55557Cv16xelV168PHTv+ej21etUi4Wf1arjhBvdv/p//hAoV/K4oPCjIw8Sbb75J3759qVzECzDT09OZMWMGjz/+OK+88grWWlJTU/nggw8ASExM5K9//etJg3z9erjiipP3qo8Na/WqRSLDkWvFAf71L/dHupQMBXmY+Pnnn2nZsiXNmzfnjjvuoFOnTphCdGUHDhzIiy++yJ49ewDYsWMHlSpVIjrafTVq1qzJpk2b8t03OTmZ5ORkALKycilTxgV13pnK1KsWkX373Lri6emQmuoGoErJKeN3AVIynnvuOVatWsWAAQMYO3YssbGxPPbYY6xZs6bAfaZPn05MTAwtWrQ42matPW67gv4gSEpKIi0tjbS0NOLjy/D55/Duu/CXv8Ctt7qlB2NiFOIikezwYejdG9LS3Dnx3/3O74rCj3rkYcQYw/nnn8/5559PdHQ0O3fupEePHnTo0IEXX3zxuO0XLFjAtGnTmDlzJgcOHGD37t0MHDiQrKwsDh8+THR0NOnp6VSvXt2HdyMioc5auPdemD4d3n7bzaUuJU898jDx+uuv06JFCx555BHatGnD999/z4gRI1iyZAkff/xxvvsMHTqU9PR01q9fz/jx42nXrh3jxo3j6quvZtKkSQCkpKTQrVu3QL4VEQkTzz0Hf/+7W460CONlpYjUIw8T27dvZ/LkydSpU+c37WXKlGH69OlFeq1hw4bRu3dvnnjiCZo1a8aAAQNKslQRiQBjxsCTT0L//i7QpfSY/M6JhrOEhASblpbmdxlhJyEhAX2uEkr0nS0906e7w+jXXOMuMzvtNL8rCg/GmCXW2oRj23VoXURESszXX0PPntCsGUyapBAPBAW5iIiUiBUr3GVmNWq4dcUrVvS7osigIBcRkVO2aRNce63rgc+e7S49lcDQYDcRETklO3e6EN+5Ez7/3E0EJYGjIBcRkWL75Rc3f/r//gezZrlz4xJYCnIRESmW7Gzo1Qu++gomToSrr/a7osikIBcRkSKzFpKS3KVmI0ZAjx5+VxS5NNhNRESK7NFHYexY+Otf4e67/a4msinIRUSkSIYNg5degvvvd7O3ib8U5CIiUmh//zsMGQJ9+sDw4VrdMBgoyEVEpFA+/tgdRu/c2R1WL6MECQr6zyAiIic1dy7ceiu0bu2mXi1b1u+K5AgFuYiInNDChW4RlIsucqPUK1TwuyLJS0EuIiIF+v57dyj9/PNhzhyoXNnviuRYCnIREcnX6tXQsSOccYY7tH7++X5XJPnRhDAiInKcTZugQwc3e1tqKtSt63dFUhAFuYiI/EZGhgvxHTtciDdq5HdFciIKchEROSorCzp1gnXr3HKkCQl+VyQnoyAXEREA9u2D66+HH36AadPgyiv9rkgKw5fBbsaYSsaYScaYlcaYFcaY3xljzjHGfGqMWeX9rOxta4wxrxtjVhtj/mOMaZ7ndRK97VcZYxL9eC+h7MCBA1x66aU0bdqUxo0b89RTTwFw2223Ua9ePeLj44mPj2fZsmU+Vyoipe3AAeje3V1q9sEHbn1xCQ1+9ciHA7OstT2MMWWBCsBjwDxr7QvGmCHAEOBRoDMQ691aASOAVsaYc4CngATAAkuMMdOstTsD/3ZCU7ly5UhNTaVixYpkZ2dz+eWX07lzZwBeeuklemg5I5GIkJ0NPXvCp5/Cu+9qJbNQE/AeuTHmLOBKYDSAtfaQtTYL6AakeJulADd697sB71lnIVDJGFMN6AR8aq3N9ML7U0B/QxaBMYaKFSsCkJ2dTXZ2NkYTJ4tElJwc6NcP/vlPeOstuO02vyuSovLj0Hp9IAN41xiz1BgzyhhzBlDVWrsFwPsZ421fA9iYZ/90r62g9uMYY5KMMWnGmLSMjIySfTchLicnh/j4eGJiYujQoQOtWrUC4PHHHycuLo5BgwZx8OBBn6sUkdKQmwsDBsDEiW41s3vv9bsiKQ4/gjwaaA6MsNY2A/bhDqMXJL8uoj1B+/GN1iZbaxOstQlVqlQpar1hLSoqimXLlpGens6iRYv44YcfGDp0KCtXrmTx4sVkZmYybNiwfPdNTk4mISGBhIQE9AeSSGixFh54AFJS3Jrif/qT3xVJcfkR5OlAurX2G+/xJFywb/UOmeP93JZn+1p59q8JbD5BuxRDpUqVaNu2LbNmzaJatWoYYyhXrhy33347ixYtynefpKQk0tLSSEtLQ38giYQOa+GPf4S334bBg7WmeKgLeJBba38GNhpjLvKa2gM/AtOAIyPPE4Gp3v1pQH9v9HprYJd36H020NEYU9kb4d7Ra5NCysjIICsrC4D9+/czd+5cGjZsyJYtWwCw1jJlyhSaNGniZ5kiUoKshccfh1dfdT3yYcO0pnio82vU+gPAOG/E+lrgdtwfFRONMQOADcAt3rYzgeuA1cAv3rZYazONMc8Ci73tnrHWZgbuLYS+LVu2kJiYSE5ODrm5ufTs2ZMuXbrQrl07MjIysNYSHx/PyJEj/S5VRErIs8/C0KGQlATDhyvEw4EvQW6tXYa7bOxY7fPZ1gL3FfA6Y4AxJVtd5IiLi2Pp0qXHtaempvpQjYiUtmHD4Kmn3Mj0ESMU4uFCq5+JiESAV16BIUOgTx8YNQrK6P/+YUP/KUVEwtzrr7vBbbfcAu+9B1FRflckJUlBLiISxt5+Gx56yE2/Om4cRGuFjbCjIBcRCVPJyXDffdC1K4wfD6ed5ndFUhoU5CIiYWjUKLjrLrjuOjdzW9myflckpUVBLiISZkaNgj/8ATp3ho8/hnLl/K5ISpOCXEQkjIwe7UL82mth8mQoX97viqS0KchFRMLEmDG/hvgnnyjEI4WCXEQkDIwa5VYy69hRIR5pFOQiIiEuOfnXnviUKQrxSKMgFxEJYSNH/jo6XT3xyKQgFxEJUW+/DffcA126aGBbJFOQi4iEoOHDf53sZdIkXWIWyRTkIiIh5qWXYOBAuOkm+OgjhXikU5CLiISQ55+HRx6B3r3dtKuasU0U5CIiIcBat5b4E0/A738P77+vudPF0To4IiJBzlp49FF3SP2OO9zlZlqKVI5QkIuIBLHcXLcM6ZtvuhHqb74JZXQsVfLQ10FEJEjl5LhrxN98Ex5+GN56SyEux9NXQkQkCB0+DImJburVJ56Al18GY/yuSoKRgjyCHThwgEsvvZSmTZvSuHFjnnrqKQDWrVtHq1atiI2NpVevXhw6dMjnSkUiy8GD0LMnjBvnRqk/+6xCXAqmII9g5cqVIzU1le+++45ly5Yxa9YsFi5cyKOPPsqgQYNYtWoVlStXZvTo0X6XKhIxfvnFTfLyySdu0pfHHvO7Igl2JwxyY0ynEzx3S8mXI4FkjKFixYoAZGdnk52djTGG1NRUevToAUBiYiJTpkzxs0yRiLF7t1v4ZO5ct674gw/6XZGEgpP1yGcaY+YbY2rk89yfS6MgCaycnBzi4+OJiYmhQ4cONGjQgEqVKhEd7S5oqFmzJps2bfK5SpHwt307tG8PX38NH37oLjMTKYyTXX72H+ADYKEx5mFr7Ud5ntMZmzAQFRXFsmXLyMrKonv37qxYseK4bUwBJ+eSk5NJTk4G4IcffiAhIaFUay0pGRkZVKlSxe8yCiWUaoXQqnflypV