004_comission/Man1130/jupyter/Man1130-python-comission/course_materials/Note/CH4F_Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CH4A - Activity 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: ylabel='Frequency'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "x  = np.random.normal(0,1,10000)\n",
    "df = pd.DataFrame(x,columns=['x1'])\n",
    "df['x1'].plot.hist(bins=25,color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CH4A - Activity 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c92cb32940>"
      ]
     },
     "execution_count": 5,
     "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": [
    "u  = np.random.normal(0,1,10000)\n",
    "y  = 2*x+u\n",
    "\n",
    "df = pd.DataFrame({'x1':x,'y1':y})\n",
    "df.plot.scatter(x='x1',y='y1')\n",
    "\n",
    "p   = np.linspace(-4,4,101)\n",
    "pp  = 2*p\n",
    "\n",
    "df1 = pd.DataFrame(pp,index=p,columns=['y2'])\n",
    "df1['y2'].plot.line(color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CH4B - Activity 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.linspace(0,5,num=11)\n",
    "y = x**2\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x162be16add8>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(x,y,'r+-')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(x,y,'b*-')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CH4B - Activity 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'title2')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.linspace(0,5,11)\n",
    "y = x**2\n",
    "\n",
    "fig, axes = plt.subplots(1,2)\n",
    "\n",
    "# Now use the axes object to add stuff to plot\n",
    "axes[0].plot(x, y, 'r')\n",
    "axes[0].set_xlabel('x')\n",
    "axes[0].set_ylabel('y')\n",
    "axes[0].set_title('title1')\n",
    "\n",
    "axes[1].plot(y, x, 'b')\n",
    "axes[1].set_xlabel('y')\n",
    "axes[1].set_ylabel('x')\n",
    "axes[1].set_title('title2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CH4C - Activity 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BBx7YMXr06Pbjxo1rPmTIkIOBeQcOK1FujAlbVqK86qxEuTHGmGqzZGGMMaZCNmcRpkSIAJoCzUvdWvj/bAbUB2KAaP+fkYACRf5bIZAL7Cl1213i523AZlVsPNOYMGbJIgyI0ALoDqSXuHWi5v59j4iwBlgNrCpxsyRiTJiwZBFiREgEfgGcxY+JoYmrQUFdoI//VtJhEVYDc4GZQKYqh2o6OGPM6bNk4XEiRAEDgAv8tz6EzlxTAtDPf7sXKBBhCfA1TvKYq8oxF+MzxlRSqHzo1CoitBfhLhE+APYBs4GHcD50Q/nfLAroDzwIfA7sFyFThP8WIdnd0IwJjvj4+J4l748bN67RqFGj2rgVT3VZz8IjRGgIjABGAQNdDqemxABD/bcnRZgDvA68o8o+VyMzYekReSSgJcozNKPCEuWnq3Tp8sqWMg90yXNLFi7yDzFdjJMgLgMCVk44BEUAw/y350X4DCdxfKDKUVcjMyZI1q9fH3PjjTe227t3b1SjRo0KpkyZsqljx44nrrrqqnaJiYkFK1asiO/evfvRevXqFeXk5ERv2bIlJikpqWDatGmbRo0a1TYrKys+MjKSJ598cutll112aNy4cY0++eSTBnl5eRFHjx6NWLBgwfpAxWrJwgUidAduAa7F/clpL4oGLvXfjojwOvC/qqx1Nyxjqq643Efx/QMHDkSef/75BwDuuOOONiNHjtx7991373322Wcb3Xnnna2//PLLjQAbN26sM2/evPVRUVHce++9LbOysuIXLly4NiEhQTMyMpoBrF+/fvWyZcvqXHLJJR03bty4En5a8jyQ7yOUx79Djgjn+L8xLwfuwRJFZdQFbgVWizBDhHPcDsiYqijeKa/49uCDD57cIW/ZsmV1b7vttn0Ad955574lS5YkFD935ZVX5kZF/fh9/qKLLtqfkJCgAPPnz08YNWrUXoCePXseb9my5YkVK1bUgZ+WPA8kSxZBJoKIcIUIC3BWAV3gdkwhSoD/Ar4WYYkII/3DeMaEpYSEhKKS9+vWrXvyfnk1/UqWPA8kSxZBIkK0CDfjXKj2Hs4qIBMYvYDXgO9FuE+EeLcDMqY6evbseWTixImJ4Oxl0adPn8OVed3gwYMPv/rqq0kAWVlZsTk5OTHdu3evUoXaqgpashCR5iLypohsFJHVIvJvEUkJ1vlKnXuTiAS0lnvlz02kCLcD3wOTgM5uxFFLtAb+AWwQ4Tf+EifGhIzx48dvmTp1auOUlJTUN954