TimeSeries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1949-01-01', '1949-02-01', '1949-03-01', '1949-04-01',\n",
       "               '1949-05-01', '1949-06-01', '1949-07-01', '1949-08-01',\n",
       "               '1949-09-01', '1949-10-01',\n",
       "               ...\n",
       "               '1960-03-01', '1960-04-01', '1960-05-01', '1960-06-01',\n",
       "               '1960-07-01', '1960-08-01', '1960-09-01', '1960-10-01',\n",
       "               '1960-11-01', '1960-12-01'],\n",
       "              dtype='datetime64[ns]', name='Month', length=144, freq=None)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r\"C:\\Users\\Downloads\\AirPassengers.csv\",parse_dates=['Month'],\n",
    "                   index_col = \"Month\")\n",
    "\n",
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "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>#Passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            #Passengers\n",
       "Month                  \n",
       "1949-01-01          112\n",
       "1949-02-01          118\n",
       "1949-03-01          132\n",
       "1949-04-01          129\n",
       "1949-05-01          121"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "#Passengers    13.720147\n",
       "dtype: float64"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data['Month'] = pd.to_datetime(data[\"Month\"])\n",
    "data['1949'].mean()\n",
    "data['1949'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "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>#Passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            #Passengers\n",
       "Month                  \n",
       "1949-01-01          112\n",
       "1949-02-01          118\n",
       "1949-03-01          132\n",
       "1949-04-01          129\n",
       "1949-05-01          121"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['1949-01':'1949-05']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b76c79bcf8>]"
      ]
     },
     "execution_count": 204,
     "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": [
    "plt.plot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = data['#Passengers'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126.66666666666667"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['#Passengers']['1949'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1949-01-01           NaN\n",
