Day16TimeSeries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import datetime \n",
    "from datetime import datetime, date\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# matplotlib 한글설정\n",
    "import matplotlib as mpl\n",
    "import matplotlib.font_manager as fm\n",
    "mpl.rcParams['axes.unicode_minus'] = False\n",
    "path = 'C:/Windows/Fonts/malgun.ttf'\n",
    "font_name = fm.FontProperties(fname=path, size=50).get_name()\n",
    "plt.rc('font', family=font_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 1, 31, 11, 16, 43, 688888)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 1, 31, 11, 17, 52, 593830)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.today()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-01-31 11:20:16.166041\n",
      "1970-01-01 00:00:00\n",
      "1970-12-12 12:24:34\n"
     ]
    }
   ],
   "source": [
    "t1 = datetime.now()\n",
    "t2 = datetime(1970,1,1)\n",
    "t3 = datetime(1970,12,12, 12, 24, 34)\n",
    "\n",
    "print(t1)\n",
    "print(t2)\n",
    "print(t3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18292 days, 11:20:16.166041\n",
      "18292 days, 11:20:16.166041\n"
     ]
    }
   ],
   "source": [
    "print(t1 - t2)\n",
    "diff = t1 - t2\n",
    "print(diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Cases_Guinea</th>\n",
       "      <th>Cases_Liberia</th>\n",
       "      <th>Cases_SierraLeone</th>\n",
       "      <th>Cases_Nigeria</th>\n",
       "      <th>Cases_Senegal</th>\n",
       "      <th>Cases_UnitedStates</th>\n",
       "      <th>Cases_Spain</th>\n",
       "      <th>Cases_Mali</th>\n",
       "      <th>Deaths_Guinea</th>\n",
       "      <th>Deaths_Liberia</th>\n",
       "      <th>Deaths_SierraLeone</th>\n",
       "      <th>Deaths_Nigeria</th>\n",
       "      <th>Deaths_Senegal</th>\n",
       "      <th>Deaths_UnitedStates</th>\n",
       "      <th>Deaths_Spain</th>\n",
       "      <th>Deaths_Mali</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/5/2015</td>\n",
       "      <td>289</td>\n",
       "      <td>2776.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10030.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1786.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2977.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/4/2015</td>\n",
       "      <td>288</td>\n",
       "      <td>2775.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9780.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1781.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2943.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2015</td>\n",
       "      <td>287</td>\n",
       "      <td>2769.0</td>\n",
       "      <td>8166.0</td>\n",
       "      <td>9722.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1767.0</td>\n",
       "      <td>3496.0</td>\n",
       "      <td>2915.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/2/2015</td>\n",
       "      <td>286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3496.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12/31/2014</td>\n",
       "      <td>284</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>8115.0</td>\n",
       "      <td>9633.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1739.0</td>\n",
       "      <td>3471.0</td>\n",
       "      <td>2827.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>3/27/2014</td>\n",
       "      <td>5</td>\n",
       "      <td>103.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>3/26/2014</td>\n",
       "      <td>4</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>3/25/2014</td>\n",
       "      <td>3</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>3/24/2014</td>\n",
       "      <td>2</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>3/22/2014</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>122 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone  \\\n",
       "0      1/5/2015  289        2776.0            NaN            10030.0   \n",
       "1      1/4/2015  288        2775.0            NaN             9780.0   \n",
       "2      1/3/2015  287        2769.0         8166.0             9722.0   \n",
       "3      1/2/2015  286           NaN         8157.0                NaN   \n",
       "4    12/31/2014  284        2730.0         8115.0             9633.0   \n",
       "..          ...  ...           ...            ...                ...   \n",
       "117   3/27/2014    5         103.0            8.0                6.0   \n",
       "118   3/26/2014    4          86.0            NaN                NaN   \n",
       "119   3/25/2014    3          86.0            NaN                NaN   \n",
       "120   3/24/2014    2          86.0            NaN                NaN   \n",
       "121   3/22/2014    0          49.0            NaN                NaN   \n",
       "\n",
       "     Cases_Nigeria  Cases_Senegal  Cases_UnitedStates  Cases_Spain  \\\n",
       "0              NaN            NaN                 NaN          NaN   \n",
       "1              NaN            NaN                 NaN          NaN   \n",
       "2              NaN            NaN                 NaN          NaN   \n",
       "3              NaN            NaN                 NaN          NaN   \n",
       "4              NaN            NaN                 NaN          NaN   \n",
       "..             ...            ...                 ...          ...   \n",
       "117            NaN            NaN                 NaN          NaN   \n",
       "118            NaN            NaN                 NaN          NaN   \n",
       "119            NaN            NaN                 NaN          NaN   \n",
       "120            NaN            NaN                 NaN          NaN   \n",
       "121            NaN            NaN                 NaN          NaN   \n",
       "\n",
       "     Cases_Mali  Deaths_Guinea  Deaths_Liberia  Deaths_SierraLeone  \\\n",
       "0           NaN         1786.0             NaN              2977.0   \n",
       "1           NaN         1781.0             NaN              2943.0   \n",
       "2           NaN         1767.0          3496.0              2915.0   \n",
       "3           NaN            NaN          3496.0                 NaN   \n",
       "4           NaN         1739.0          3471.0              2827.0   \n",
       "..          ...            ...             ...                 ...   \n",
       "117         NaN           66.0             6.0                 5.0   \n",
       "118         NaN           62.0             NaN                 NaN   \n",
       "119         NaN           60.0             NaN                 NaN   \n",
       "120         NaN           59.0             NaN                 NaN   \n",
       "121         NaN           29.0             NaN                 NaN   \n",
       "\n",
       "     Deaths_Nigeria  Deaths_Senegal  Deaths_UnitedStates  Deaths_Spain  \\\n",
       "0               NaN             NaN                  NaN           NaN   \n",
       "1               NaN             NaN                  NaN           NaN   \n",
       "2               NaN             NaN                  NaN           NaN   \n",
       "3               NaN             NaN                  NaN           NaN   \n",
       "4               NaN             NaN                  NaN           NaN   \n",
       "..              ...             ...                  ...           ...   \n",
       "117             NaN             NaN                  NaN           NaN   \n",
       "118             NaN             NaN                  NaN           NaN   \n",
       "119             NaN             NaN                  NaN           NaN   \n",
       "120             NaN             NaN                  NaN           NaN   \n",
       "121             NaN             NaN                  NaN           NaN   \n",
       "\n",
       "     Deaths_Mali  \n",
       "0            NaN  \n",
       "1            NaN  \n",
       "2            NaN  \n",
       "3            NaN  \n",
       "4            NaN  \n",
       "..           ...  \n",
       "117          NaN  \n",
       "118          NaN  \n",
       "119          NaN  \n",
       "120          NaN  \n",
       "121          NaN  \n",
       "\n",
       "[122 rows x 18 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebola = pd.read_csv('./time/country_timeseries.csv')\n",
    "ebola"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 122 entries, 0 to 121\n",
      "Data columns (total 18 columns):\n",
      "Date                   122 non-null object\n",
      "Day                    122 non-null int64\n",
      "Cases_Guinea           93 non-null float64\n",
      "Cases_Liberia          83 non-null float64\n",
      "Cases_SierraLeone      87 non-null float64\n",
      "Cases_Nigeria          38 non-null float64\n",
      "Cases_Senegal          25 non-null float64\n",
      "Cases_UnitedStates     18 non-null float64\n",
      "Cases_Spain            16 non-null float64\n",
      "Cases_Mali             12 non-null float64\n",
      "Deaths_Guinea          92 non-null float64\n",
      "Deaths_Liberia         81 non-null float64\n",
      "Deaths_SierraLeone     87 non-null float64\n",
      "Deaths_Nigeria         38 non-null float64\n",
      "Deaths_Senegal         22 non-null float64\n",
      "Deaths_UnitedStates    18 non-null float64\n",
      "Deaths_Spain           16 non-null float64\n",
      "Deaths_Mali            12 non-null float64\n",
      "dtypes: float64(16), int64(1), object(1)\n",
      "memory usage: 17.3+ KB\n"
     ]
    }
   ],
   "source": [
    "ebola.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 122 entries, 0 to 121\n",
      "Data columns (total 18 columns):\n",
      "Date                   122 non-null datetime64[ns]\n",
      "Day                    122 non-null int64\n",
      "Cases_Guinea           93 non-null float64\n",
      "Cases_Liberia          83 non-null float64\n",
      "Cases_SierraLeone      87 non-null float64\n",