o9GLL764tTKvu//++3cVFhZKSkpK6ogRIzpMmDBhU1xcXFCrJQSlRLmICDAfeEVVX/I/1gOop6pzAn7Cn59/E9BHVWu0NLEIZwNjcUpvmJq3HLhPlS/dDsR4g5Uor7qaLlF+DpBfnCgAVPVbYK6I/ENEVorIChEZASAiZ4tIpoi8JSLrReTvInKdiHzjb9fB366JiLwrIov8t0H+xxuJyOciskxEJuCMbyMij4qIrzgGEXlcRO4J9JsVoZ0I7+JclWyJwj3pwBcifCxCaoWtjTGVFqxk0Q0o62KVK4EeOL/U5wH/EJEW/ufSAR+QBtwApKhqP2AicLe/zVjgGVXtC1zlfw4gA5irqj2BD4HiqyP/D7gRQEQigGtwxroDQoS6IjwOrPG/N+MNlwBZIrwkQlO3gzEmHNT0apLBwBuqWgjsFJFMoC9wEFikqjkAIrIRpxwEwAo4uVzyPCDVGeUCoL6I1MO5AvhKAFX9WERy/T9vEpG9ItITpyT3MlXdG4g3IsJInPHyloE4ngm4SOB24GoR7lLlDbcDMiaUBStZrAJ+VcbjUsZjxfJK/FxU4n4RP8YZAQxQ1Z8Un/Mnj1NNvkwEbsLZw2FSeUFXhgiNgH9iPYlQkQS8LsJVwJ2q7HY7IGNCUbCGob4GYkXk1uIHRKQvzkY6I0QkUkSa4PQIvqnCcT8H7ipxzB7+H2cD1/kfuxhILPGa94GLcHown1X9rfxIhItwejqWKELPVcBKEa5wOxBjQlFQkoU6S6yuAM73L51dBTyMU+snC2fVytfA/aq6owqHvgfoIyJZIrIauMP/+CPAUBFZinPR25YSsZzAmXh+yz/8VWUixInwAvAJzk5zJjQ1Bd4T4VX/viDGmEoKytJZL/FPbC8FrlbVDVV/PX2AV3F2njPhIxu4RZVP3Q7EBI8Xls5u3Lgx+rbbbmvz3XffxRUVFXHeeecdGD9+/LalS5fW2bp1a8yIESMOANx7770tExISCv/yl7/sdDPeUy2dDetyCSKSCswA3q9qohBBcPZdeBinsJ0JLy2Bf4vwGPCwKkEpkWC8RgJaohy03BLlRUVFXH755WeOHj16l8/n21hQUMDIkSPb+ny+5K5dux5bvHhx3eJkcboKCgooWUsq0ML6ildVXa2qZ6jqf1fldSLUw5nreBxLFOFMgD8BH/n3EzEmoD766KN6sbGxRT6fby9AVFQUL7300tY33nij8cMPP9zqo48+SuzcuXPqyy+/nAiwZs2auH79+nVq1apV2mOPPXZy2feLL76YlJaW1qVz586pI0eObFtQUAA4Gyv97ne/a9m9e/fOX331VUKZQQRIWCeL6hChA/Af4Jdux2JqzCX1OTAdka5uB2LCy4oVK+LS09N/sh9LUlJSUXJy8ol7770357LLLstdu3bt6ltvvTUX4LvvvquTmZm5ftGiRWueeuqplnl5ebJ06dI677zzTtLixYvXrl27dnVERIS+9NJLjQCOHTsW0a1bt2NZWVlrL7zwwkrVlaqusB6Gqip/+et3wSY/axc9OJuhrYH5iPwa1dNaNWdMMVVFRH42Mex//GftL7jggv1xcXEaFxdXkJSUlL9t27aoTz/9tN7KlSvj09PTuwAcP348omnTpgUAkZGR3HTTTblBfyNYsjhJhJtwrp+wYadaRYsm8Zu16WT18z/wMSK/R/U5V8MyYSEtLe3YBx988JMvn/v27YvYsWNHTGRk5M+SSGxs7MnHIiMjKSgoEFWVq6++eu8LL7ywvXT7mJiYomDOU5Rkw1CACI8Ck7FEUevcwNQ5N/OvfiUeigTGIfI8IpFuxWXCw/Dhww8dP3484vnnn28EziT0mDFjWl999dV7mjdvnn/48OEKP4MvuuiigzNmzEjcvn17FMDOnTsj169fHxPs2Eur1clChAgRJgEPuR2LqXkdWf+fKdw47BRP/xZ4ExHrfZtqi4iIYPr06d+99957iW3btu3Wvn37brGxsUXjxo3bfvHFFx9av359XMkJ7rL07t37+EMPPbT9F7/4RUpKSkrqueeem7J169Ya/2Ib9tdZnIp/74NJ+AsNmtolniPrdtG0dV2OVrRx0jTgOqp5Qadxlxeuswg1NV2i3NP8iWIylihqJaFobxbd61YiUQCMAF7BubjTmFqr1v0ClEgUo9yOxbhBC6Zz+dYOfN+qCi+6DphsCcPUZrXqP78/UfwLSxS11r387/zhfNSj4pY/Mwp4ucz1jsbUArUmWZRIFDe4HIpxSR8WzXma+4aexiF+A0ywhGFqo1qTLHCuobBEUUs1JHfFXAb3D8ChbgVeCMBxjAkptSJZiPAAcIvbcRh3RFKQs5JuzWI5Eai16XciMi5AxzImJIR9svBvdvM3t+MwbtHjX/GL3GSyA70X992I3BvgY5ow9MADDzQ/88wzu6akpKR27tw59euvv65bVrvZs2fH33TTTa3Leq5fv36d2rVr161z586pnTt3Tp08eXIiQM+ePTsHM/aSwvqCIxF64exFYWPMtdTfeHDJMGYPCtLhn0BkKaqzgnR8E2Bjc8cGtES5L9FXbonyL7/8su5nn33WcMWKFavj4uI0JycnKi8vr8zPo6FDhx4dOnTo0bKeA5gyZcr3pZ9ftmzZ2upFXnVh27MQoSXwIVCZtfQmDJ3P55l/4IlgJQpwvmxNQ6Qqy3BNLbJ9+/bopKSkgri4OAVo0aJFQbt27fIzMzPje/bs2blTp06paWlpXXJzcyNmzJhR75xzzjmzKsePj4/vGZzIfy4sk4UI8TiJItntWIw7mpOz5N9cMrgGTtUUeAeRGq/VY7zv8ssvP5idnR3Trl27btdff32bjz/+OOH48eNy3XXXdXj22We3rFu3bnVmZua6hISECjffGjVq1BnFw1A7duyo8bplYTcM5d/hbioQ4B2xTKiI5sTmlXTrEEVhTf1C9QfG8eOe8MYA0KBBg6KVK1eu/vTTT+t99dVX9W688cYOv//973OaNm2aP2zYsKPg7G9RmWOVNQxVk8IuWQC/B650OwjjFj20kP4FjdhX0zvf3Y7IN6hOquHzGo+Liori0ksvPXTppZce6t69+7GXXnqpSVl7XJQ0ePDgjnv27IlOT08/Mm3atM01FWt5wipZiNAd+KvbcRi3qE5k9JqefNuv4rZB8QIiy9Hy92U2tcfy5ctjIyIiSEtLywNYtmxZXMeOHY/PmjWrQWZmZvywYcOO5ubmRpQehpo7d+4GdyI+tbBJFiLUAV4DYt2OxbjjWt6YfQuTTlVyvCbUAd5DpDeqVunUcPDgwch77rmnzcGDByMjIyO1Xbt2ea+88srm9evX77nnnnvaHD9+PKJOnTpFs2fPXu92rBUJmxLlIjwL+NyOw7jjDDYu2EDH/hGoF5ZJfwFcSLj8coUwK1FedWFdolyEC4B73I7DuCOOoxuWk57mkUQBcD5OHSljwkbIJwsRGuEUCPTKB4WpQULRvm/pUSeBI2VeFeuiJxBp7HYQxgRKyCcLnAKBLdwOwrhBC97hV5tT2FBmiQSXNQKecjsIYwIlpJOFCJdjy2Rrrbt5bv6VvF9jV7BWw42InO12ELVcUVFRkY06VJL/76rM6z5CNln4Vz/9r9txGHf0YNnccfhOZ2+KmvKSXd3tqpW7d+9uYAmjYkVFRbJ79+4GwMqyng/lpbP3Ae3dDsLUvAbsX/kfBvR1O45K6gQ8ADxa3QOIyBXAe0AXVT1l4TgRuQn4XFWz/fcnAv+rqqure+5QV1BQMHrHjh0Td+zY0Y0Q/nJcQ4qAlQUFBaPLejIkl86K0ApYhxUJrHUiKdixiXbSiu3N3I6lCo4Daah+V50Xi8hbOPNyX6nqw+W0mwXcp6qLq3MeY8oTqpn2KSxR1EKa9wXn7w2xRAHOxXrjq/NCEUkABuFs3nVNicfvF5EVIrJcRP4uIr8C+gCvici3IhInIrNEpI+//bX+9itF5IkSxzksIo/7j7NARELt79bUkJBLFiIMBUa4HYepeY/x0KJzmNXV7Tiq6TxERlbjdZcDn6rqemCfiPQSkYv9j/dX1XTgSVV9B1gMXKeqPVT1WPEBRKQl8ARwLtAD6Csil/ufrgss8B9nNs62scb8TEglCxEicap7mlrmHL7O/H/8tSZKjgfTk4hUtRzNtcCb/p/f9N8/D5isqkcBVHVfBcfoC8xS1d2qWoBTFqd4ccAJYIb/5yVAuyrGZ2qJUJvgHgWkux2EqVlN2bn0cy4I9UQBzv4qtwLPV6axiDTC6Q1081cpjQQUeNf/Z2WVtxIoX3+cuCwk9D4TTA0JmZ6FCBHAH9yOw9SsaE5sWUXX9jW4N0Ww/aEKvYtfAVNUta2qtlPV1sAPwD7gNyISDyAiSf72h4B6ZRxnITBMRBqLSCRO7yTztN6FqXVCJlng/OKkuB2EqUl6eD4D8xqzN9HtSAKouHdRGdcC75d67F04uWXwYhH5FmcZOThlb14qnuAufoGq5gAPAjOB5cBSVf2g2u/A1Eohs3RWhGU4k3OmVlAdz50L72DCWW5HEgTbgQ6o5rkdiDGVFRI9CxEuxhJFrfIr3skM00QBTu/iRreDMKYqQqJnIcJsYIjbcZia0Y4fFm6kQz8PlRwPhnVAF9vzwoQKz/csRBiMJYpaI46jG7Lo3jXMEwU4ZUAuczsIYyrL88kCZ2LO1AJCUe4yesbW43CC27HUkPsqbmKMN3g6WYjQAbjE7ThMTdDCaYz4oRPr27gdSQ0agkh/t4MwpjI8nSywScBa407Gz72ad3q5HYcL7nA7AGMqw7MT3CIIzgVIbd2OxQRXGllzs0gPhyu0q+MA0MyW0Rqv83LP4hwsUYS9ehxctZD+fdyOw0UNsKFWEwK8nNR0bm0AAB9dSURBVCxsCCrMRVC4cxVdG8VxvI7bsbjsmoqbGOMuTyYLERKAq9yOwwST5n3Ghbtbs62525F4wKU4+1YY41meTBbA1Th19k2YepiHF53HV93cjsMj4oHhbgdhTHk8OcEtwixgmNtxmOAYwuzM2Qyzf9+fmoGqXaRnPMtzyUKEZkAO5dfgNyGqMbuXZdMyLZoC2zfhp04AzVHNdTsQY8rixWGoC7BEEZaiyN+2mtS2lijKFIPN0xkP82KyuNDtAEww6JF5DDrahD1JFbettWxVlPEsTyUL/4V4F7gdhwk01ee4O6sfi2zzqvKdg0gTt4MwpiyeShZAT8B+WcLM5UzPvIsXBrgdRwiIwCosG4/yWrKwIagw04bN37zLVbbyqfIsqRpP8lqyuMjtAEzg1OHYxhWkdakFe1ME0kC3AzCmLJ5JFiLUw75VhQ2haP9SekXV51A9t2MJMb0RiXE7CGNK80yyAIYC0W4HYQJBC19n5MYurLVCkFUXC/R2OwhjSvNSsujndgAmMEYzce41TLMPvOqzoSjjOV5KFrW5THXYSGXVvJe5zSa0T48lC+M5nin3IcIOoJnbcZjqS+DQml00bW8lx09bDqot3Q7CmJI80bMQIRlLFCEtgsLdK0hraIkiIFog0s7tIIwpyRPJAkh3OwBzOvTEx/xXTjs2t3A7kjBiQ1HGU7ySLGxfgxD2R/668CI+6+52HGHG5vCMp3glWXR1OwBTPQOYP/txHrISFYFny46Np1iyMNXWiD3fzuJsu5AyOFq7HYAxJXklWZzhdgCmaqLI37aKrq1iyLcLKYOjldsBGFOS68lChDpAottxmKrQo7MZeqQZuxq7HUkYa4aIJWLjGa4nC6C52wGYqnmW3y0fwIJObscR5iIAu9bCeIYXkoUttwwh/8WMTB/jbJ6iZti8hfEMSxam0lqxddEH/NJWPtUcSxbGMyxZmEqJ5fj3K+nWKZIiL/yfqS1sktt4hhd+8S1ZeJ4eWEwfacDB+m5HUstYz8J4hiULUwEtmsoNG7qxqr3bkdRC1rMwnuGFZGEFBD3sRl6Zcz2vWekJd9gXKeMZXkgWtoWkR3Vi7fx/cbPtTeEe+90wnuGFZBHpdgDm5+pyeM1SevV0O45azi7KM55hycL8jH9vigbxHItzO5ZazpKF8QxLFqYUzf+Q4dnt2WRXD7vPkoXxjCi3A8CShaf8nmfm/Bf/HgyccDsWgzf2PDYGSxam2ADW8j5HL585fTvX8ADwCmiu22EZY7zBhqFqu3Zkk8U85tGJZvT66L7LOh/cVm8kkA0yFWSw2yEaY9xnyaK2qs9BPiKT70kijUEIArC7Y9NjL3b9baeC45HZwPXAHJBVID4QKyVvTC0lqu4Oi4qwEOjnahC1SRQFPMk8fHQlgp/tR/GLP341Z8jf5g5p3Hn35jGrXmggETQs8fRx4G3gn6BzKzrV2NyxMUBfvDHcaWCTL9G32e0gTGjyQrL4BLjI1SBqi9tYyDiaEsspS3ec+cmGFddf8noaQOqvVi391Vtvp4uU2ftbBbwMTClvbmNs7thWwC3AaKx8hdv+7Ev0Pep2ECY0eWEYao/bAYS9s1nFXrKYQP/yEgXA9r7JJz/QV7/Ttdc3z/U7VQ+iK/AssB1kCsigshr5En3bfIm+R4B2wGXADKCwOm/DnLYCtwMwocsLyWKv2wGErY5sZS3z+ZpUkuhemZccaxyfWBQhJxP4p75LhmUvbjGnnJfEATcAc0FWgtwD0rB0I1+ir9CX6JvhS/RdBrQHHgG2Ve0NmdNkycJUmyWLcJTEfr4gk3U0oxMDiyevK+tYUtxPPsQnDbql/7F9dbIq8dKuwFiclVSvgAwsq5Ev0bfVl+h7GKe3MRz4GCiqSoymWixZmGqzZBFOYjjBeDLZDZzHMKR6hej2pjQ6WPJ+4YmomBdS72pRmB+xvZKHiANGAfNAVoDcXU5v4yNfou9SnMTxF6Cy5zBVl+d2ACZ0WbIIF79jPofYwR0M46crmKpsW//knz12ZGdCk8lDfnNYlSNVPFw3YBxOb+NfFfQ2MoC2wC+x3kYw7HI7ABO6LFmEuktYzgFW8QwDiaFNIA65ZXCbMnfE276wVadP7r44S7VaZSjigBtxehtZIHeBNCjdyN/b+NDf22iP9TYCaYfbAZjQ5YVksdvtAEJSV37gexbyMenUp2sgD729f/LPuxZ+i17oP2D1O6mZp3mKNOA5nN7GZJABZTXyJfq2lOhtXA78G+ttnI4ctwMwocsL11kkAAehapOwtVYT9vIuqxjMQCR4F7tlRDySK0rZV2yL6t0bxi1M6pB7VgBPmYVz3cZU0AOnajQ2d2wbnGs2bgGsMm7VJPgSfVUdRjQG8ECyABBhMwRmCCVs1eE4L7GQUfREKHOYKJAeSHpiRVzu8bRTPR9d98SR+3b+Y1tM3fxOAT71UeAtYALoglM1Gps7NhK4FLgduBBv9JK97LAv0VfP7SBM6PLKL9gatwPwLEF5kHkcYh83MqwmEgXAvg5J+8t7Pv9ITN3xaWMSigol0MOI8cBNwH9AloP8tpy5jQ98ib5LgDOAx4DsAMcSTmwIypwWSxZedhVLOcg6/sogomp2yGV735YVzg3s/yEx+Y3LRuaokh+kMLoDz+PMbUwCKXPYy5fo2+xL9P0JZ27jCuATbG6jNJvcNqfFK8litdsBeEpPvmMbi3mHXiTQ2Y0Qtgxpk1CZdt990rH7nMeHnnK4KEDigZtxehvfgowB+VkPy5foK/Al+qaX6m3YN2qH/T2Y0+KVZGE9C4CW7OIb5rCE9iTTx81Qtp3VutI9mZl/OnfIpsy2s4MZTwnpwAtADsj/gfQvq1GJ3kYb4ErgU2p3b8N6Fua0eGWCO4nafL1FXY4wiUVcTV+Eum6HUyxDHjkkUKlJUYksKrx3+9PLE5od6RXsuMqwHPgn8CrowVM1Gps7th3OSqrfAC1qJjTPuMeX6HvO7SBM6PJEz0KVfdTGq0sjKeRR5nCAw/yas72UKABO1IvZUtm2WhgR+WLqb88oPBHhxn4Jxb2NbJCJIGXuj+JL9G3yJfoe4sfexmfUnt7GCrcDMKHNE8nCb4nbAdSo61jMQb7nIYYQSTO3wylLbvvEcldElXZsX3zDl/vdVqDKKb/dB1ldnOsvFoIsA7mznLmN932JvouADsBfCf9hmkonCxH5fyKySkSyRORbkbKH+kSkj4iMO8Vzs0Rknf/134rIr/yPz69e+MZtnhiGAhDhAeDvbscRdGexjukcoRluDNdUyfBbPszsNWnZsKq+rvsNyxdd/sr7vUU88WXkCPAmznUbi07VaGzu2CicCri3A+cTXheJ5vgSfZWagxKRAcD/Amerap6INAZiVLVKy5JFZBZwn6ournK0xpO88MtcbJbbAQRVW3JYzjzm0zEUEgXAliFt4qvzuqyp6X2XTepZ3h4YNam4t/ENyFKQO0B+Ng/j722850v0XYjT2/gb4dPbqEx5+WItgD2qmgegqntUNVtE+orIfBFZLiLfiEg9ETlbRGZUJRAROVyV9sY7vJQslgCH3A4i4OpzkA+ZxQ80pDuD8Ma37UrZOqBVtYfHPhr9y2G7VjaZF8h4AqAnMB5nJdXLIH3LauRL9P3gS/T9EWdu41fAF1Ct4oleUZVv958DrUVkvYi8KCLDRCQGmAb4VDUdOA84VoljvVZiGKpRNeI2HuKZDy5VCoBTbeEZeqIo4Clmk8sJLuNshDi3Q6qqfWcmtdLKfSiU6eW+t/XOOxizKpAxBUhdnFVRFfU28n2Jvnd9ib4LgDNxhkl31nCsgXDK4bfSVPUw0Bu4DafI5zScobkcVWcYT1UPqmplNlK6TlV7+G+1d7VjmPBMsvCb5XYAAXEr33CYrfw3Q4mgsdvhVJdGRkTk142u9Iqo0gqOR9d5IfWuxkUF4uXhnOLeRjbIP0HKvL7Fl+j73pfoexBoTej1NhZWpbGqFqrqLFXNAO7CWTlW7nsVkc/8PYiJpxGn8TBLFoF0NqvYw3L+ST9iae92OIFwoE2D0/pGeGh7/WZTzrtxnyrHAxVTkCQAtwKLQJaA3B4mvY1tvkRfpZO1iHQSkY4lHuqBc9FsSxFn2M4/X/GTiseqeqG/BzE6IFEbz/FasgjNeYsObGMN8/maVBqR7nY4gZTdq8Vp133anNku9cv7zw+lpdG9gJf4sbfRu6xGpXobVwNf4r3eRlWXqiYAr4jIahHJAlKBPwMjgOdEZDlOr6pOYMM0XueZpbPFRPgAZwmj9yWxnzdYzvmchRDrdjjB0PulxQsuu/PjgOxbce2M1zJT/mtDlZfiesRSYALwOugpV/SMzR3bAaeHcjPQtIZiK8+NvkTfFLeDMKHPaz0LgLfdDqBCMZzgBTLZjXIBw8I1UQBsG9g6YBcMvjn82iEHttb/JlDHq2G9cJJFNsgEkDKXP/sSfRt9ib4/AK2AXwNf4V5vowinAq8xp82LPYsEnNIf3lw95OM/PElyoPa79rqI/MKCP8U8ViQQE4jjxdTLO3Tfzn/sio4r6BCI47lsCU4CeaOC3saZOL2Nm6jZ3sZCX6IvkLsZmlrMcz0LVQ4DH7sdx89cSBa5rORZBtSWRAFQFB0ZVVAnqtoroko7cSi23oSed0RpEbmBOqaLeuMUMMwGeQmkZ1mNfIm+73yJvgdw5jZGUHO9De/9HpmQ5blk4fem2wGclMomNrKQT+lOQ7q5HY4bDraqH9Dd8Paua9z2rV+N2OS/tiYc1MO5FmEpyCKQW0F+th+IL9F3wpfoe8uX6DsPSAGexLmWIVgsWZiA8Wqy+Bi3V0U1YS+ZzGYlyZxBmYXUaosd6c1OBPqYa9/v0nPBs2eFY1G5PlStt9EKuAb4msD2NrJ9ib6lATyeqeU8mSz8a/Knu3LyOhxnMpnsJIqhDEWIdiUOD9k6uE1A5itK+/zei4Zu/ybZKzWkAq10b2M0yM9K0Pt7G9N8ib5fAJ2AfxCY3sa/A3AMY07yZLLwq9mhKEH5A/M4yF5uYhhCgxo9v4dtGdQ6aFehTxr8m7OO7o1bHqzje0Qf4GWc3sZ4kB5lNfIl+jb4En3382NvYybV723YEJQJKM+thiomzjf67UCToJ/sCpYxhTok0CXo5wpBkXkFJx6q83ikQGQwjh/f5Mjee7c/fSwyuqhVMI7vUYtwVlK9CXrkVI3G5o7tiFOn6UYq/7twFGjmS/RZhVcTMJ7tWaiSj3MVbfD0YCNbWcR79LREcWqFsVExhTGRAVsRVdrR3XUbTRr0m6P+lXC1RV9gIvBIeY38vY3/weltXIvT26jIu5YoTKB5Nln4vQDkBfyoLdnFQuawlHa0oswy1eanDrdICOaqHbIXtUr5+M5LV6p6rlxGsL1cmUb+uY03fYm+c3HmNp4C9pyi+aRABWdMMU8nC1V2Aq8H7IDxHOUNZrGNePoxBAnOsEo42tm9WbVLlVfWkgl9zlo1rdvsYJ/HQ74GXVfVF/kSfetL9DZG8tMCnBuBzMCEZ8yPPDtnUUyEbpzuZvMRFJHBPP5IR6JoHpjIapdBf587//wHvxoY9BOJ6l3rnlvQqOO+AUE/l/suA63STnOnMjZ3bArO3MZmX6LvuUAc05iSPJ8sAET4HGdf5KobyWJepgHxdKy4sTmV1nO3rLllyOQamdeJjj9x9L6d/9gSk5DfuSbO55LloGWuijLGizw9DFXCM1V+RX/WkcMSXqOPJYrTt6Nn87ZaQwXx8o/GxI9PG1O/qFCCOk/isr+7HYAxVREqyeJTYHWlWrYlh2+Zy3/oSHPK3IfAVF1+3Zj4oqiI7TV1vv2bElu+fsl1O1QJ+NXjHvAdoVBd2ZgSQiJZ+FfIPFVuo3ocYjqZ/EBD0hmMhMZ7CyVHmtXNqcnzbfz8zLTMh88O1ZLm5XkStNDtIIypilD6QJ0CrP3Zo1EU8A9mk8txfskwxKOlzcPArq5Nj9b0OTP/cvbgH75uF06re7YBr7gdhDFVFTLJQpVC4KGfPDiabzjMVu5jKJE1cKV3Lbd1YCtXlhpPvWDU4EM5CaG0LWt5/gQajkNrJsyFTLIAUOVd4BuGsZo9LOdl+hFLe7fjqi22DGqT6MZ5tTAi8sXU355ZkBe5yY3zB9BynB6yMSEnpJIFAO9zLzPpQiPS3Q6ltsnu27K1W+c+vj+uwct9blNVDrgVQwDcB1rkdhDGVEfIJQu9nHmIS+XLa7m8BnXqF0XKDrfOv2tls/bvXX/lBlVC8QP3U9Av3Q7CmOoKuWThdx+E5ZJKzzvaOD7bzfOvfL17n6Uv9wq1PTAKcf7PGhOyQjJZKHxPdS7UM6dtd5cm7u5gCMy4ffiwnVlN57odRxWMB13ldhDGnI6QTBZ+jwAb3A6ittnWP9kT/2de7ndb3+P7Y1e6HUclbAUedDsIY06XJ37xq0PhGHALNVSCwji2DGnriR0EC/OiYl/s+tumRQVSoxcKVsOdoLa3hAl5IZssABTm4Ox5YWrItv7Jrq2IKu1Qdv2mr5xz035Vgl4+vZreBLXtTU1YCOlk4fcHYJPbQdQWxxrHJxZFeKfA35a5bbt8/t8XLHM7jjLsBe5xOwhjAiXkk4XCEWC023HUJscT67i6Iqq0Bc8MHLjug06z3I6jlN+BeiapGnO6Qj5ZACh8RSW3pzSnb29KI89dGPfmFdcM27+5gVeKDr4B+qrbQRgTSGGRLPzupaxCgybgtvVv5XYIP6ci49PGdMk/GuX2CrlNwJ0ux2BMwIVNslA4DFyJ86cJos1D2tR3O4aynDgUW++lHnfW0SL2uRRCPnAtqOd6XsacrrBJFgAKa4Bb3Y4j3G3vn9zS7RhOZd+GRq2nXXHNFlUKXDj9H0EXVKahiLQSkQ9EZIOIbBSRsSISIyI9ROSSEu0eFhG7+tu4LqySBYDCm8A4t+MIZ4eS6zdVYb/bcZzKug8795j/1MD5NXzaD4CnK9NQRAR4D5iuqh2BFCABeBzoAVxSzsurRERcKStvwk/YJQu/+4Ca/rCoVY43qLPV7RjK8+X9Fwzd+p9WNVVDKgu4HrSyF4ieCxxX1ckAqloI/B5nVd+TwAgR+VZERvjbp4rILBH5XkROLscVketF5Bt/2wnFiUFEDovIX0RkITAgQO/R1HJhmSzUGTv+NbDL7VjC1b4zEz3bsyj2r2E3n3V0T/y3QT7NLmB4Fa/S7gr8ZDMnVT2IMzn+GDBNVXuo6jT/052BC4F+QIaIRItIF2AEMEhVe+AUK7zO374usFJV+6tqKNXQMh4WlskCQGE7cAV49urekJbdN9nzZcKL8iOjX+jy2zaF+RHB6gXlAVeAbq7i64Syy9Sc6vGPVTVPVffgJKdmwC+A3sAiEfnWf/8Mf/tC4N0qxmRMucI2WQCoMxQ1AueXxwTQlsFtEtyOoTKO7qmb9H8DRh9XDcoqudtAqzPcuQroU/IBEakPtKbs/6t5JX4uBKJwEssr/h5ID1XtpKoP+9sc9w9tGRMwYZ0sABQ+Am53O45ws/WsVi3cjqGycpa07PjRbZetUg1o0cm/gFZ3i9SvgHgRGQUnJ6GfBv4F7ATqVfIYvxKRpv5jJIlI22rGY0yFwj5ZACj8H/CQ23GEk/1nJLZUcH1vi8paNrF3/xWvpWUG6HBjQTOq+2JVVZwh0qtFZAOwHjgO/BGYiTOhXXKCu6xjrMb5P/25iGQBXwAhk8BN6BGt9AKO0CfwHHCX23GEiwfr/2117KETqW7HURW/XfPc/Mad9w48jUNMAkZXYeWTMWGhVvQsSvAB0ypsZSolt32iW1dKV9uEXnf0zDsUs6aaL38LuNUShamNalWyUCgCrgesyFsA5PRuEXKTqAXHouPGp41pWFQoVV1W/W+cayk8vwrMmGCoVckCQKEAGAW85HYsoW7z4DbxbsdQHQc2N2zx6oU37FL9ySqj8ryLs0Q2P5hxGeNltS5ZAKgzjnAn8JTbsYSyrQNbN3M7hur64aszus380zmLKtF0EjAC9ESwYzLGy2plsiim8D9AtVe11Hb7Oia10hC+6HHO48MGb/zijPJWSD0NegvYNQvG1OpkAaDwF5y9MEwVaWRERH589Ba34zgdr118/eBD2QmLy3jqIVCr9mqMX61PFgAKz+Bc6X3U7VhCzYE2Dfa6HcPp0MKIyBdS7+pYkBf5g/+hfOAW0MfdjMsYr7Fk4afOsshBQFXr/NRqOb1ahPykb96BOg3+2ft2igrlB+B80Elux2SM11iyKEHhW6AvMNvtWELFlsGt67gdQyDsXtX0yLgzfL8ADdRV3saEFUsWpSjsBs4DxrsdSyjYOrBNE7djCIB3gLN+t/mZHypsaUwtFeV2AF7k3w9jjMAy4HkgxuWQPGt3auM2CvkC0W7HUg0ngD9kaMYzbgdijNdZz6IcCi/jDEutcDsWryqKjowqqBMViiui1gD9LVEYUzmWLCqgzpaZfYF/4JQLMaUcbFVvt9sxVNE/gT4ZmhHsXfSMCRs2DFUJ6mw+c7/ADOAVoJ27EXnLjvTmeY2+y3U7jMrYC4zO0IzpbgdiTKixnkUVqLNKqjvOJjXGb+ugNqEwX/EG0NUShTHVYz2LKvJv+HOzwNvAs0BHl0Ny3ZZBrb28ImojMCZDMz53OxBjQpn1LKpJnZLV3YA/QFD2dw4ZO7s3a6Pe2+f8BPA40M0ShTGnz3oWp0GdD6QnBKYAT+DslSHuRlXzCutExRbGRG6KOlHYzu1Y/D4Dfp+hGdXd5MgYU4r1LAJAIUedPTIGAWUVpQt7h1sk7HQ7BmAhcG6GZlxkicKYwLKeRQAp/EegP/Br4M9AF5dDqjE7uzU93nDzAbdOvxb4fxma8Z5bARgT7qxnEWAKRQpv4sxnjMS5+CvsbR3U2o0vHt8Dt+LMS1iiMCaIrGcRJP79vt8QJ3FcjjMR3s/dqIJny+A2jWrwdN/izBG9naEZXptYNyYsWbIIMgUF3gfeFzgHuBu4jDD7u8/p1aKNgkrwJviLgI+BZzI0Y2aQzmGMOYWw+sDyOoWZwEyBZsBNwGjgTFeDCpD8ujHxRVER2yILiloF+NDZwFRgYoZmfBfgYxtjKsmShQsUduIsuX0SOBtn3P1KINbNuE7XkaZ1d9TPPhSIZHEc+ADnSvkvbKjJGPeFVbIQkUKcCrHRQAFOHadnVdWTBQD9Q1TFvY0k4Crgl8AvgJDbVGh31yZH6mcfqu7LC4A5ODsWvpmhGfsDFpgx5rSFVbIAjqlqDwARaQq8DjQAMko2EpEoVS1wIb5TUtiHUxL9ZYG6wAXAcOBSoLGbsVXW1oGtIzt88X1VXrIf+AT4CPjkdBKEiBxW1YQS928C+qjqXdU9pjHmR+GWLE5S1V0ichuwSEQeBm4E/gvnG3tdERmOM9SRiNMTeUhVPxCRdsCnwFzgLGA5MBl4BGgKXKeq34hIP5zaUHHAMeBmVV0XkNjhCD9OikcCA4FLcC766+M/p+dsHtwmsYImecASnL/bT4E5GZrhiaRd+gtEZb9QePGLhzHBELbJAkBVvxeRCJwPeYABQHdV3SciUcAVqnpQRBoDC0TkQ3+7M4GrgduARTjXSwzG+ab/R5ylsGuBoapaICLnAX/FGUYK7Htwai7N8d/w70jXAyeBFN8CPalcLdl9W7Yu9dBeYD4wDydBLM7QjLyajktE2gKTgCY42+berKpbRORfOD26nsBSETkEtMQpQb9HRH6Ds71uH5xhsntVdaa/13Lyiwdwbo2+IWNcENbJwq/kUs4vVHVficf/KiJDcZZlJuOsUgL4QVVXAIjIKuArVVURWcGPe1k0AF4RkY44cw81Uqbbv+XrIv9trP+NtMK5CLAjkFLiz7bUzIWXe4D1eQ3qrD9RNzor5kj+WmBFhmZsq4FzF4sTkZKbGSUBxcn/eWCKqr7iTwDjcBI+OH9P56lqob8H2hsYrKrHROS/AVQ1TUQ6A5+LSIr/dSe/eAT3bRnjDWGdLETkDJxv5rv8Dx0p8fR1ON80e6tqvohs4sdJ5ZLffotK3C/ix7+zR4GZqnqFf+hqVoDDrzSFbTi3T0s+Ls7e4R2A9kAjnA/QRP+fJX+OdQ5DURm3AiAX5++w5G23/89sdZ53HP5jcN5kxU7OV8GPcxb+uwNwVpuBswz3yRKve1tVS662+lBVj/l/Hgw8B6Cqa0VkM05ygZ9+8TAm7IVtshCRJsBLwPP+XkHpJg2AXf5EcQ7Ot/CqaABs9/980+nEGiz+qrhrqCUlR6pAS/x8pNRzJe+Xd4Fh6dcZE9bCrTZUnIh86x86+hL4HGdiuiyvAX1EZDFOL2NtFc/1JPA3EZmHMwltvGs+cI3/5+tw5k8qY7a/Pf7hpzZAQBYxGBNqwqpnoaqn/NBW1X9RYjtUVd2DMzxRlm4l2t1U4udNxc+p6n/4cUgC4E9Vj9jUkHuASSLyP/gnuCv5uheBl/xzVQXATaqaV0Yv1ZiwJ6pacStjjDG1WrgNQxljjAkCSxbGGGMqZMnCGGNMhSxZGGOMqZAlC2OMMRWyZGGMMaZCliyMMcZUyJKFMcaYClmyMMYYUyFLFsYYYypkycIYY0yFLFkYY4ypkCULY4wxFbJkYYwxpkKWLIwxxlTIkoUxxpgKWbIwxhhTIUsWxhhjKmTJwhhjTIUsWRhjjKmQJQtjjDEVsmRhjDGmQpYsjDHGVMiShTHGmApZsjDGGFMhSxbGGGMqZMnCGGNMhSxZGGOMqdD/B2w8vqLYlU23AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "types  = ['Action','Comedy','Drama','Horror','Other','Sci-Fi']\n",
    "values = [147,456,203,87,83,63]\n",
    "colors = ['red', 'blue', 'cyan', 'purple', 'yellow', 'lightgreen']\n",
    "prop   = [14.15,43.89,19.54,8.37,7.99,6.06]\n",
    "focus  = [0, 0, 0, 0, 0, 0.2]\n",
    "\n",
    "# Data Visualization\n",
    "plt.pie(values, colors= colors, labels=types, explode = focus)\n",
    "plt.title('Movie Genres')\n",
    "plt.legend(bbox_to_anchor=(1.2,1.1))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CH4D - Activity 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Population in Thousands')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "\n",
    "# Pandas\n",
    "df = pd.read_csv(\"countryGDP.csv\")\n",
    "data1 = df['GDP per Capita'].dropna() \n",
    "data2 = df['Population'].dropna()/1000 \n",
    "\n",
    "# Data Visualization\n",
    "fig, axes = plt.subplots(1,2)\n",