       "1949-02-01           NaN\n",
       "1949-03-01           NaN\n",
       "1949-04-01           NaN\n",
       "1949-05-01           NaN\n",
       "                 ...    \n",
       "1960-08-01    463.333333\n",
       "1960-09-01    467.083333\n",
       "1960-10-01    471.583333\n",
       "1960-11-01    473.916667\n",
       "1960-12-01    476.166667\n",
       "Name: #Passengers, Length: 144, dtype: float64"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['#Passengers'].rolling(window=12).mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1949-01-01          NaN\n",
       "1949-02-01          NaN\n",
       "1949-03-01          NaN\n",
       "1949-04-01          NaN\n",
       "1949-05-01          NaN\n",
       "                ...    \n",
       "1960-08-01    83.630500\n",
       "1960-09-01    84.617276\n",
       "1960-10-01    82.541954\n",
       "1960-11-01    79.502382\n",
       "1960-12-01    77.737125\n",
       "Name: #Passengers, Length: 144, dtype: float64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['#Passengers'].rolling(window=12).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1949-12-31    126.666667\n",
       "1950-12-31    139.666667\n",
       "1951-12-31    170.166667\n",
       "1952-12-31    197.000000\n",
       "1953-12-31    225.000000\n",
       "1954-12-31    238.916667\n",
       "1955-12-31    284.000000\n",
       "1956-12-31    328.250000\n",
       "1957-12-31    368.416667\n",
       "1958-12-31    381.000000\n",
       "1959-12-31    428.333333\n",
       "1960-12-31    476.166667\n",
       "Freq: A-DEC, Name: #Passengers, dtype: float64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Resmapling - I want know the Monthly frequency\n",
    "data['#Passengers'].resample('M').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1949-12-31    126.666667\n",
       "1950-12-31    139.666667\n",
       "1951-12-31    170.166667\n",
       "1952-12-31    197.000000\n",
       "1953-12-31    225.000000\n",
       "1954-12-31    238.916667\n",
       "1955-12-31    284.000000\n",
       "1956-12-31    328.250000\n",
       "1957-12-31    368.416667\n",
       "1958-12-31    381.000000\n",
       "1959-12-31    428.333333\n",
       "1960-12-31    476.166667\n",