      "Cases_Nigeria          38 non-null float64\n",
      "Cases_Senegal          25 non-null float64\n",
      "Cases_UnitedStates     18 non-null float64\n",
      "Cases_Spain            16 non-null float64\n",
      "Cases_Mali             12 non-null float64\n",
      "Deaths_Guinea          92 non-null float64\n",
      "Deaths_Liberia         81 non-null float64\n",
      "Deaths_SierraLeone     87 non-null float64\n",
      "Deaths_Nigeria         38 non-null float64\n",
      "Deaths_Senegal         22 non-null float64\n",
      "Deaths_UnitedStates    18 non-null float64\n",
      "Deaths_Spain           16 non-null float64\n",
      "Deaths_Mali            12 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(16), int64(1)\n",
      "memory usage: 17.3 KB\n"
     ]
    }
   ],
   "source": [
    "ebola['Date'] = pd.to_datetime(ebola.Date)\n",
    "ebola.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>other_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02/01/15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03/01/15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  other_day\n",
       "0  01/01/15\n",
       "1  02/01/15\n",
       "2  03/01/15"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df1 = pd.DataFrame({'other_day':['01/01/15', '02/01/15', '03/01/15']})\n",
    "test_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 1 columns):\n",
      "other_day    3 non-null object\n",
      "dtypes: object(1)\n",
      "memory usage: 152.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "test_df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2015-01-01\n",
       "1   2015-01-02\n",
       "2   2015-01-03\n",
       "Name: other_day, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(test_df1.other_day, format='%d/%m/%y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2015-01-01\n",
       "1   2015-02-01\n",
       "2   2015-03-01\n",
       "Name: other_day, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(test_df1.other_day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>other_day</th>\n",
       "      <th>date1</th>\n",
       "      <th>date2</th>\n",
       "      <th>date3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/15</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2001-01-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02/01/15</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>2002-01-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03/01/15</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>2003-01-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  other_day      date1      date2      date3\n",
       "0  01/01/15 2015-01-01 2015-01-01 2001-01-15\n",
       "1  02/01/15 2015-01-02 2015-02-01 2002-01-15\n",
       "2  03/01/15 2015-01-03 2015-03-01 2003-01-15"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df1['date1'] = pd.to_datetime(test_df1.other_day, format='%d/%m/%y')\n",
    "test_df1['date2'] = pd.to_datetime(test_df1.other_day, format='%m/%d/%y')\n",
    "test_df1['date3'] = pd.to_datetime(test_df1.other_day, format='%y/%m/%d')\n",
    "test_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2018-05-16\n",
       "1    2018-05-17\n",
       "2    2018-05-18\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['2018-05-16', '2018-05-17', '2018-05-18'])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2018-05-16\n",
       "1   2018-05-17\n",
       "2   2018-05-18\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.to_datetime(s)\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-05-16 00:00:00')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "5\n",
      "2018\n"
     ]
    }
   ],
   "source": [
    "print(s1[0].day)\n",
    "print(s1[0].month)\n",
    "print(s1[0].year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Cases_Guinea</th>\n",
       "      <th>Cases_Liberia</th>\n",
       "      <th>Cases_SierraLeone</th>\n",
       "      <th>Cases_Nigeria</th>\n",
       "      <th>Cases_Senegal</th>\n",
       "      <th>Cases_UnitedStates</th>\n",
       "      <th>Cases_Spain</th>\n",
       "      <th>Cases_Mali</th>\n",
       "      <th>Deaths_Guinea</th>\n",
       "      <th>Deaths_Liberia</th>\n",
       "      <th>Deaths_SierraLeone</th>\n",
       "      <th>Deaths_Nigeria</th>\n",
       "      <th>Deaths_Senegal</th>\n",
       "      <th>Deaths_UnitedStates</th>\n",
       "      <th>Deaths_Spain</th>\n",
       "      <th>Deaths_Mali</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>289</td>\n",
       "      <td>2776.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10030.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1786.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2977.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>288</td>\n",
       "      <td>2775.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9780.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1781.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2943.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>287</td>\n",
       "      <td>2769.0</td>\n",
       "      <td>8166.0</td>\n",
       "      <td>9722.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1767.0</td>\n",
       "      <td>3496.0</td>\n",
       "      <td>2915.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3496.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>284</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>8115.0</td>\n",
       "      <td>9633.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1739.0</td>\n",
       "      <td>3471.0</td>\n",
       "      <td>2827.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone  \\\n",
       "0 2015-01-05  289        2776.0            NaN            10030.0   \n",
       "1 2015-01-04  288        2775.0            NaN             9780.0   \n",
       "2 2015-01-03  287        2769.0         8166.0             9722.0   \n",
       "3 2015-01-02  286           NaN         8157.0                NaN   \n",
       "4 2014-12-31  284        2730.0         8115.0             9633.0   \n",
       "\n",
       "   Cases_Nigeria  Cases_Senegal  Cases_UnitedStates  Cases_Spain  Cases_Mali  \\\n",
       "0            NaN            NaN                 NaN          NaN         NaN   \n",
       "1            NaN            NaN                 NaN          NaN         NaN   \n",
       "2            NaN            NaN                 NaN          NaN         NaN   \n",
       "3            NaN            NaN                 NaN          NaN         NaN   \n",
       "4            NaN            NaN                 NaN          NaN         NaN   \n",
       "\n",
       "   Deaths_Guinea  Deaths_Liberia  Deaths_SierraLeone  Deaths_Nigeria  \\\n",
       "0         1786.0             NaN              2977.0             NaN   \n",
       "1         1781.0             NaN              2943.0             NaN   \n",
       "2         1767.0          3496.0              2915.0             NaN   \n",
       "3            NaN          3496.0                 NaN             NaN   \n",
       "4         1739.0          3471.0              2827.0             NaN   \n",
       "\n",
       "   Deaths_Senegal  Deaths_UnitedStates  Deaths_Spain  Deaths_Mali  \n",
       "0             NaN                  NaN           NaN          NaN  \n",
       "1             NaN                  NaN           NaN          NaN  \n",
       "2             NaN                  NaN           NaN          NaN  \n",
       "3             NaN                  NaN           NaN          NaN  \n",
       "4             NaN                  NaN           NaN          NaN  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebola.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 122 entries, 0 to 121\n",
      "Data columns (total 18 columns):\n",
      "Date                   122 non-null datetime64[ns]\n",
      "Day                    122 non-null int64\n",
      "Cases_Guinea           93 non-null float64\n",
      "Cases_Liberia          83 non-null float64\n",
      "Cases_SierraLeone      87 non-null float64\n",
      "Cases_Nigeria          38 non-null float64\n",
      "Cases_Senegal          25 non-null float64\n",
      "Cases_UnitedStates     18 non-null float64\n",
      "Cases_Spain            16 non-null float64\n",
      "Cases_Mali             12 non-null float64\n",
      "Deaths_Guinea          92 non-null float64\n",
      "Deaths_Liberia         81 non-null float64\n",
      "Deaths_SierraLeone     87 non-null float64\n",
      "Deaths_Nigeria         38 non-null float64\n",
      "Deaths_Senegal         22 non-null float64\n",
      "Deaths_UnitedStates    18 non-null float64\n",
      "Deaths_Spain           16 non-null float64\n",
      "Deaths_Mali            12 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(16), int64(1)\n",
      "memory usage: 17.3 KB\n"
     ]
    }
   ],
   "source": [
    "ebola.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebola.Date[10].year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Cases_Guinea</th>\n",
       "      <th>Cases_Liberia</th>\n",
       "      <th>Cases_SierraLeone</th>\n",
       "      <th>Cases_Nigeria</th>\n",
       "      <th>Cases_Senegal</th>\n",
       "      <th>Cases_UnitedStates</th>\n",
       "      <th>Cases_Spain</th>\n",
       "      <th>Cases_Mali</th>\n",
       "      <th>...</th>\n",
       "      <th>Deaths_Liberia</th>\n",
       "      <th>Deaths_SierraLeone</th>\n",
       "      <th>Deaths_Nigeria</th>\n",
       "      <th>Deaths_Senegal</th>\n",
       "      <th>Deaths_UnitedStates</th>\n",
       "      <th>Deaths_Spain</th>\n",
       "      <th>Deaths_Mali</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>289</td>\n",
       "      <td>2776.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10030.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2977.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>288</td>\n",