    "axes[0].hist(data1, color='red',bins=20)\n",
    "axes[0].set_xlabel('GDP')\n",
    "axes[0].set_xlim([0,75000])\n",
    "axes[0].set_title('GDP Level')\n",
    "\n",
    "axes[1].hist(data2, color='blue',bins=20)\n",
    "axes[1].set_xlabel('Population')\n",
    "axes[1].set_xlim([0,200000])\n",
    "axes[1].set_title('Population in Thousands')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CH4E - Activity 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yyyymm</th>\n",
       "      <th>Index</th>\n",
       "      <th>D12</th>\n",
       "      <th>E12</th>\n",
       "      <th>b/m</th>\n",
       "      <th>tbl</th>\n",
       "      <th>AAA</th>\n",
       "      <th>BAA</th>\n",
       "      <th>lty</th>\n",
       "      <th>ntis</th>\n",
       "      <th>Rfree</th>\n",
       "      <th>infl</th>\n",
       "      <th>ltr</th>\n",
       "      <th>corpr</th>\n",
       "      <th>svar</th>\n",
       "      <th>csp</th>\n",
       "      <th>CRSP_SPvw</th>\n",
       "      <th>CRSP_SPvwx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1771</th>\n",
       "      <td>201808</td>\n",
       "      <td>2901.52</td>\n",
       "      <td>52.338996</td>\n",
       "      <td>130.39</td>\n",
       "      <td>0.229618</td>\n",
       "      <td>0.0203</td>\n",
       "      <td>0.0388</td>\n",
       "      <td>0.0477</td>\n",
       "      <td>0.0293</td>\n",
       "      <td>-0.021495</td>\n",
       "      <td>0.001692</td>\n",
       "      <td>0.000556</td>\n",
       "      <td>0.0152</td>\n",
       "      <td>0.0058</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.032938</td>\n",
       "      <td>0.030647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1772</th>\n",
       "      <td>201809</td>\n",
       "      <td>2913.98</td>\n",
       "      <td>52.338996</td>\n",
       "      <td>130.39</td>\n",
       "      <td>0.225335</td>\n",
       "      <td>0.0213</td>\n",
       "      <td>0.0398</td>\n",
       "      <td>0.0488</td>\n",
       "      <td>0.0334</td>\n",
       "      <td>-0.020871</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>0.001162</td>\n",
       "      <td>-0.0518</td>\n",
       "      <td>-0.0120</td>\n",
       "      <td>0.000230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005138</td>\n",
       "      <td>0.003758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1773</th>\n",
       "      <td>201810</td>\n",
       "      <td>2711.74</td>\n",
       "      <td>53.748178</td>\n",
       "      <td>132.39</td>\n",
       "      <td>0.237380</td>\n",
       "      <td>0.0225</td>\n",
       "      <td>0.0414</td>\n",
       "      <td>0.0507</td>\n",
       "      <td>0.0352</td>\n",
       "      <td>-0.021222</td>\n",
       "      <td>0.001875</td>\n",
       "      <td>0.001767</td>\n",
       "      <td>-0.0204</td>\n",
       "      <td>-0.0323</td>\n",
       "      <td>0.004578</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.068409</td>\n",
       "      <td>-0.069492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1774</th>\n",
       "      <td>201811</td>\n",
       "      <td>2760.17</td>\n",
       "      <td>53.748178</td>\n",
       "      <td>132.39</td>\n",
       "      <td>0.233451</td>\n",
       "      <td>0.0233</td>\n",