       "Freq: A-DEC, Name: #Passengers, dtype: float64"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Resmapling - I want know the Yearly  frequency\n",
    "data['#Passengers'].resample('Y').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b76756ef60>"
      ]
     },
     "execution_count": 215,
     "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": [
    "#Also we can plot these figures\n",
    "data['#Passengers'].resample('Y').mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b766edd9b0>"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iX6IwEIkjm3MO8p3n0lm7az8PXNqb0RpWQiJEYSASJ2avy+a2FzIocXhuzGBO79Eq2iVJDaIwEIkDz8/ZyAMzltM5uTFPjx5El1ZNol2S1DAKA5EYVlhcwi/f+IKpczZyds/WTLi2v8YXkiqhMBCJUbkHDnPbCwuYvT6b8Wd25e4LelFX9yaWKqIwEIlBq3fsY+xz89mxt4A/fOtUrhzQMdolSQ1X5vloZtbQzOaZ2WIzW25mvwjau5jZXDNbY2YvmVn9oL1B8Hpt8H5q1XZBpGb54IsdXPHnWeQXlvDSuKEKAqkW4ZycXACc4+6nAv2AEWY2FPgt8Ji79wBygbHB9GOBXHfvDjwWTCciZXB3nvxkLTdPTadbm6a8ccfpuj+xVJsyw8BD9gcvE4KHA+cA/wjanwMuD55fFrwmeP9c04nQIseVX1jMnS8u4pF3V3Fp3xOYPn4Y7Vo0jHZZUouEdczAzOoCGUB34M/AOmCPuxcFk2QBHYLnHYDNAO5eZGZ5QDKwO4J1i9QY2/PyGTc1naVb8rjrgp7cdlY3XUgm1S6sMHD3YqCfmbUEXgNOOtZkwc9j/RX70Q1mNg4YB5CSkhJWsSI1zcJNuYyfmsGBgiIm3pDG+b3bRrskqaXKNaCJu+8BPgGGAi3N7EiYdAS2Bs+zgE4AwfstgJxjLGuiu6e5e1rr1q0rVr1IHHttYRbXTJxDw4S6vHb7aQoCiapwziZqHWwRYGaNgPOAFcDHwDeDyUYDrwfPZwSvCd7/yN2/tGUgUlsVlzi/eXsF339pMQNSWvL67adxYttm0S5LarlwdhO1B54LjhvUAaa7+5tm9gXwopk9BCwEJgXTTwKmmtlaQlsEI6ugbpG4tDe/kDv/vpCPV+3ihqGd+fmlvUmoqxFHJfrKDAN3XwL0P0b7emDwMdrzgasjUp1IDZK5+wDfmZJO5u4DPHT5yVw/tHO0SxL5D12BLFINZq3dzW0vLKCOwdSxQxjWLTnaJYn8D4WBSBVyd577PJMH31pB99ZN+duoNFKSG0e7LJEvURiIVJHDRSX8/PVlvDh/M+f3bstj1/SjaQP9l5PYpL9MkSqQc+Awt0zNYF5mDnec3Z0fnH8idTTiqMQwhYFIhK3ZsY+bnpvPzr0FTBjZj8v6dSh7JpEoUxiIRNDHq3by3WkLaVi/Li+NH0a/Ti2jXZJIWBQGIhHg7kyelclDb31Br3bNeXp0Gie0bBTtskTCpjAQqaTC4hLun7GcaXM3cUGf0IHixvX1X0vii/5iRSphz8HQrSk/X5fNbWd140df76kDxRKXFAYiFbR+137GPpfOltxDujWlxD2FgUgFzFq7m1ufzyChbh2m3TyEtNSkaJckUikKA5Fyen7ORu6fsZzurZvy9Og0OiXpimKJfwoDkTAVFZfw0FsrePbzTM7p1YYJI/vRrGFCtMsSiQiFgUgY9uYXcse0hcxcvYvvnN6Fey86ibo6UCw1iMJApAwbsw8w9rnQ0NMPX3kKIwfrNq1S8ygMRI5j7vpsbnk+A0dDT0vNpjAQ+QrT0zfzk9eWkpLUmEmjB5Haqkm0SxKpMgoDkaMUlzi/fXclE2eu54werfjTtwfQopEOFEvNpjAQKWV/QRHfe3EhH6zYyahhnfn5Jb2pp3sUSy2gMBAJZOUe5DvPpbNm535+eVkfRg1LjXZJItWmzK88ZtbJzD42sxVmttzM7gzaHzCzLWa2KHhcVGqee81srZmtMrMLqrIDIpGQsTGXy/88iy17DvHsmEEKAql1wtkyKAJ+6O4LzKwZkGFm7wfvPebuvy89sZn1BkYCfYATgA/M7ER3L45k4SKR8s