       "      <td>2775.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9780.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2943.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>287</td>\n",
       "      <td>2769.0</td>\n",
       "      <td>8166.0</td>\n",
       "      <td>9722.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3496.0</td>\n",
       "      <td>2915.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3496.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>284</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>8115.0</td>\n",
       "      <td>9633.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3471.0</td>\n",
       "      <td>2827.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2014-03-27</td>\n",
       "      <td>5</td>\n",
       "      <td>103.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2014-03-26</td>\n",
       "      <td>4</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2014-03-25</td>\n",
       "      <td>3</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2014-03-24</td>\n",
       "      <td>2</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2014-03-22</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>122 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone  \\\n",
       "0   2015-01-05  289        2776.0            NaN            10030.0   \n",
       "1   2015-01-04  288        2775.0            NaN             9780.0   \n",
       "2   2015-01-03  287        2769.0         8166.0             9722.0   \n",
       "3   2015-01-02  286           NaN         8157.0                NaN   \n",
       "4   2014-12-31  284        2730.0         8115.0             9633.0   \n",
       "..         ...  ...           ...            ...                ...   \n",
       "117 2014-03-27    5         103.0            8.0                6.0   \n",
       "118 2014-03-26    4          86.0            NaN                NaN   \n",
       "119 2014-03-25    3          86.0            NaN                NaN   \n",
       "120 2014-03-24    2          86.0            NaN                NaN   \n",
       "121 2014-03-22    0          49.0            NaN                NaN   \n",
       "\n",
       "     Cases_Nigeria  Cases_Senegal  Cases_UnitedStates  Cases_Spain  \\\n",
       "0              NaN            NaN                 NaN          NaN   \n",
       "1              NaN            NaN                 NaN          NaN   \n",
       "2              NaN            NaN                 NaN          NaN   \n",
       "3              NaN            NaN                 NaN          NaN   \n",
       "4              NaN            NaN                 NaN          NaN   \n",
       "..             ...            ...                 ...          ...   \n",
       "117            NaN            NaN                 NaN          NaN   \n",
       "118            NaN            NaN                 NaN          NaN   \n",
       "119            NaN            NaN                 NaN          NaN   \n",
       "120            NaN            NaN                 NaN          NaN   \n",
       "121            NaN            NaN                 NaN          NaN   \n",
       "\n",
       "     Cases_Mali  ...  Deaths_Liberia  Deaths_SierraLeone  Deaths_Nigeria  \\\n",
       "0           NaN  ...             NaN              2977.0             NaN   \n",
       "1           NaN  ...             NaN              2943.0             NaN   \n",
       "2           NaN  ...          3496.0              2915.0             NaN   \n",
       "3           NaN  ...          3496.0                 NaN             NaN   \n",
       "4           NaN  ...          3471.0              2827.0             NaN   \n",
       "..          ...  ...             ...                 ...             ...   \n",
       "117         NaN  ...             6.0                 5.0             NaN   \n",
       "118         NaN  ...             NaN                 NaN             NaN   \n",
       "119         NaN  ...             NaN                 NaN             NaN   \n",
       "120         NaN  ...             NaN                 NaN             NaN   \n",
       "121         NaN  ...             NaN                 NaN             NaN   \n",
       "\n",
       "     Deaths_Senegal  Deaths_UnitedStates  Deaths_Spain  Deaths_Mali  year  \\\n",
       "0               NaN                  NaN           NaN          NaN  2015   \n",
       "1               NaN                  NaN           NaN          NaN  2015   \n",
       "2               NaN                  NaN           NaN          NaN  2015   \n",
       "3               NaN                  NaN           NaN          NaN  2015   \n",
       "4               NaN                  NaN           NaN          NaN  2014   \n",
       "..              ...                  ...           ...          ...   ...   \n",
       "117             NaN                  NaN           NaN          NaN  2014   \n",
       "118             NaN                  NaN           NaN          NaN  2014   \n",
       "119             NaN                  NaN           NaN          NaN  2014   \n",
       "120             NaN                  NaN           NaN          NaN  2014   \n",
       "121             NaN                  NaN           NaN          NaN  2014   \n",
       "\n",
       "     month  day  \n",
       "0        1    5  \n",
       "1        1    4  \n",
       "2        1    3  \n",
       "3        1    2  \n",
       "4       12   31  \n",
       "..     ...  ...  \n",
       "117      3   27  \n",
       "118      3   26  \n",
       "119      3   25  \n",
       "120      3   24  \n",
       "121      3   22  \n",
       "\n",
       "[122 rows x 21 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebola['year'] = ebola.Date.apply( lambda x: x.year)\n",
    "ebola['month'] = ebola.Date.apply( lambda x: x.month)\n",
    "ebola['day'] = ebola.Date.apply( lambda x: x.day)\n",
    "ebola"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2014-03-22 00:00:00')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebola.Date.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2015-01-05 00:00:00')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebola.Date.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Cases_Guinea</th>\n",
       "      <th>Cases_Liberia</th>\n",
       "      <th>Cases_SierraLeone</th>\n",
       "      <th>Cases_Nigeria</th>\n",
       "      <th>Cases_Senegal</th>\n",
       "      <th>Cases_UnitedStates</th>\n",
       "      <th>Cases_Spain</th>\n",
       "      <th>Cases_Mali</th>\n",
       "      <th>...</th>\n",
       "      <th>Deaths_SierraLeone</th>\n",
       "      <th>Deaths_Nigeria</th>\n",
       "      <th>Deaths_Senegal</th>\n",
       "      <th>Deaths_UnitedStates</th>\n",
       "      <th>Deaths_Spain</th>\n",
       "      <th>Deaths_Mali</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>outbreak_d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>289</td>\n",
       "      <td>2776.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10030.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2977.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>289 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>288</td>\n",
       "      <td>2775.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9780.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2943.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>288 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>287</td>\n",
       "      <td>2769.0</td>\n",
       "      <td>8166.0</td>\n",
       "      <td>9722.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2915.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>287 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>286 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>284</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>8115.0</td>\n",
       "      <td>9633.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2827.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>284 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone  \\\n",
       "0 2015-01-05  289        2776.0            NaN            10030.0   \n",
       "1 2015-01-04  288        2775.0            NaN             9780.0   \n",
       "2 2015-01-03  287        2769.0         8166.0             9722.0   \n",
       "3 2015-01-02  286           NaN         8157.0                NaN   \n",
       "4 2014-12-31  284        2730.0         8115.0             9633.0   \n",
       "\n",
       "   Cases_Nigeria  Cases_Senegal  Cases_UnitedStates  Cases_Spain  Cases_Mali  \\\n",
       "0            NaN            NaN                 NaN          NaN         NaN   \n",
       "1            NaN            NaN                 NaN          NaN         NaN   \n",
       "2            NaN            NaN                 NaN          NaN         NaN   \n",
       "3            NaN            NaN                 NaN          NaN         NaN   \n",
       "4            NaN            NaN                 NaN          NaN         NaN   \n",
       "\n",
       "   ...  Deaths_SierraLeone  Deaths_Nigeria  Deaths_Senegal  \\\n",
       "0  ...              2977.0             NaN             NaN   \n",
       "1  ...              2943.0             NaN             NaN   \n",
       "2  ...              2915.0             NaN             NaN   \n",
       "3  ...                 NaN             NaN             NaN   \n",
       "4  ...              2827.0             NaN             NaN   \n",
       "\n",
       "   Deaths_UnitedStates  Deaths_Spain  Deaths_Mali  year  month  day  \\\n",
       "0                  NaN           NaN          NaN  2015      1    5   \n",
       "1                  NaN           NaN          NaN  2015      1    4   \n",
       "2                  NaN           NaN          NaN  2015      1    3   \n",
       "3                  NaN           NaN          NaN  2015      1    2   \n",
       "4                  NaN           NaN          NaN  2014     12   31   \n",
       "\n",