+l3DUpk3QoQOsW+eWIb3+er8rklBysiC31tq/G2M+B8YZY64D7rPW/gLY0i9PAqVSpUq0bduWhQsXkpWVxeHDh4mOjiY9PZ3q1avnu09SUhJJSUkAJCQkkJaWFsiSi021lp5QqjdY/vBcswauuQZ27IBZs+Cqq/yuSEJNoQa7WWv/B/wO2AosNca0KtWqJCAyMjLIysoCYP/+/cydO5dGjRpx9dVXM2nSJABSUlLo1q2bn2WKhK3vv4fLL4c9eyA1VSEuxXOyHvnRY6rW2sPAEGPMLOBDIDSOn0mBtmzZQmJiIjk5OeTm5tKzZ0+6dOnCxRdfTO/evXniiSdo1qwZAwYM8LtUkbDz9ddw3XVQoQJ88QVcfLHfFUmoOlmQP31sg7X2M2NMC+Cu0ilJAiUuLo6lS5ce116/fn0WLVpUpNc6cog9FKjW0hNK9fpZ66xZcPPNUL06zJkD9er5VoqEAWNtZJ3qTkhIsKFyDk9Ews/48W71siZNXKBXrep3RRIqjDFLrLXHDe7QhDAiIgHy9ttw661w2WXw2WcKcSkZCnI5oY0bN3L11VfTqFEjGjduzPDhwwHIzMykQ4cOxMbG0qFDB3bu3Jnv/ikpKcTGxhIbG0tKSkpQ1xoVFUV8fDzx8fF07drVl1o/+ugjGjduTJkyZU44+nvWrFlcdNFFXHDBBbzwwgtBXWvdunW55JJLiI+PL/WR4gXVOnjwYBo2bEhcXBzdu3c/OsjzWKX1uR5ZS/y++6BLF9cTP/vsEnt5iXTW2oi6tWjRwkrhbd682S5ZssRaa+3u3bttbGysXb58uR08eLAdOnSotdbaoUOH2kceeeS4fXfs2GHr1atnd+zYYTMzM229evVsZmZmUNZqrbVnnHFGqdVW2Fp//PFHu3LlSnvVVVfZxYsX57vv4cOHbf369e2aNWvswYMHbVxcnF2+fHlQ1mqttXXq1LEZGRmlVl9hap09e7bNzs621lr7yCOP5PsdKK3P9fBha++6y1qw9o47rPXKECkyIM3mk2vqkcsJVatWjebNmwNw5pln0qhRIzZt2sTUqVNJTEwECp7Gdfbs2XTo0IFzzjmHypUr06FDB2bNmhWUtQZaQbU2atSIiy666IT7Llq0iAsuuID69etTtmxZevfuzdSpU4Oy1kArqNaOHTsena2wdevWpKenH7dvaXyuBw64xU/eeQeGDHErmUWfbIixSBEpyKXQ1q9fz9KlS2nVqhVbt26lWrVqgPuf57Zt247bftOmTdSqVevo40BO91rUWsGtBpeQkEDr1q0DGvZ5ay2MYPlcC8sYQ8eOHWnRosXRmQADoaBax4wZQ+fOnY/bvqQ/16wsN2/65Mnw6qswdKhWMJPSob8NpVD27t3LzTffzGuvvcZZZ51VqH1sPldEFDTda0kqTq0AGzZsoHr16qxdu5Z27dpxySWX0KBBg1KsNDI+1wULFlC9enW2bdtGhw4daNiwIVdeeWUpVlpwrc8//zzR0dH07dv3uH1K8nPdtAk6d4aVK91SpLfeWqyXESkU9cjlpLKzs7n55pvp27cvN910EwBVq1Zly5YtgJtYJiYm5rj9atasycaNG48+PtF0r37XChytrX79+rRt2zbfa+xLu9bCCJbPtbCO1BYTE0P37t2LPEdBURVUa0pKCtOnT2fcuHH5BnRJfa4rVsDvfufmTZ85UyEupU9BLidkrWXAgAE0atSIhx9++Gh7165dj45CL2ga106dOjFnzhx27tzJzp07mTNnDp06Fbgyrq+17ty5k4MHDwKwfft2FixYwMWlONVWQbUWRsuWLVm1ahXr1q3j0KFDjB8/vlRH2Z9Krfv27WPPnj1H78+ZM4cmTZqURplAwbXOmjWLYcOGMW3aNCpUqJDvviXxuX71FbRpA4cOuSQkJLMAABeISURBVNnarrnmlN6OSOHkNwIunG8atV40X375pQXsJZdcYps2bWqbNm1qZ8yYYbdv327btWtnL7jgAtuuXTu7Y8cOa621ixcvtgMGDDi6/+jRo22DBg1sgwYN7JgxY4K21gULFtgmTZrYuLg426RJEztq1Chfap08ebKtUaOGLVu2rI2JibEdO3a01lq7adMm27lz56P7z5gxw8bGxtr69evb5557LmhrXbNmjY2Li7NxcXH24osv9q3WBg0a2Jo1ax5tu+uuu46r1dpT+1w//tja8uWtjY21du3aEn1bItbagketa2Y3EZFT9MYb8NBD0KoV/POfcN55flck4Ugzu4mIlLDcXBg8GB58ELp1g3nzFOISeBq1LiJSDAcOwG23wYQJbsa24cMhKsrvqiQSKchFRIpoxw7XA1+wAF58Ef70J10jLv5RkIuIFMHq1W4d8Q0bYOJEuOUWvyuSSOfbOXJjTJQxZqkxZrr3uJ4x5htjzCpjzARjTFmvvZz3eLX3fN08r/Fnr/2/xpjSu65JRAT4+mt3jXhmpjsfrhCXYODnYLeHgBV5Hg8DXrXWxgI7gQFe+wBgp7X2AuBVbzuMMRcDvYHGwLXA28YYnaESkVIxYQJcfTVUquQCvU0bvysScXwJcmNMTeB6YJT32ADtgEneJinAjd79bt5jvOfbe9t3A8Zbaw9aa9cBq4FLA/MORCRSWAvPPw+9e0PLli7EY2P9rkrkV371yF8DHgFyvcfnAlnW2sPe43Sghne/BrARwHt+l7f90fZ89hEJaYsXLyYuLo4DBw6wb98+GjduzA8//OB3WRHn0CG4/XZ44gno1w/mztXlZRJ8Aj7YzRjTBdhmrV1ijGl7pDmfTe1JnjvRPsf+ziQgCaB27dpFqlfEDy1btqRr16488cQT7N+/n379+pXq1KZyvB07oEcP+Owz+Otf4cknNTJdgpMfo9bbAF2NMdcB5YGzcD30SsaYaK/XXRPY7G2fDtQC0o0x0cDZQGae9iPy7vMb1tpkIBnczG4l/o5ESsGTTz5Jy5YtKV++PK+//rrf5USUlSuhSxdIT9fqZRL8An5o3Vr7Z2ttTWttXdxgtVRrbV9gPtDD2ywRmOrdn+Y9xns+1ZtzdhrQ2xvVXg+IBUp3WSWRAMrMzGTv3r3s2bOHAwcO+F1OxJg7F1q3hj17YP58hbgEv2CaovVR4GFjzGrcOfDRXvto4Fyv/WFgCIC1djkwEfgRmAXcZ63NCXjVIqUkKSmJZ599lr59+/Loo4/6XU5EGDECrr0WatWCRYvcpWYiwc7XCWGstZ8Bn3n315LPqHNr7QEg36s1rbXPA8+XXoUi/njvvfeIjo7m1ltvJScnh8suu4zU1FTatWvnd2lhKTsbBg6Et9+G66+HDz6As87yuyqRwtHqZyIS0TIzoWdPN8HL4MEwdKjmTJfgVNDqZ5qiVUQi1sqVcMMNbrrVsWMhMfGku4gEHQW5iESkmTOhTx8oX94NarvsMr8rEimeYBrsJiJS6qyFYcPc5WUNGsDixQpxCW3qkYtIxPjlF7jzTvjwQ+jVC8aMgQoV/K5K5NSoRy4iEeGnn+Dyy2H8ePi//3NhrhCXcKAeuYiEvfnz3cj0Q4fgn/90l5iJhAv1yEUkbFkLr70GHTpAlSrufLhCXMKNglxEwtIvv8Dvfw+DBrlLzBYuhAsv9LsqkZKnIBeRsLN2rRuJ/sEH8Mwz8PHHmqlNwpfOkYtIWPnXv9xCJ8bAjBnQubPfFYmULvXIRSQs5Oa63vf110OdOpCWphCXyKAeuYiEvMxM6NfP9cb79YN33tGlZRI5FOQiEtKWLIGbb4YtW9wypHfd5Q6ri0QKHVoXkZBkLSQnQ5s27rD6l1/C3XcrxCXyKMhFJOTs3Qv9+7ve91VXwbffwqWX+l2ViD8U5CISUn780YX2uHFucNu//gXnned3VSL+0TlyEQkZ//iH64VXrAiffgrt2/tdkYj/1CMXkaD3yy9wxx1uprYWLWDpUoW4yBEKchEJaj/+CC1bwtix8MQTkJoK1av7XZVI8NChdREJSta68L7/fjjjDJg92y1+IiK/pR65iASd3bvdxC533OEGti1bphAXKYiCXESCSloaNG8O48fDs8/C3Lk6lC5yIgpyEQkKubnwyitu1bJDh+Dzz9058agovysTCW46Ry4ivvv5Z0hMhDlz4MYbYfRoOOccv6sSCQ3qkYuIr2bOhLg4N8XqyJEwebJCXKQoFOQi4ov9++HBB92yo9WquXPjWvBEpOgU5CIScMuWQUICvPEGDBwI33wDF1/sd1UioUlBLiIBk5sLf/sbtGoFO3e6a8NffRXKl/e7MpHQpcFuIhIQGzbAbbfB/PnQvbtbglSLnYicOvXIRaRUWQvvvw+XXAKLF7sR6R9/rBAXKSkKchEpNdu3Q8+ebu3wuDj4z3/cbG0a0CZSchTkIlIqpk2Dxo1h6lQYNgw++wzq1fO7KpHwo3PkIlKisrLcSPSUFIiPd+uGx8X5XZVI+FKPXERKzJw5LrT/8Q/4y1/cZWUKcZHSpR65iJyy3bvhj3+EUaOgUSP4+mu3hriIlD71yEXklMyZA02awJgx8Oij8O23CnGRQFKPXESKZedO1wt/913XC//qKzfRi4gElnrkIlJkU6e6EenvvQd//rPrhSvERfyhHrmIFNq2bW6hkwkToGlTmD4dmjf3uyqRyKYeuYiclLXucrJGjeCTT+CZZ9wsbQpxEf+pRy4iJ7R2rVtedO5caNMG/v53F+giEhzUIxeRfGVnw4svuhHp33wDI0bAF18oxEWCjXrkInKchQshKQm+/96tVPb661Czpt9ViUh+1CMXkaOysuDee+Gyy9zlZVOmwOTJCnGRYBbwIDfG1DLGzDfGrDDGLDfGPOS1n2OM+dQYs8r7WdlrN8aY140xq40x/zHGNM/zWone9quMMYmBfi8i4cJaN63qRRfBO++4kek//gjduvldmYicjB898sPAH621jYDWwH3GmIuBIcA8a20sMM97DNAZiPVuScAIcMEPPAW0Ai4FnjoS/iJSeCtXQvv28PvfQ926kJYGr70GZ57pd2UiUhgBD3Jr7RZr7bfe/T3ACqAG0A1I8TZLAW707ncD3rPOQqCSMaYa0An41Fqbaa3dCXwKXBvAtyIS0vbuhSFD3KImS5fCyJFudrZmzfyuTESKwtfBbsaYukAz4BugqrV2C7iwN8bEeJvVADbm2S3dayuoPb/fk4TrzVO7du2SewMiIchamDQJHn4Y0tPhttvceuExMSfdVUSCkG+D3YwxFYGPgYHW2t0n2jSfNnuC9uMbrU221iZYaxOqVKlS9GJFwsSPP0LHjtCzJ5x3HixY4OZKV4iLhC5fgtwYcxouxMdZayd7zVu9Q+Z4P7d57elArTy71wQ2n6BdRI6RlQWDBrnD6Glp8MYb7udll/ldmYicKj9GrRtgNLDCWvtKnqemAUdGnicCU/O09/dGr7cGdnmH4GcDHY0xlb1Bbh29NhHx5OTA6NFw4YUwfDjceSf8739w//0QFeV3dSJSEvw4R94G+D3wvTFmmdf2GPACMNEYMwDYANziPTcTuA5YDfwC3A5grc00xjwLLPa2e8ZamxmYtyAS/L74AgYOdAPZ2rSB2bM1kE0kHAU8yK21/yb/89sA7fPZ3gL3FfBaY4AxJVedSOhbtw4eecQNaKtVCz78EHr1AlPQvzoRCWma2U0kTGRluQBv2BBmznQrlP33v9C7t0JcJJxprnWREJed7WZj++tfITMT+veH55+HGvlejCki4UY9cpEQZa2bC/2SS+CBB9yI9CVLYOxYhbhIJFGQi4Sgr76Cyy93K5MZA1Onwrx5GswmEokU5CIhZMUKuOkmNwp97Vp3SP3776FrV50HF4lUCnKRELBxIwwYAE2awNy58PTTsHq1WzM8WiNdRCKa/hcgEsQyMuCFF+Ctt9w58Ycegj//GTTTsIgcoSAXCUJZWfDyy2450f373Uj0p58GrfkjIsdSkIsEkT173DzoL73kwrxXL3dZWcOGflcmIsFKQS4SBPbuhTffdL3wHTvghhvg2WehaVO/KxORYKcgF/HR3r3w9tuuB759O1x3neuBt2zpd2UiEioU5CI+2L3b9cBfecX1wDt1cufAW7XyuzIRCTUKcpEAysx058Bfe82dA7/+evjLXxTgIlJ8CnKRAPj5Z9f7HjHCHU7v1s0FeIsWflcmIqFOQS5Sitasgb/9DcaMcYub9O4NQ4a4+dFFREqCglykFCxZAi++6NYEj45214E/+ihccIHflYlIuFGQi5QQa2H2bHcJ2bx5cNZZ8Kc/wcCBUK2a39WJSLhSkIucooMH4YMP3CH05cuhenU3rerdd8PZZ/tdnYiEOwW5SDFt2wYjR7rrwLdudeuBp6S48+Bly/pdnYhECgW5SBEtWwbDh7te+KFDbhKXgQPhmmu0lKiIBJ6CXKQQsrNh8mR3DfiCBVChAtx5Jzz4IFx0kd/ViUgkU5CLnMDmzfD3v8M778CWLVC/vjsXfvvtULmy39WJiCjIRY5jLaSmuslbpkyBnBw3hWpyMnTuDFFRflcoIvIrBbmIZ9s2GDsWRo2CVavg3HPh4YfhrrugQQO/qxMRyZ+CXCJaTo675nvUKNf7zs6GK65w06fecguUL+93hSIiJ6Ygl4i0dq3rfY8dCxs3wjnnwP33wx/+AI0a+V2diEjhKcglYuzZ46ZMTUmBzz93l4p17OgGr3XtCuXK+V2hiEjRKcglrB0+7Aauvf++u3zsl1/cfOfPPAO33Qa1avldoYjIqVGQS9ix1i1aMm4cjB/vlhA9+2zo1w8SE+F3v9PELSISPhTkEjaWL3fBPWGCG3Vetixcfz307et+auCaiIQjBbmEtBUr3HnviRPhhx+gTBm4+moYPBh69NCkLSIS/hTkElKsdYE9eTJ89JHrhRsDl13mpk/t0QPOP9/vKkVEAkdBLkEvJwcWLoRPPnHXeq9Z48L7iitceN90k1s6VEQkEinIJSjt2weffgrTpsH06ZCRAaedBu3bwyOPuMvF1PMWEVGQSxBZvRpmznS3zz6DgwehUiU3v/kNN7jlQs8+2+8qRUSCi4JcfLN3rwvsOXNg1iw30hzcsqD33ON63Zdf7nriIiKSPwW5BMzhw+767nnzYO5c+Pe/3dzmFSrAVVe5tb07d9YCJSIiRaEgl1KTkwP/+Y/rdR+57d7tnmvaFAYOdMuDXn65pkcVESkuBbmUmEOHXI/73/+GL790t6ws91yDBtCrF1xzjbvOu0oVf2sVEQkXCnIptm3b3GVhX3/tbt98AwcOuOdiY9013W3busPmNWv6WqqISNhSkEuh/PILfPstLF4Mixa529q17rnoaGjWzA1Qu/xyaNMGqlb1t14RkUihIJfj7NwJ330HS5e68P72W1i5EnJz3fO1akHLlnD33W4BkhYt4PTT/a1ZRCRSKcgj2IEDLqB//NFNdfrdd25w2saNv25TvTo0bw433+zCu2VLTcQiIhJMFORhzlrYutVNtvLf/7rgPvJzzZpfe9nR0dCwoZv2tGlTiIuD+HiFtohIsAv5IDfGXAsMB6KAUdbaF3wuKeD27HG96PXrYd26X29r1rgA37fv123LlXMD0eLioHdvaNzY3S680C37KSIioSWkg9wYEwW8BXQA0oHFxphp1tof/a3s1FnrZj7butWNDt+6FX7+GTZvhi1b3M/0dNiwAXbt+u2+5cpB3brukq+rrnLB3aCBmzGtTh2IivLlLYmISCkI6SAHLgVWW2vXAhhjxgPdgFIP8kOH3Kxkubm/3nJyXFt2tnv+0CHYv9+di96/34383rv319vu3S6Es7Lcz8xMd9uxw90OHjz+95Yp40aEV68O9eq5oK5Vy93q1HFtVau67UREJPyFepDXAPIMzSIdaBWIX/z44/Dyy6f+Omee6RYCqVQJKld2PeeWLeHcc+G881woV60KMTG/3o8O9f9qIiJSYkI9Ekw+bfa4jYxJApIAateuXSK/+PrrXbiWKePWxi5Txh2yPu00dytb1t1OPx3Kl3c/Tz/dBXfFiu52xhkKZREROTWhHiPpQK08j2sCm4/dyFqbDCQDJCQkHBf0xdG2rbuJiIj4KdTPpC4GYo0x9YwxZYHewDSfaxIREQmYkO6RW2sPG2PuB2bjLj8bY61d7nNZIiIiARPSQQ5grZ0JzPS7DhERET+E+qF1ERGRiKYgFxERCWEKchERkRCmIBcREQlhCnIREZEQpiAXEREJYQpyERGREKYgFxERCWHG2hKZejxkGGMygJ9K6OXOA7aX0GtFIn1+xafPrvj02Z0afX7Fd6qfXR1rbZVjGyMuyEuSMSbNWpvgdx2hSp9f8emzKz59dqdGn1/xldZnp0PrIiIiIUxBLiIiEsIU5Kcm2e8CQpw+v+LTZ1d8+uxOjT6/4iuVz07nyEVEREKYeuQiIiIhTEFeTMaYa40x/zXGrDbGDPG7nmBmjKlljJlvjFlhjFlujHnIaz/HGPOpMWaV97Oy37UGK2NMlDFmqTFmuve4njHmG++zm2CMKet3jcHKGFPJGDPJGLPS+w7+Tt+9wjHGDPL+zf5gjPnQGFNe372CGWPGGGO2GWN+yNOW73fNOK97GfIfY0zz4v5eBXkxGGOigLeAzsDFQB9jzMX+VhXUDgN/tNY2AloD93mf1xBgnrU2FpjnPZb8PQSsyPN4GPCq99ntBAb4UlVoGA7MstY2BJriPkd9907CGFMDeBBIsNY2AaKA3ui7dyJjgWuPaSvou9YZiPVuScCI4v5SBXnxXAqsttautdYeAsYD3XyuKWhZa7dYa7/17u/B/Y+0Bu4zS/E2SwFu9KfC4GaMqQlcD4zyHhugHTDJ20SfXQGMMWcBVwKjAay1h6y1Wei7V1jRwOnGmGigArAFffcKZK39Asg8prmg71o34D3rLAQqGWOqFef3KsiLpwawMc/jdK9NTsIYUxdoBnwDVLXWbgEX9kCMf5UFtdeAR4Bc7/G5QJa19rD3WN+/gtUHMoB3vVMTo4wxZ6Dv3klZazcBLwMbcAG+C1iCvntFVdB3rcRyREFePCafNg3/PwljTEXgY2CgtXa33/WEAmNMF2CbtXZJ3uZ8NtX3L3/RQHNghLW2GbAPHUYvFO9cbjegHlAdOAN3OPhY+u4VT4n9O1aQF086UCvP45rAZp9qCQnGmNNwIT7OWjvZa9565FCS93ObX/UFsTZAV2PMetwpnHa4Hnol73An6Pt3IulAurX2G+/xJFyw67t3ctcA66y1GdbabGAycBn67hVVQd+1EssRBXnxLAZivdGbZXEDQKb5XFPQ8s7pjgZWWGtfyfPUNCDRu58ITA10bcHOWvtna21Na21d3Pcs1VrbF5gP9PA202dXAGvtz8BGY8xFXlN74Ef03SuMDUBrY0wF79/wkc9O372iKei7Ng3o741ebw3sOnIIvqg0IUwxGWOuw/WMooAx1trnfS4paBljLge+BL7n1/O8j+HOk08EauP+p3GLtfbYgSLiMca0Bf5kre1ijKmP66GfAywF+llrD/pZX7AyxsTjBgqWBdYCt+M6MfrunYQx5mmgF+7Kk6XAnbjzuPru5cMY8yHQFrfK2VbgKWAK+XzXvD+O3sSNcv8FuN1am1as36sgFxERCV06tC4iIhLCFOQiIiIhTEEuIiISwhTkIiIiIUxBLiIiEsIU5CJSbN7KduuMMed4jyt7j+v4XZtIpFCQi0ixWWs34lZtesFregFIttb+5F9VIpFF15GLyCnxpt9dAowB/gA081YFFJEAiD75JiIiBbPWZhtjBgOzgI4KcZHA0qF1ESkJnXFLXTbxuxCRSKMgF5FT4s1l3gFoDQw6stKTiASGglxEis1b+GEEbo35DcBLwMv+ViUSWRTkInIq/gBssNZ+6j1+G2hojLnKx5pEIopGrYuIiIQw9chFRERCmIJcREQkhCnIRUREQpiCXEREJIQpyEVEREKYglxERCSEKchFRERCmIJcREQkhP0/acDdkdFK488AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercice 4\n",
    "\n",
    "**Utiliser plt.subplots(nrows=1, ncols=2) pour créer le tracé ci-dessous.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/iJ8BVoGvA6/cprr/Avw38I3pn+Xteszr1o75i7HZYw7wt8Bp4FvAoW38Oe8DvjL9pfkG8Acj1PwU8D3gZ0zOeO4E3ga8bebxHp329K2x/p0XOdeLnO1FzfUiZ3sRcz3P2farFSSpCa+0laQmDHxJasLAl6QmDHxJasLAl6QmDHxJasLAl6Qm/g9yAKZwvIhC+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tracer maintenant (x,y) et (x,z) sur les axes. Jouer avec la largeur et le style de la ligne**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Voyez si vous pouvez redimensionner le tracé en ajoutant l'argument figsize() dans plt.subplots() et copier et coller votre code précédent.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'z')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bon travail!"
   ]
  }
 ],
 "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}