+FW7j7lSW0b9GQF8cNonubptEuSaTalRkG7r4N2BY832dmK4Dj3brpMuBFdy8ANpjZWmAwMDsC9YpETEmJ84f3V/Onj9cytGsST103kMQm9aNdlkhUlOvImJmlAv2BuUHTHWa2xMyeMbPEoK0DsLnUbFkcIzzMbJyZpZtZ+q5du8pduEhlHDxcxO3TFvCnj9cyclAnptw0REEgtVrYYWBmTYFXgO+5+17gKaAb0I/QlsOjRyY9xuz+pQb3ie6e5u5prVu3LnfhIhW1PS+fb/11Nu8u385PLz6J31x5CvXr6Ywhqd3COpvIzBIIBcEL7v4qgLvvKPX+34A3g5dZQKdSs3cEtkakWpFKWpK1h+88l86BgiImjU7jnF5to12SSEwI52wiAyYBK9z9D6Xa25ea7ApgWfB8BjDSzBqYWRegBzAvciWLVMxbS7bxrb/Opn69Orx622kKApFSwtkyOA24AVhqZouCtvuAa82sH6FdQJnAeAB3X25m04EvCJ2JdLvOJJJocnf++NFa/vD+atI6J/KXGwbSqmmDaJclElPCOZvoM459HODt48zzK+BXlahLJCKycg/yyLurmLF4K1cO6MBvrjyFBvXqRrsskZijK5ClRtm1r4DZ67OZvW43n6/LZmP2QQDuHtGTW8/sRmivp4gcTWEgcS3vUCFz12fz+bpsPl+3m9U79gPQrEE9hnRN5sbhqZzRo7UuJBMpg8JA4srBw0WkZ+Yya91uZq/LZtmWPEocGibUYVBqElf078jwbsn0OaG5BpgTKQeFgcS0w0UlLNyUy+frspm9LpuFm3MpLHYS6hr9OyXyf+f0YHi3ZPqltNSxAJFKUBhITCkucZZtyfvPbp/5mTnkF5ZQx+DkDi0Ye3pXhndLJi01kcb19ecrEin63yRR5e6s3rGfz4MDvnPWZ7MvvwiAnm2bMXJQCsO7JTOkSzItGusGMyJVRWEg1crd2ZRzkM/XZTNr7W7mrM9m9/7DAHRObswlfdszrFsrhnVNpnUzXQsgUl0UBlLl3J0PV+zk3eXbmb0umy17DgHQtnkDzujRmmHdkhneLZmOiY2jXKlI7aUwkCrj7sxcs5tH31vFkqw8EhsnMKxbMrec1Y3h3ZLp2qqJzvsXiREKA6kS8zNz+N2/VjFvQw4dExvxu2/25Yr+HXS6p0iMUhhIRC3NyuP3763i09W7aNOsAQ9e1odrBqVoiGiRGKcwkIhYs2Mfj763mneXb6dl4wTuu6gXNwxNpVF9nfsvEg8UBlIpm7IP8vgHq3lt0Raa1K/H987rwdjTu9CsoU4DFYknCgOpkO15+Tzx0Rqmz99MvbrGuK915ZavddOtI0XilMJAyiV7fwFPfrKOqXM24u58e0gKd5zdnTbNG0a7NBGpBIWBhCXvUCFP/3s9kz7bQH5hMVcN6Mh3z+1BpyRdGyBSEygM5LgOHi5i8qxM/vrpOvbmF3FJ3/Z8//wT6dZaQ0KL1CQKAzmm/MJips3dxJOfrGX3/sOc26sNP/j6ifQ5oUW0SxORKqAwkP9RWFzCKxlZPPHhGrbm5TO8WzJ/vaEnAzsnRrs0EalCZYaBmXUCpgDtgBJgortPMLMk4CUgFcgEvuXuuRYaX2ACcBFwELjR3RdUTfkSKSUlzhtLtvLY+6vJzD5Iv04t+d3Vp3Ja91bRLk1EqkE4WwZFwA/dfYGZNQMyzOx94EbgQ3d/2Mx+DPwYuAe4EOgRPIYATwU/JQa5O+9/sYNH31vNqh376NWuGU+PSuPck9po3CCRWqTMMHD3bcC24Pk+M1sBdAAuA84KJnsO+IRQGFwGTHF3B+aYWUszax8sR2KEu/PZ2t38/r3VLN68h66tmvDHa/tz8SntqVNHISBS25TrmIGZpQL9gblA2yMreHffZmZtgsk6AJtLzZYVtP1PGJjZOGAcQEpKSgVKl4pKDwaRm7shhw4tG/HIVX25coAGkROpzcIOAzNrCrwCfM/d9x5nF8Kx3vAvNbhPBCYCpKWlfel9ibwlWXt47P3VfLxqF62aNuAX3+jDyMGddO9gEQkvDMwsgVAQvODurwbNO47s/jGz9sDOoD0L6FRq9o7A1kgVLOW3ePMeJny4ho9W7qRl4wTuGdGL0cM76x7CIvIf4ZxNZMAkYIW7/6HUWzOA0cDDwc/XS7XfYWYvEjpwnKfjBdGxaPMeJnwQ2hJo2TiBuy7oyejhqTRtoBAQkf8VzlrhNOAGYKmZLQra7iMUAtPNbCywCbg6eO9tQqeVriV0aumYiFYsZVIIiEh5hXM20Wcc+zgAwLnHmN6B2ytZl1TAwk25TPhwDZ+s2kVi4wTuHtGTUcMUAiJSNq0laoAFm3KZ8MEaPl0dCoF7RvTihmGdFQIiEjatLeJYxsbQlsDMUiEwalhnmigERKSctNaIQ6VDIKlJfX58YS9uGKoQEJGK09ojjmRszOXxD1bz7zW7SWpSn3sv7MX1CgERiQCtReJAxsYcHv9gjUJARKqM1iYxLD0zhwkfhkIguUl97rsoFAK6WExEIk1rlRiUnhnaEvhs7W5aNa3PTy46ieuGpigERKTKaO0SQ+Zn5vD4B6uZtTZbISAi1UprmRgwb0MOEz78bwj89OKTuG5IZxrV1wByIlI9FAZRNHd9NhM+XMPn67Jp1bSBQkBEokZhEAUZG3N49L3VCgERiRkKg2q0OecgD7+7kreWbKNV0wb87JLefHtwikJARKJOYVAN9uUX8uQn65j02QbqGNx5bg/Gn9lVB4ZFJGZobVSFikuc6embefS9Vezef5gr+3fgrhE9ad+iUbRLExH5HwqDKjJr7W4efPMLVm7fR1rnRCaNHsSpnVpGuywRkWNSGETY+l37+fXbK/hgxU46JjbiT9/uz8WntOc494wWEYk6hUGE7Dl4mAkfrmHq7I00TKjL3SN6ctNpXWiYoIPDIhL7FAaVVFhcwgtzNvL4h2vYe6iQawZ14gfn96R1swbRLk1EJGwKgwpydz5etZNfvbWCdbsOcFr3ZH56cW9Oat882qWJiJRbnbImMLNnzGynmS0r1faAmW0xs0XB46JS791rZmvNbJWZXVBVhUfTqu37GPXMPG56Nh13eHpUGs+PHaIgEJG4Fc6WwbPAn4ApR7U/5u6/L91gZr2BkUAf4ATgAzM70d2LI1Br1O3eX8Af3l/Ni/M20axhAj+/pDfXD+1M/XplZqqISEwrMwzcfaaZpYa5vMuAF929ANhgZmuBwcDsClcYAwqKipk8K5M/f7SWQ4XFjBqWyvfO60HLxvWjXZqISERU5pjBHWY2CkgHfujuuUAHYE6pabKCtrjk7ryzbDu/eWcFm3MOcU6vNtx30Ul0b9M02qWJiERURcPgKeBBwIOfjwI3Acc6md6PtQAzGweMA0hJSalgGVVnaVYeD775BfMyc+jZthlTxw7mjB6to12WiEiVqFAYuPuOI8/N7G/Am8HLLKBTqUk7Alu/YhkTgYkAaWlpxwyMaNixN59H3l3FqwuzSGpcn19dcTLXpHWiXl0dFxCRmqtCYWBm7d19W/DyCuDImUYzgGlm9gdCB5B7APMqXWU1OHS4mIkz1/OXT9dRXOKM+1pXbj+7O80bJkS7NBGRKldmGJjZ34GzgFZmlgXcD5xlZv0I7QLKBMYDuPtyM5sOfAEUAbfH+plEJSXO64u38Mi7q9iWl89Fp7TjxyNOIiW5cbRLExGpNuYe/T00aWlpnp6eXq2fWVLizM/M4dfvrGTx5j2c0qEFP7ukN4O7JFVrHSIiFWVmGe6eFoll1ZorkAuLS1i+dS/zNmQzb0MO8zNzyTtUSNvmDXj06lO5on8H6tTRYHIiUjvV2DDILyxm0eY9zNuQw7wNOSzYlMvBw6E9Vl1bNWFEn3YM7pLEhae0001mRKTWqzFrwX35hWRszGV+Zmjlv3hzHoeLSzCDnm2bcfXAjgzuksygLom0adYw2uWKiMSUuA2DnAOH/7Pin7chh+Vb8yhxqFvHOKVDC8aclsrgLkmkdU6iRWOdESQicjxxEwbb8/KZG+zvn7chhzU79wPQoF4d+qe05I6zuzO4SzL9U1rSpEHcdEtEJCbE5FrT3dmYfTC04g++/W/KOQhA0wb1GNg5kcv7d2BIlyRO6diCBvV0AxkRkcqImTBYuX0v8zfkMDf45r9zXwEAiY0TGNwlidHDUxnSJYle7ZrpamARkQiLiTD4YtteRjz+bwDaNW/IsG7JDO6SxODUJLq3aar7B4uIVLGYCIPmDRP4/dWnMqRLEh0TG2nlLyJSzWIiDDomNuI6jmeLAAAGaUlEQVSbAztGuwwRkVpLO99FRERhICIiCgMREUFhICIiKAxERASFgYiIoDAQEREUBiIiQozc9tLM8oA1Ufr4FGBTFD63BZAXhc+F2tfnaPUX1OfqVBv73MfdG0ViQbESBhPdfVyUPnuXu7eOwueqz9X3uVHpb/DZ6nP1fa76XAmxspvojSh+9p4ofa76XH2i1V9Qn6uT+lwJMREG7h7NFWNUdtWoz9UqWrvj1OfqpT5XQkyEQZRNjHYBUVDb+lzb+gvqc20RsT7HxDEDERGJLm0ZiIhIzQsDM3vGzHaa2bJSbaea2WwzW2pmb5hZ86A91cwOmdmi4PGXUvMMDKZfa2ZPWAzfcScSfTazxmb2lpmtNLPlZvZwtPoTjkj9nkvNO6P0smJRBP+265vZRDNbHfy+r4pGf8IRwT5fG0y/xMzeNbNW0ehPWcrT3+C9vsF7y4P3Gwbt5V9/uXuNegBfAwYAy0q1zQfODJ7fBDwYPE8tPd1Ry5kHDAMMeAe4MNp9q8o+A42Bs4Pn9YF/1/Q+l5rvSmDa8aaJhUcE/7Z/ATwUPK8DtIp236qyz4Ru4rXzSD+BR4AHot23CPS3HrAEODV4nQzUDZ6Xe/1V47YM3H0mkHNUc09gZvD8feC434TMrD3Q3N1ne+hfdgpweaRrjZRI9NndD7r7x8Hzw8ACIGZvPxeJPgOYWVPgB8BDES2wCkSqz4RWKL8Jllni7rsjVmSERajPFjyaBN+QmwNbI1lnpJSzv18Hlrj74mDebHcvruj6q8aFwVdYBnwjeH410KnUe13MbKGZfWpmZwRtHYCsUtNkBW3xpLx9/g8zawlcCnxY9WVGVEX6/CDwKHCwmmqMtHL1OfjdAjxoZgvM7GUza1uN9UZCufrs7oXArcBSQiHQG5hUjfVW1lf190TAzexfwe/y7qC9Quuv2hIGNwG3m1kG0Aw4HLRvA1LcvT+hb4fTgv1xx9q/Fm+nXZW3zwCYWT3g78AT7r6+mmuurHL12cz6Ad3d/bXolBsR5f091yO0xTfL3QcAs4HfV3/ZlVLe33MCoTDoD5xAaNfKvdVfdoV9VX/rAacD1wU/rzCzc6ng+qteZGqNbe6+ktAmFWZ2InBx0F4AFATPM8xsHaG0zeJ/d5F0JEY3K79KBfqcHsw6EVjj7o9Xe9GVVIE+DwIGmlkmof8LbczsE3c/q/qrr5gK9DmD0FbQkQB8GRhbzWVXSgX6bEHbumCe6cCPq7/yivmq/hJaT316ZDefmb1N6HjD81Rg/VUrtgzMrE3wsw7wU+DIGTStzaxu8Lwr0ANY7+7bgH1mNjTYxzgKeD0qxVdQefscvH6I0IBb34tGzZVVgd/zU+5+grunEvpmtTqeggAq1GcnNHTCWcEizgW+qOayK6UCf9tbgN5mdmQMn/OBFdVdd0V9VX+BfwF9LXQmYD3gTOCLCq+/on30vAqOxv+d0OZiIaHkHAvcCawOHg/z34vtrgKWA4sJHTC9tNRy0gjtq1sH/OnIPLH4iESfCX17cEL/SRYFj+9Eu29V/XsutbxUYv9sokj9bXcmdEByCaHjQinR7ls19PmW4G97CaEwTI523yrb32D664M+LwMeKdVe7vWXrkAWEZHasZtIRESOT2EgIiIKAxERURiIiAgKAxERQWEgtYSZuZlNLfW6npntMrM3K7i8lmZ2W6nXZ1V0WSKxQGEgtcUB4GQzaxS8Pp/QxUgV1RK4rcypROKEwkBqk3f476X81xK6wAcAM0sys39aaLz7OWbWN2h/wEJjzH9iZuvN7LvBLA8D3Sw0bv7vgramZvYPC90j4IXg6k+RuKAwkNrkRWCkhW4A0heYW+q9XwAL3b0vcB+hYX+P6AVcAAwG7g8GPvsxsM7d+7n7XcF0/QkN5dEb6AqcVpWdEYkkhYHUGu6+hNCwE9cCbx/19unA1GC6j4BkM2sRvPeWuxd4aECwncBXDfk8z92z3L2E0HAeqZHtgUjVqRWjloqUMoPQkM1nEboz1BHHG/a3oFRbMV/9/ybc6URijrYMpLZ5Bviluy89qn0moXHhMbOzgN3uvvc4y9lHaGx5kRpB31ykVnH3LGDCMd56AJhsZksIjfc/uozlZJvZLAvduPwd4K1I1ypSnTRqqYiIaDeRiIgoDEREBIWBiIigMBARERQGIiKCwkBERFAYiIgICgMREQH+H8mHXUjN+9zfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['#Passengers'].resample('Y').mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b767c93470>"
      ]
     },
     "execution_count": 217,
     "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": [
    "data['#Passengers'].resample('Y').mean().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b76b62c3c8>"
      ]
     },
     "execution_count": 218,
     "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": [
    "data['#Passengers'].resample('m').mean().plot(kind = 'bar')"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}