       "   outbreak_d  \n",
       "0    289 days  \n",
       "1    288 days  \n",
       "2    287 days  \n",
       "3    286 days  \n",
       "4    284 days  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebola['outbreak_d'] = ebola.Date - ebola.Date.min() \n",
    "ebola.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Bank Name</th>\n",
       "      <th>City</th>\n",
       "      <th>ST</th>\n",
       "      <th>CERT</th>\n",
       "      <th>Acquiring Institution</th>\n",
       "      <th>Closing Date</th>\n",
       "      <th>Updated Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Washington Federal Bank for Savings</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>30570</td>\n",
       "      <td>Royal Savings Bank</td>\n",
       "      <td>2017-12-15</td>\n",
       "      <td>2017-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Farmers and Merchants State Bank of Argonia</td>\n",
       "      <td>Argonia</td>\n",
       "      <td>KS</td>\n",
       "      <td>17719</td>\n",
       "      <td>Conway Bank</td>\n",
       "      <td>2017-10-13</td>\n",
       "      <td>2017-10-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fayette County Bank</td>\n",
       "      <td>Saint Elmo</td>\n",
       "      <td>IL</td>\n",
       "      <td>1802</td>\n",
       "      <td>United Fidelity Bank, fsb</td>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>2017-07-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Guaranty Bank, (d/b/a BestBank in Georgia &amp; Mi...</td>\n",
       "      <td>Milwaukee</td>\n",
       "      <td>WI</td>\n",
       "      <td>30003</td>\n",
       "      <td>First-Citizens Bank &amp; Trust Company</td>\n",
       "      <td>2017-05-05</td>\n",
       "      <td>2017-07-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>First NBC Bank</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>LA</td>\n",
       "      <td>58302</td>\n",
       "      <td>Whitney Bank</td>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>2017-12-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Bank Name         City  ST   CERT  \\\n",
       "0                Washington Federal Bank for Savings      Chicago  IL  30570   \n",
       "1    The Farmers and Merchants State Bank of Argonia      Argonia  KS  17719   \n",
       "2                                Fayette County Bank   Saint Elmo  IL   1802   \n",
       "3  Guaranty Bank, (d/b/a BestBank in Georgia & Mi...    Milwaukee  WI  30003   \n",
       "4                                     First NBC Bank  New Orleans  LA  58302   \n",
       "\n",
       "                 Acquiring Institution Closing Date Updated Date  \n",
       "0                   Royal Savings Bank   2017-12-15   2017-12-20  \n",
       "1                          Conway Bank   2017-10-13   2017-10-20  \n",
       "2            United Fidelity Bank, fsb   2017-05-26   2017-07-26  \n",
       "3  First-Citizens Bank & Trust Company   2017-05-05   2017-07-26  \n",
       "4                         Whitney Bank   2017-04-28   2017-12-05  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banks = pd.read_csv('./time/banklist.csv', parse_dates=[5,6] )\n",
    "banks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 555 entries, 0 to 554\n",
      "Data columns (total 7 columns):\n",
      "Bank Name                555 non-null object\n",
      "City                     555 non-null object\n",
      "ST                       555 non-null object\n",
      "CERT                     555 non-null int64\n",
      "Acquiring Institution    555 non-null object\n",
      "Closing Date             555 non-null datetime64[ns]\n",
      "Updated Date             555 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](2), int64(1), object(4)\n",
      "memory usage: 30.5+ KB\n"
     ]
    }
   ],
   "source": [
    "banks.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banks['Closing Date'][0].year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banks['Closing Date'][0].quantilerter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Bank Name</th>\n",
       "      <th>City</th>\n",
       "      <th>ST</th>\n",
       "      <th>CERT</th>\n",
       "      <th>Acquiring Institution</th>\n",
       "      <th>Closing Date</th>\n",
       "      <th>Updated Date</th>\n",
       "      <th>ClosingQ</th>\n",
       "      <th>ClosingY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Washington Federal Bank for Savings</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>30570</td>\n",
       "      <td>Royal Savings Bank</td>\n",
       "      <td>2017-12-15</td>\n",
       "      <td>2017-12-20</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Farmers and Merchants State Bank of Argonia</td>\n",
       "      <td>Argonia</td>\n",
       "      <td>KS</td>\n",
       "      <td>17719</td>\n",
       "      <td>Conway Bank</td>\n",
       "      <td>2017-10-13</td>\n",
       "      <td>2017-10-20</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fayette County Bank</td>\n",
       "      <td>Saint Elmo</td>\n",
       "      <td>IL</td>\n",
       "      <td>1802</td>\n",
       "      <td>United Fidelity Bank, fsb</td>\n",
       "      <td>2017-05-26</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Guaranty Bank, (d/b/a BestBank in Georgia &amp; Mi...</td>\n",
       "      <td>Milwaukee</td>\n",
       "      <td>WI</td>\n",
       "      <td>30003</td>\n",
       "      <td>First-Citizens Bank &amp; Trust Company</td>\n",
       "      <td>2017-05-05</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>First NBC Bank</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>LA</td>\n",
       "      <td>58302</td>\n",
       "      <td>Whitney Bank</td>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>2017-12-05</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Bank Name         City  ST   CERT  \\\n",
       "0                Washington Federal Bank for Savings      Chicago  IL  30570   \n",
       "1    The Farmers and Merchants State Bank of Argonia      Argonia  KS  17719   \n",
       "2                                Fayette County Bank   Saint Elmo  IL   1802   \n",
       "3  Guaranty Bank, (d/b/a BestBank in Georgia & Mi...    Milwaukee  WI  30003   \n",
       "4                                     First NBC Bank  New Orleans  LA  58302   \n",
       "\n",
       "                 Acquiring Institution Closing Date Updated Date  ClosingQ  \\\n",
       "0                   Royal Savings Bank   2017-12-15   2017-12-20         4   \n",
       "1                          Conway Bank   2017-10-13   2017-10-20         4   \n",
       "2            United Fidelity Bank, fsb   2017-05-26   2017-07-26         2   \n",
       "3  First-Citizens Bank & Trust Company   2017-05-05   2017-07-26         2   \n",
       "4                         Whitney Bank   2017-04-28   2017-12-05         2   \n",
       "\n",
       "   ClosingY  \n",
       "0      2017  \n",
       "1      2017  \n",
       "2      2017  \n",
       "3      2017  \n",
       "4      2017  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(banks['ClosingQ'], banks['ClosingY']) = (banks['Closing Date'].dt.quarter, banks['Closing Date'].dt.year)\n",
    "banks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 연도별 문을닫은 은행의 갯수를 출력해 차트로 만들기\n",
    "with sns.axes_style('whitegrid'):\n",
    "    banks.groupby('ClosingY').size().plot(kind='bar')\n",
    "    plt.xlabel('Year')\n",
    "    plt.ylabel('Closed')\n",
    "    plt.title('Closed banks per Year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10bde1c8>"
      ]
     },
     "execution_count": 56,
     "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": [
    "banks.groupby('ClosingQ').size().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14c12d08>"
      ]
     },
     "execution_count": 59,
     "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": [
    "banks.groupby('Closing Date').size().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2009-10-30 00:00:00')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banks.groupby('Closing Date').size().idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>ExDividend</th>\n",
       "      <th>SplitRatio</th>\n",
       "      <th>AdjOpen</th>\n",
       "      <th>AdjHigh</th>\n",
       "      <th>AdjLow</th>\n",
       "      <th>AdjClose</th>\n",
       "      <th>AdjVolume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>304.00</td>\n",
       "      <td>304.27</td>\n",
       "      <td>277.18</td>\n",
       "      <td>279.18</td>\n",
       "      <td>13696168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>304.00</td>\n",
       "      <td>304.27</td>\n",
       "      <td>277.18</td>\n",
       "      <td>279.18</td>\n",
       "      <td>13696168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-03-26</td>\n",
       "      <td>307.34</td>\n",
       "      <td>307.59</td>\n",
       "      <td>291.36</td>\n",
       "      <td>304.18</td>\n",
       "      <td>8324639.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>307.34</td>\n",
       "      <td>307.59</td>\n",
       "      <td>291.36</td>\n",
       "      <td>304.18</td>\n",
       "      <td>8324639.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>311.25</td>\n",
       "      <td>311.61</td>\n",
       "      <td>300.45</td>\n",
       "      <td>301.54</td>\n",
       "      <td>6600538.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>311.25</td>\n",
       "      <td>311.61</td>\n",
       "      <td>300.45</td>\n",
       "      <td>301.54</td>\n",
       "      <td>6600538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-22</td>\n",
       "      <td>313.89</td>\n",
       "      <td>318.82</td>\n",
       "      <td>308.18</td>\n",
       "      <td>309.10</td>\n",
       "      <td>4914307.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>313.89</td>\n",
       "      <td>318.82</td>\n",
       "      <td>308.18</td>\n",
       "      <td>309.10</td>\n",