       "      <td>0.0422</td>\n",
       "      <td>0.0522</td>\n",
       "      <td>0.0317</td>\n",
       "      <td>-0.024593</td>\n",
       "      <td>0.001942</td>\n",
       "      <td>-0.003349</td>\n",
       "      <td>0.0505</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.002838</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.019980</td>\n",
       "      <td>0.017477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1775</th>\n",
       "      <td>201812</td>\n",
       "      <td>2506.85</td>\n",
       "      <td>53.748178</td>\n",
       "      <td>132.39</td>\n",
       "      <td>0.255578</td>\n",
       "      <td>0.0237</td>\n",
       "      <td>0.0402</td>\n",
       "      <td>0.0513</td>\n",
       "      <td>0.0284</td>\n",
       "      <td>-0.019217</td>\n",
       "      <td>0.001975</td>\n",
       "      <td>-0.003194</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>0.0370</td>\n",
       "      <td>0.006793</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.090928</td>\n",
       "      <td>-0.092457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      yyyymm    Index        D12     E12       b/m     tbl     AAA     BAA  \\\n",
       "1771  201808  2901.52  52.338996  130.39  0.229618  0.0203  0.0388  0.0477   \n",
       "1772  201809  2913.98  52.338996  130.39  0.225335  0.0213  0.0398  0.0488   \n",
       "1773  201810  2711.74  53.748178  132.39  0.237380  0.0225  0.0414  0.0507   \n",
       "1774  201811  2760.17  53.748178  132.39  0.233451  0.0233  0.0422  0.0522   \n",
       "1775  201812  2506.85  53.748178  132.39  0.255578  0.0237  0.0402  0.0513   \n",
       "\n",
       "         lty      ntis     Rfree      infl     ltr   corpr      svar  csp  \\\n",
       "1771  0.0293 -0.021495  0.001692  0.000556  0.0152  0.0058  0.000471  NaN   \n",
       "1772  0.0334 -0.020871  0.001775  0.001162 -0.0518 -0.0120  0.000230  NaN   \n",
       "1773  0.0352 -0.021222  0.001875  0.001767 -0.0204 -0.0323  0.004578  NaN   \n",
       "1774  0.0317 -0.024593  0.001942 -0.003349  0.0505  0.0071  0.002838  NaN   \n",
       "1775  0.0284 -0.019217  0.001975 -0.003194  0.0481  0.0370  0.006793  NaN   \n",
       "\n",
       "      CRSP_SPvw  CRSP_SPvwx  \n",
       "1771   0.032938    0.030647  \n",
       "1772   0.005138    0.003758  \n",
       "1773  -0.068409   -0.069492  \n",
       "1774   0.019980    0.017477  \n",
       "1775  -0.090928   -0.092457  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "goyal = pd.read_excel(io='http://www.hec.unil.ch/agoyal/docs/PredictorData2018.xlsx', \n",
    "                      sheet_name='Monthly')\n",
    "goyal.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "goyal['Date'] = pd.to_datetime(goyal['yyyymm'], format='%Y%m')\n",
    "# goyal.set_index('Date').tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "goyal.index = pd.to_datetime(goyal['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x240fbd1f6d8>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "goyal['Index'].plot(figsize=(12,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x240fbe2acc0>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "goyal['Index'].plot(figsize=(12,3),xlim=['2006-01-01','2018-12-01'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}