       "      <td>4914307.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-21</td>\n",
       "      <td>310.25</td>\n",
       "      <td>322.44</td>\n",
       "      <td>310.19</td>\n",
       "      <td>316.53</td>\n",
       "      <td>5927881.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>310.25</td>\n",
       "      <td>322.44</td>\n",
       "      <td>310.19</td>\n",
       "      <td>316.53</td>\n",
       "      <td>5927881.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Open    High     Low   Close      Volume  ExDividend  \\\n",
       "0 2018-03-27  304.00  304.27  277.18  279.18  13696168.0         0.0   \n",
       "1 2018-03-26  307.34  307.59  291.36  304.18   8324639.0         0.0   \n",
       "2 2018-03-23  311.25  311.61  300.45  301.54   6600538.0         0.0   \n",
       "3 2018-03-22  313.89  318.82  308.18  309.10   4914307.0         0.0   \n",
       "4 2018-03-21  310.25  322.44  310.19  316.53   5927881.0         0.0   \n",
       "\n",
       "   SplitRatio  AdjOpen  AdjHigh  AdjLow  AdjClose   AdjVolume  \n",
       "0         1.0   304.00   304.27  277.18    279.18  13696168.0  \n",
       "1         1.0   307.34   307.59  291.36    304.18   8324639.0  \n",
       "2         1.0   311.25   311.61  300.45    301.54   6600538.0  \n",
       "3         1.0   313.89   318.82  308.18    309.10   4914307.0  \n",
       "4         1.0   310.25   322.44  310.19    316.53   5927881.0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla = pd.read_csv('./time/tesla_stock_quandl.csv', parse_dates=[0])\n",
    "tesla.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1949 entries, 0 to 1948\n",
      "Data columns (total 13 columns):\n",
      "Date          1949 non-null datetime64[ns]\n",
      "Open          1949 non-null float64\n",
      "High          1949 non-null float64\n",
      "Low           1949 non-null float64\n",
      "Close         1949 non-null float64\n",
      "Volume        1949 non-null float64\n",
      "ExDividend    1949 non-null float64\n",
      "SplitRatio    1949 non-null float64\n",
      "AdjOpen       1949 non-null float64\n",
      "AdjHigh       1949 non-null float64\n",
      "AdjLow        1949 non-null float64\n",
      "AdjClose      1949 non-null float64\n",
      "AdjVolume     1949 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(12)\n",
      "memory usage: 198.1 KB\n"
     ]
    }
   ],
   "source": [
    "tesla.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>ExDividend</th>\n",
       "      <th>SplitRatio</th>\n",
       "      <th>AdjOpen</th>\n",
       "      <th>AdjHigh</th>\n",
       "      <th>AdjLow</th>\n",
       "      <th>AdjClose</th>\n",
       "      <th>AdjVolume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td>2010-07-30</td>\n",
       "      <td>20.2000</td>\n",
       "      <td>20.44</td>\n",
       "      <td>19.5500</td>\n",
       "      <td>19.94</td>\n",
       "      <td>426900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.2000</td>\n",
       "      <td>20.44</td>\n",
       "      <td>19.5500</td>\n",
       "      <td>19.94</td>\n",
       "      <td>426900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>2010-07-29</td>\n",
       "      <td>20.7700</td>\n",
       "      <td>20.88</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>20.35</td>\n",
       "      <td>616000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.7700</td>\n",
       "      <td>20.88</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>20.35</td>\n",
       "      <td>616000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>2010-07-28</td>\n",
       "      <td>20.5500</td>\n",
       "      <td>20.90</td>\n",
       "      <td>20.5100</td>\n",
       "      <td>20.72</td>\n",
       "      <td>467200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.5500</td>\n",
       "      <td>20.90</td>\n",
       "      <td>20.5100</td>\n",
       "      <td>20.72</td>\n",
       "      <td>467200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>2010-07-27</td>\n",
       "      <td>20.9100</td>\n",
       "      <td>21.18</td>\n",
       "      <td>20.2600</td>\n",
       "      <td>20.55</td>\n",
       "      <td>619700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.9100</td>\n",
       "      <td>21.18</td>\n",
       "      <td>20.2600</td>\n",
       "      <td>20.55</td>\n",
       "      <td>619700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>2010-07-26</td>\n",
       "      <td>21.5000</td>\n",
       "      <td>21.50</td>\n",
       "      <td>20.3000</td>\n",
       "      <td>20.95</td>\n",
       "      <td>922200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.5000</td>\n",
       "      <td>21.50</td>\n",
       "      <td>20.3000</td>\n",
       "      <td>20.95</td>\n",
       "      <td>922200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>2010-07-23</td>\n",
       "      <td>21.1900</td>\n",
       "      <td>21.56</td>\n",
       "      <td>21.0600</td>\n",
       "      <td>21.29</td>\n",
       "      <td>653600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.1900</td>\n",
       "      <td>21.56</td>\n",
       "      <td>21.0600</td>\n",
       "      <td>21.29</td>\n",
       "      <td>653600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>2010-07-22</td>\n",
       "      <td>20.5000</td>\n",
       "      <td>21.25</td>\n",
       "      <td>20.3700</td>\n",
       "      <td>21.00</td>\n",
       "      <td>957800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.5000</td>\n",
       "      <td>21.25</td>\n",
       "      <td>20.3700</td>\n",
       "      <td>21.00</td>\n",
       "      <td>957800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>2010-07-21</td>\n",
       "      <td>20.6600</td>\n",
       "      <td>20.90</td>\n",
       "      <td>19.5000</td>\n",
       "      <td>20.22</td>\n",
       "      <td>1252500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.6600</td>\n",
       "      <td>20.90</td>\n",
       "      <td>19.5000</td>\n",
       "      <td>20.22</td>\n",
       "      <td>1252500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>2010-07-20</td>\n",
       "      <td>21.8500</td>\n",
       "      <td>21.85</td>\n",
       "      <td>20.0500</td>\n",
       "      <td>20.30</td>\n",
       "      <td>1825300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.8500</td>\n",
       "      <td>21.85</td>\n",
       "      <td>20.0500</td>\n",
       "      <td>20.30</td>\n",
       "      <td>1825300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td>2010-07-19</td>\n",
       "      <td>21.3700</td>\n",
       "      <td>22.25</td>\n",
       "      <td>20.9200</td>\n",
       "      <td>21.91</td>\n",
       "      <td>2486500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.3700</td>\n",
       "      <td>22.25</td>\n",
       "      <td>20.9200</td>\n",
       "      <td>21.91</td>\n",
       "      <td>2486500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>2010-07-16</td>\n",
       "      <td>20.7000</td>\n",
       "      <td>21.30</td>\n",
       "      <td>20.0500</td>\n",
       "      <td>20.64</td>\n",
       "      <td>2621300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.7000</td>\n",
       "      <td>21.30</td>\n",
       "      <td>20.0500</td>\n",
       "      <td>20.64</td>\n",
       "      <td>2621300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td>2010-07-15</td>\n",
       "      <td>19.9400</td>\n",
       "      <td>21.50</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>19.89</td>\n",
       "      <td>3739800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.9400</td>\n",
       "      <td>21.50</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>19.89</td>\n",
       "      <td>3739800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1938</th>\n",
       "      <td>2010-07-14</td>\n",
       "      <td>17.9400</td>\n",
       "      <td>20.15</td>\n",
       "      <td>17.7601</td>\n",
       "      <td>19.84</td>\n",
       "      <td>4195200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.9400</td>\n",
       "      <td>20.15</td>\n",
       "      <td>17.7601</td>\n",
       "      <td>19.84</td>\n",
       "      <td>4195200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939</th>\n",
       "      <td>2010-07-13</td>\n",
       "      <td>17.3938</td>\n",
       "      <td>18.64</td>\n",
       "      <td>16.9000</td>\n",
       "      <td>18.14</td>\n",
       "      <td>2680100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.3938</td>\n",
       "      <td>18.64</td>\n",
       "      <td>16.9000</td>\n",
       "      <td>18.14</td>\n",
       "      <td>2680100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>2010-07-12</td>\n",
       "      <td>17.9500</td>\n",
       "      <td>18.07</td>\n",
       "      <td>17.0000</td>\n",
       "      <td>17.05</td>\n",
       "      <td>2202500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.9500</td>\n",
       "      <td>18.07</td>\n",
       "      <td>17.0000</td>\n",
       "      <td>17.05</td>\n",
       "      <td>2202500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>2010-07-09</td>\n",
       "      <td>17.5800</td>\n",
       "      <td>17.90</td>\n",
       "      <td>16.5500</td>\n",
       "      <td>17.40</td>\n",
       "      <td>4050600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.5800</td>\n",
       "      <td>17.90</td>\n",
       "      <td>16.5500</td>\n",
       "      <td>17.40</td>\n",
       "      <td>4050600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>2010-07-08</td>\n",
       "      <td>16.1400</td>\n",
       "      <td>17.52</td>\n",
       "      <td>15.5700</td>\n",
       "      <td>17.46</td>\n",
       "      <td>7711400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.1400</td>\n",
       "      <td>17.52</td>\n",
       "      <td>15.5700</td>\n",
       "      <td>17.46</td>\n",
       "      <td>7711400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>2010-07-07</td>\n",
       "      <td>16.4000</td>\n",
       "      <td>16.63</td>\n",
       "      <td>14.9800</td>\n",
       "      <td>15.80</td>\n",
       "      <td>6921700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.4000</td>\n",
       "      <td>16.63</td>\n",
       "      <td>14.9800</td>\n",
       "      <td>15.80</td>\n",
       "      <td>6921700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>2010-07-06</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>20.00</td>\n",
       "      <td>15.8300</td>\n",
       "      <td>16.11</td>\n",
       "      <td>6866900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>20.00</td>\n",
       "      <td>15.8300</td>\n",
       "      <td>16.11</td>\n",
       "      <td>6866900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1945</th>\n",
       "      <td>2010-07-02</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>23.10</td>\n",
       "      <td>18.7100</td>\n",
       "      <td>19.20</td>\n",
       "      <td>5139800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>23.10</td>\n",
       "      <td>18.7100</td>\n",
       "      <td>19.20</td>\n",
       "      <td>5139800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1946</th>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>25.92</td>\n",
       "      <td>20.2700</td>\n",
       "      <td>21.96</td>\n",
       "      <td>8218800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>25.92</td>\n",
       "      <td>20.2700</td>\n",
       "      <td>21.96</td>\n",
       "      <td>8218800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date     Open   High      Low  Close     Volume  ExDividend  \\\n",
       "1926 2010-07-30  20.2000  20.44  19.5500  19.94   426900.0         0.0   \n",
       "1927 2010-07-29  20.7700  20.88  20.0000  20.35   616000.0         0.0   \n",
       "1928 2010-07-28  20.5500  20.90  20.5100  20.72   467200.0         0.0   \n",
       "1929 2010-07-27  20.9100  21.18  20.2600  20.55   619700.0         0.0   \n",
       "1930 2010-07-26  21.5000  21.50  20.3000  20.95   922200.0         0.0   \n",
       "1931 2010-07-23  21.1900  21.56  21.0600  21.29   653600.0         0.0   \n",
       "1932 2010-07-22  20.5000  21.25  20.3700  21.00   957800.0         0.0   \n",
       "1933 2010-07-21  20.6600  20.90  19.5000  20.22  1252500.0         0.0   \n",
       "1934 2010-07-20  21.8500  21.85  20.0500  20.30  1825300.0         0.0   \n",
       "1935 2010-07-19  21.3700  22.25  20.9200  21.91  2486500.0         0.0   \n",
       "1936 2010-07-16  20.7000  21.30  20.0500  20.64  2621300.0         0.0   \n",
       "1937 2010-07-15  19.9400  21.50  19.0000  19.89  3739800.0         0.0   \n",
       "1938 2010-07-14  17.9400  20.15  17.7601  19.84  4195200.0         0.0   \n",
       "1939 2010-07-13  17.3938  18.64  16.9000  18.14  2680100.0         0.0   \n",
       "1940 2010-07-12  17.9500  18.07  17.0000  17.05  2202500.0         0.0   \n",
       "1941 2010-07-09  17.5800  17.90  16.5500  17.40  4050600.0         0.0   \n",
       "1942 2010-07-08  16.1400  17.52  15.5700  17.46  7711400.0         0.0   \n",
       "1943 2010-07-07  16.4000  16.63  14.9800  15.80  6921700.0         0.0   \n",
       "1944 2010-07-06  20.0000  20.00  15.8300  16.11  6866900.0         0.0   \n",
       "1945 2010-07-02  23.0000  23.10  18.7100  19.20  5139800.0         0.0   \n",
       "1946 2010-07-01  25.0000  25.92  20.2700  21.96  8218800.0         0.0   \n",
       "\n",
       "      SplitRatio  AdjOpen  AdjHigh   AdjLow  AdjClose  AdjVolume  \n",
       "1926         1.0  20.2000    20.44  19.5500     19.94   426900.0  \n",
       "1927         1.0  20.7700    20.88  20.0000     20.35   616000.0  \n",
       "1928         1.0  20.5500    20.90  20.5100     20.72   467200.0  \n",
       "1929         1.0  20.9100    21.18  20.2600     20.55   619700.0  \n",
       "1930         1.0  21.5000    21.50  20.3000     20.95   922200.0  \n",
       "1931         1.0  21.1900    21.56  21.0600     21.29   653600.0  \n",
       "1932         1.0  20.5000    21.25  20.3700     21.00   957800.0  \n",
       "1933         1.0  20.6600    20.90  19.5000     20.22  1252500.0  \n",
       "1934         1.0  21.8500    21.85  20.0500     20.30  1825300.0  \n",
       "1935         1.0  21.3700    22.25  20.9200     21.91  2486500.0  \n",
       "1936         1.0  20.7000    21.30  20.0500     20.64  2621300.0  \n",
       "1937         1.0  19.9400    21.50  19.0000     19.89  3739800.0  \n",
       "1938         1.0  17.9400    20.15  17.7601     19.84  4195200.0  \n",
       "1939         1.0  17.3938    18.64  16.9000     18.14  2680100.0  \n",
       "1940         1.0  17.9500    18.07  17.0000     17.05  2202500.0  \n",
       "1941         1.0  17.5800    17.90  16.5500     17.40  4050600.0  \n",
       "1942         1.0  16.1400    17.52  15.5700     17.46  7711400.0  \n",
       "1943         1.0  16.4000    16.63  14.9800     15.80  6921700.0  \n",
       "1944         1.0  20.0000    20.00  15.8300     16.11  6866900.0  \n",
       "1945         1.0  23.0000    23.10  18.7100     19.20  5139800.0  \n",
       "1946         1.0  25.0000    25.92  20.2700     21.96  8218800.0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2010년 7월의 주가데이터\n",
    "tesla[(tesla.Date >= datetime(2010,7,1))&(tesla.Date<=datetime(2010,7,30))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>ExDividend</th>\n",
       "      <th>SplitRatio</th>\n",
       "      <th>AdjOpen</th>\n",
       "      <th>AdjHigh</th>\n",
       "      <th>AdjLow</th>\n",
       "      <th>AdjClose</th>\n",
       "      <th>AdjVolume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td>2010-07-30</td>\n",
       "      <td>20.2000</td>\n",
       "      <td>20.44</td>\n",
       "      <td>19.5500</td>\n",
       "      <td>19.94</td>\n",
       "      <td>426900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.2000</td>\n",
       "      <td>20.44</td>\n",
       "      <td>19.5500</td>\n",
       "      <td>19.94</td>\n",
       "      <td>426900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>2010-07-29</td>\n",
       "      <td>20.7700</td>\n",
       "      <td>20.88</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>20.35</td>\n",
       "      <td>616000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.7700</td>\n",
       "      <td>20.88</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>20.35</td>\n",
       "      <td>616000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>2010-07-28</td>\n",
       "      <td>20.5500</td>\n",
       "      <td>20.90</td>\n",
       "      <td>20.5100</td>\n",
       "      <td>20.72</td>\n",
       "      <td>467200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.5500</td>\n",
       "      <td>20.90</td>\n",
       "      <td>20.5100</td>\n",
       "      <td>20.72</td>\n",
       "      <td>467200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>2010-07-27</td>\n",
       "      <td>20.9100</td>\n",
       "      <td>21.18</td>\n",
       "      <td>20.2600</td>\n",
       "      <td>20.55</td>\n",
       "      <td>619700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.9100</td>\n",
       "      <td>21.18</td>\n",
       "      <td>20.2600</td>\n",
       "      <td>20.55</td>\n",
       "      <td>619700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>2010-07-26</td>\n",
       "      <td>21.5000</td>\n",
       "      <td>21.50</td>\n",
       "      <td>20.3000</td>\n",
       "      <td>20.95</td>\n",
       "      <td>922200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.5000</td>\n",
       "      <td>21.50</td>\n",
       "      <td>20.3000</td>\n",
       "      <td>20.95</td>\n",
       "      <td>922200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>2010-07-23</td>\n",
       "      <td>21.1900</td>\n",
       "      <td>21.56</td>\n",
       "      <td>21.0600</td>\n",
       "      <td>21.29</td>\n",
       "      <td>653600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.1900</td>\n",
       "      <td>21.56</td>\n",
       "      <td>21.0600</td>\n",
       "      <td>21.29</td>\n",
       "      <td>653600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>2010-07-22</td>\n",
       "      <td>20.5000</td>\n",
       "      <td>21.25</td>\n",
       "      <td>20.3700</td>\n",
       "      <td>21.00</td>\n",
       "      <td>957800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.5000</td>\n",
       "      <td>21.25</td>\n",
       "      <td>20.3700</td>\n",
       "      <td>21.00</td>\n",
       "      <td>957800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>2010-07-21</td>\n",
       "      <td>20.6600</td>\n",
       "      <td>20.90</td>\n",
       "      <td>19.5000</td>\n",
       "      <td>20.22</td>\n",
       "      <td>1252500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.6600</td>\n",
       "      <td>20.90</td>\n",
       "      <td>19.5000</td>\n",
       "      <td>20.22</td>\n",
       "      <td>1252500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>2010-07-20</td>\n",
       "      <td>21.8500</td>\n",
       "      <td>21.85</td>\n",
       "      <td>20.0500</td>\n",
       "      <td>20.30</td>\n",
       "      <td>1825300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.8500</td>\n",
       "      <td>21.85</td>\n",
       "      <td>20.0500</td>\n",
       "      <td>20.30</td>\n",
       "      <td>1825300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td>2010-07-19</td>\n",
       "      <td>21.3700</td>\n",
       "      <td>22.25</td>\n",
       "      <td>20.9200</td>\n",
       "      <td>21.91</td>\n",
       "      <td>2486500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.3700</td>\n",
       "      <td>22.25</td>\n",
       "      <td>20.9200</td>\n",
       "      <td>21.91</td>\n",
       "      <td>2486500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>2010-07-16</td>\n",
       "      <td>20.7000</td>\n",
       "      <td>21.30</td>\n",
       "      <td>20.0500</td>\n",
       "      <td>20.64</td>\n",
       "      <td>2621300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.7000</td>\n",
       "      <td>21.30</td>\n",
       "      <td>20.0500</td>\n",
       "      <td>20.64</td>\n",
       "      <td>2621300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td>2010-07-15</td>\n",
       "      <td>19.9400</td>\n",
       "      <td>21.50</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>19.89</td>\n",
       "      <td>3739800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.9400</td>\n",
       "      <td>21.50</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>19.89</td>\n",
       "      <td>3739800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1938</th>\n",
       "      <td>2010-07-14</td>\n",
       "      <td>17.9400</td>\n",
       "      <td>20.15</td>\n",
       "      <td>17.7601</td>\n",
       "      <td>19.84</td>\n",
       "      <td>4195200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.9400</td>\n",
       "      <td>20.15</td>\n",
       "      <td>17.7601</td>\n",
       "      <td>19.84</td>\n",
       "      <td>4195200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939</th>\n",
       "      <td>2010-07-13</td>\n",
       "      <td>17.3938</td>\n",
       "      <td>18.64</td>\n",
       "      <td>16.9000</td>\n",
       "      <td>18.14</td>\n",
       "      <td>2680100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.3938</td>\n",
       "      <td>18.64</td>\n",
       "      <td>16.9000</td>\n",
       "      <td>18.14</td>\n",
       "      <td>2680100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>2010-07-12</td>\n",
       "      <td>17.9500</td>\n",
       "      <td>18.07</td>\n",
       "      <td>17.0000</td>\n",
       "      <td>17.05</td>\n",
       "      <td>2202500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.9500</td>\n",
       "      <td>18.07</td>\n",
       "      <td>17.0000</td>\n",
       "      <td>17.05</td>\n",
       "      <td>2202500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>2010-07-09</td>\n",
       "      <td>17.5800</td>\n",
       "      <td>17.90</td>\n",
       "      <td>16.5500</td>\n",
       "      <td>17.40</td>\n",
       "      <td>4050600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.5800</td>\n",
       "      <td>17.90</td>\n",
       "      <td>16.5500</td>\n",
       "      <td>17.40</td>\n",
       "      <td>4050600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>2010-07-08</td>\n",
       "      <td>16.1400</td>\n",
       "      <td>17.52</td>\n",
       "      <td>15.5700</td>\n",
       "      <td>17.46</td>\n",
       "      <td>7711400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.1400</td>\n",
       "      <td>17.52</td>\n",
       "      <td>15.5700</td>\n",
       "      <td>17.46</td>\n",
       "      <td>7711400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>2010-07-07</td>\n",
       "      <td>16.4000</td>\n",
       "      <td>16.63</td>\n",
       "      <td>14.9800</td>\n",
       "      <td>15.80</td>\n",
       "      <td>6921700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.4000</td>\n",
       "      <td>16.63</td>\n",
       "      <td>14.9800</td>\n",
       "      <td>15.80</td>\n",
       "      <td>6921700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>2010-07-06</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>20.00</td>\n",
       "      <td>15.8300</td>\n",
       "      <td>16.11</td>\n",
       "      <td>6866900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>20.00</td>\n",
       "      <td>15.8300</td>\n",
       "      <td>16.11</td>\n",
       "      <td>6866900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1945</th>\n",
       "      <td>2010-07-02</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>23.10</td>\n",
       "      <td>18.7100</td>\n",
       "      <td>19.20</td>\n",
       "      <td>5139800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>23.10</td>\n",
       "      <td>18.7100</td>\n",
       "      <td>19.20</td>\n",
       "      <td>5139800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1946</th>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>25.92</td>\n",
       "      <td>20.2700</td>\n",
       "      <td>21.96</td>\n",
       "      <td>8218800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>25.92</td>\n",
       "      <td>20.2700</td>\n",
       "      <td>21.96</td>\n",
       "      <td>8218800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date     Open   High      Low  Close     Volume  ExDividend  \\\n",
       "1926 2010-07-30  20.2000  20.44  19.5500  19.94   426900.0         0.0   \n",
       "1927 2010-07-29  20.7700  20.88  20.0000  20.35   616000.0         0.0   \n",
       "1928 2010-07-28  20.5500  20.90  20.5100  20.72   467200.0         0.0   \n",
       "1929 2010-07-27  20.9100  21.18  20.2600  20.55   619700.0         0.0   \n",
       "1930 2010-07-26  21.5000  21.50  20.3000  20.95   922200.0         0.0   \n",
       "1931 2010-07-23  21.1900  21.56  21.0600  21.29   653600.0         0.0   \n",
       "1932 2010-07-22  20.5000  21.25  20.3700  21.00   957800.0         0.0   \n",
       "1933 2010-07-21  20.6600  20.90  19.5000  20.22  1252500.0         0.0   \n",
       "1934 2010-07-20  21.8500  21.85  20.0500  20.30  1825300.0         0.0   \n",
       "1935 2010-07-19  21.3700  22.25  20.9200  21.91  2486500.0         0.0   \n",
       "1936 2010-07-16  20.7000  21.30  20.0500  20.64  2621300.0         0.0   \n",
       "1937 2010-07-15  19.9400  21.50  19.0000  19.89  3739800.0         0.0   \n",
       "1938 2010-07-14  17.9400  20.15  17.7601  19.84  4195200.0         0.0   \n",
       "1939 2010-07-13  17.3938  18.64  16.9000  18.14  2680100.0         0.0   \n",
       "1940 2010-07-12  17.9500  18.07  17.0000  17.05  2202500.0         0.0   \n",
       "1941 2010-07-09  17.5800  17.90  16.5500  17.40  4050600.0         0.0   \n",
       "1942 2010-07-08  16.1400  17.52  15.5700  17.46  7711400.0         0.0   \n",
       "1943 2010-07-07  16.4000  16.63  14.9800  15.80  6921700.0         0.0   \n",
       "1944 2010-07-06  20.0000  20.00  15.8300  16.11  6866900.0         0.0   \n",
       "1945 2010-07-02  23.0000  23.10  18.7100  19.20  5139800.0         0.0   \n",
       "1946 2010-07-01  25.0000  25.92  20.2700  21.96  8218800.0         0.0   \n",
       "\n",
       "      SplitRatio  AdjOpen  AdjHigh   AdjLow  AdjClose  AdjVolume  \n",
       "1926         1.0  20.2000    20.44  19.5500     19.94   426900.0  \n",
       "1927         1.0  20.7700    20.88  20.0000     20.35   616000.0  \n",
       "1928         1.0  20.5500    20.90  20.5100     20.72   467200.0  \n",
       "1929         1.0  20.9100    21.18  20.2600     20.55   619700.0  \n",
       "1930         1.0  21.5000    21.50  20.3000     20.95   922200.0  \n",
       "1931         1.0  21.1900    21.56  21.0600     21.29   653600.0  \n",
       "1932         1.0  20.5000    21.25  20.3700     21.00   957800.0  \n",
       "1933         1.0  20.6600    20.90  19.5000     20.22  1252500.0  \n",
       "1934         1.0  21.8500    21.85  20.0500     20.30  1825300.0  \n",
       "1935         1.0  21.3700    22.25  20.9200     21.91  2486500.0  \n",
       "1936         1.0  20.7000    21.30  20.0500     20.64  2621300.0  \n",
       "1937         1.0  19.9400    21.50  19.0000     19.89  3739800.0  \n",
       "1938         1.0  17.9400    20.15  17.7601     19.84  4195200.0  \n",
       "1939         1.0  17.3938    18.64  16.9000     18.14  2680100.0  \n",
       "1940         1.0  17.9500    18.07  17.0000     17.05  2202500.0  \n",
       "1941         1.0  17.5800    17.90  16.5500     17.40  4050600.0  \n",
       "1942         1.0  16.1400    17.52  15.5700     17.46  7711400.0  \n",
       "1943         1.0  16.4000    16.63  14.9800     15.80  6921700.0  \n",
       "1944         1.0  20.0000    20.00  15.8300     16.11  6866900.0  \n",
       "1945         1.0  23.0000    23.10  18.7100     19.20  5139800.0  \n",
       "1946         1.0  25.0000    25.92  20.2700     21.96  8218800.0  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2010년 7월의 주가데이터\n",
    "tesla[(tesla.Date.dt.month==7)&(tesla.Date.dt.year==2010)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>ExDividend</th>\n",
       "      <th>SplitRatio</th>\n",
       "      <th>AdjOpen</th>\n",
       "      <th>AdjHigh</th>\n",
       "      <th>AdjLow</th>\n",
       "      <th>AdjClose</th>\n",
       "      <th>AdjVolume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>2010-07-06</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>15.83</td>\n",
       "      <td>16.11</td>\n",
       "      <td>6866900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>15.83</td>\n",
       "      <td>16.11</td>\n",
       "      <td>6866900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1945</th>\n",
       "      <td>2010-07-02</td>\n",
       "      <td>23.00</td>\n",
       "      <td>23.1000</td>\n",
       "      <td>18.71</td>\n",
       "      <td>19.20</td>\n",
       "      <td>5139800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>23.1000</td>\n",
       "      <td>18.71</td>\n",
       "      <td>19.20</td>\n",
       "      <td>5139800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1946</th>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>25.00</td>\n",
       "      <td>25.9200</td>\n",
       "      <td>20.27</td>\n",
       "      <td>21.96</td>\n",
       "      <td>8218800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>25.9200</td>\n",
       "      <td>20.27</td>\n",
       "      <td>21.96</td>\n",
       "      <td>8218800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947</th>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>25.79</td>\n",
       "      <td>30.4192</td>\n",
       "      <td>23.30</td>\n",
       "      <td>23.83</td>\n",
       "      <td>17187100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.79</td>\n",
       "      <td>30.4192</td>\n",
       "      <td>23.30</td>\n",
       "      <td>23.83</td>\n",
       "      <td>17187100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>2010-06-29</td>\n",
       "      <td>19.00</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>17.54</td>\n",
       "      <td>23.89</td>\n",
       "      <td>18766300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>17.54</td>\n",
       "      <td>23.89</td>\n",
       "      <td>18766300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date   Open     High    Low  Close      Volume  ExDividend  \\\n",
       "1944 2010-07-06  20.00  20.0000  15.83  16.11   6866900.0         0.0   \n",
       "1945 2010-07-02  23.00  23.1000  18.71  19.20   5139800.0         0.0   \n",
       "1946 2010-07-01  25.00  25.9200  20.27  21.96   8218800.0         0.0   \n",
       "1947 2010-06-30  25.79  30.4192  23.30  23.83  17187100.0         0.0   \n",
       "1948 2010-06-29  19.00  25.0000  17.54  23.89  18766300.0         0.0   \n",
       "\n",
       "      SplitRatio  AdjOpen  AdjHigh  AdjLow  AdjClose   AdjVolume  \n",
       "1944         1.0    20.00  20.0000   15.83     16.11   6866900.0  \n",
       "1945         1.0    23.00  23.1000   18.71     19.20   5139800.0  \n",
       "1946         1.0    25.00  25.9200   20.27     21.96   8218800.0  \n",
       "1947         1.0    25.79  30.4192   23.30     23.83  17187100.0  \n",
       "1948         1.0    19.00  25.0000   17.54     23.89  18766300.0  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>ExDividend</th>\n",
       "      <th>SplitRatio</th>\n",
       "      <th>AdjOpen</th>\n",
       "      <th>AdjHigh</th>\n",
       "      <th>AdjLow</th>\n",
       "      <th>AdjClose</th>\n",
       "      <th>AdjVolume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-27</th>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>304.00</td>\n",
       "      <td>304.27</td>\n",
       "      <td>277.18</td>\n",
       "      <td>279.18</td>\n",
       "      <td>13696168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>304.00</td>\n",
       "      <td>304.27</td>\n",
       "      <td>277.18</td>\n",
       "      <td>279.18</td>\n",
       "      <td>13696168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-26</th>\n",
       "      <td>2018-03-26</td>\n",
       "      <td>307.34</td>\n",
       "      <td>307.59</td>\n",
       "      <td>291.36</td>\n",
       "      <td>304.18</td>\n",
       "      <td>8324639.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>307.34</td>\n",
       "      <td>307.59</td>\n",
       "      <td>291.36</td>\n",
       "      <td>304.18</td>\n",
       "      <td>8324639.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-23</th>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>311.25</td>\n",
       "      <td>311.61</td>\n",
       "      <td>300.45</td>\n",
       "      <td>301.54</td>\n",
       "      <td>6600538.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>311.25</td>\n",
       "      <td>311.61</td>\n",
       "      <td>300.45</td>\n",
       "      <td>301.54</td>\n",
       "      <td>6600538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-22</th>\n",
       "      <td>2018-03-22</td>\n",
       "      <td>313.89</td>\n",
       "      <td>318.82</td>\n",
       "      <td>308.18</td>\n",
       "      <td>309.10</td>\n",
       "      <td>4914307.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>313.89</td>\n",
       "      <td>318.82</td>\n",
       "      <td>308.18</td>\n",
       "      <td>309.10</td>\n",
       "      <td>4914307.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-21</th>\n",
       "      <td>2018-03-21</td>\n",
       "      <td>310.25</td>\n",
       "      <td>322.44</td>\n",
       "      <td>310.19</td>\n",
       "      <td>316.53</td>\n",
       "      <td>5927881.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>310.25</td>\n",
       "      <td>322.44</td>\n",
       "      <td>310.19</td>\n",
       "      <td>316.53</td>\n",
       "      <td>5927881.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date    Open    High     Low   Close      Volume  ExDividend  \\\n",
       "Date                                                                            \n",
       "2018-03-27 2018-03-27  304.00  304.27  277.18  279.18  13696168.0         0.0   \n",
       "2018-03-26 2018-03-26  307.34  307.59  291.36  304.18   8324639.0         0.0   \n",
       "2018-03-23 2018-03-23  311.25  311.61  300.45  301.54   6600538.0         0.0   \n",
       "2018-03-22 2018-03-22  313.89  318.82  308.18  309.10   4914307.0         0.0   \n",
       "2018-03-21 2018-03-21  310.25  322.44  310.19  316.53   5927881.0         0.0   \n",
       "\n",
       "            SplitRatio  AdjOpen  AdjHigh  AdjLow  AdjClose   AdjVolume  \n",
       "Date                                                                    \n",
       "2018-03-27         1.0   304.00   304.27  277.18    279.18  13696168.0  \n",
       "2018-03-26         1.0   307.34   307.59  291.36    304.18   8324639.0  \n",
       "2018-03-23         1.0   311.25   311.61  300.45    301.54   6600538.0  \n",
       "2018-03-22         1.0   313.89   318.82  308.18    309.10   4914307.0  \n",
       "2018-03-21         1.0   310.25   322.44  310.19    316.53   5927881.0  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla.index = tesla.Date\n",
    "tesla.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>ExDividend</th>\n",
       "      <th>SplitRatio</th>\n",
       "      <th>AdjOpen</th>\n",
       "      <th>AdjHigh</th>\n",
       "      <th>AdjLow</th>\n",
       "      <th>AdjClose</th>\n",
       "      <th>AdjVolume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>238.51</td>\n",
       "      <td>243.4500</td>\n",
       "      <td>238.3700</td>\n",
       "      <td>240.010</td>\n",
       "      <td>2715038.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>238.51</td>\n",
       "      <td>243.4500</td>\n",
       "      <td>238.3700</td>\n",
       "      <td>240.010</td>\n",
       "      <td>2715038.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>236.60</td>\n",
       "      <td>243.6340</td>\n",
       "      <td>235.6707</td>\n",
       "      <td>238.090</td>\n",
       "      <td>3697921.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>236.60</td>\n",
       "      <td>243.6340</td>\n",
       "      <td>235.6707</td>\n",
       "      <td>238.090</td>\n",
       "      <td>3697921.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>230.06</td>\n",
       "      <td>237.7200</td>\n",
       "      <td>229.5470</td>\n",
       "      <td>237.190</td>\n",
       "      <td>2406290.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>230.06</td>\n",
       "      <td>237.7200</td>\n",
       "      <td>229.5470</td>\n",
       "      <td>237.190</td>\n",
       "      <td>2406290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>2015-12-28</td>\n",
       "      <td>231.49</td>\n",
       "      <td>231.9800</td>\n",
       "      <td>225.5400</td>\n",
       "      <td>228.950</td>\n",
       "      <td>1901304.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>231.49</td>\n",
       "      <td>231.9800</td>\n",
       "      <td>225.5400</td>\n",
       "      <td>228.950</td>\n",
       "      <td>1901304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-24</th>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>230.56</td>\n",
       "      <td>231.8800</td>\n",
       "      <td>228.2800</td>\n",
       "      <td>230.570</td>\n",
       "      <td>710277.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>230.56</td>\n",
       "      <td>231.8800</td>\n",
       "      <td>228.2800</td>\n",
       "      <td>230.570</td>\n",
       "      <td>710277.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>2015-01-08</td>\n",
       "      <td>212.81</td>\n",
       "      <td>213.7999</td>\n",
       "      <td>210.0100</td>\n",
       "      <td>210.615</td>\n",
       "      <td>3442509.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>212.81</td>\n",
       "      <td>213.7999</td>\n",
       "      <td>210.0100</td>\n",
       "      <td>210.615</td>\n",
       "      <td>3442509.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>2015-01-07</td>\n",
       "      <td>213.35</td>\n",
       "      <td>214.7800</td>\n",
       "      <td>209.7800</td>\n",
       "      <td>210.950</td>\n",
       "      <td>2968390.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>213.35</td>\n",
       "      <td>214.7800</td>\n",
       "      <td>209.7800</td>\n",
       "      <td>210.950</td>\n",
       "      <td>2968390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>2015-01-06</td>\n",
       "      <td>210.06</td>\n",
       "      <td>214.2000</td>\n",
       "      <td>204.2100</td>\n",
       "      <td>211.280</td>\n",
       "      <td>6261936.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>210.06</td>\n",
       "      <td>214.2000</td>\n",
       "      <td>204.2100</td>\n",
       "      <td>211.280</td>\n",
       "      <td>6261936.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>214.55</td>\n",
       "      <td>216.5000</td>\n",
       "      <td>207.1626</td>\n",
       "      <td>210.090</td>\n",
       "      <td>5368477.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>214.55</td>\n",
       "      <td>216.5000</td>\n",
       "      <td>207.1626</td>\n",
       "      <td>210.090</td>\n",
       "      <td>5368477.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>222.87</td>\n",
       "      <td>223.2500</td>\n",
       "      <td>213.2600</td>\n",
       "      <td>219.310</td>\n",
       "      <td>4764443.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>222.87</td>\n",
       "      <td>223.2500</td>\n",
       "      <td>213.2600</td>\n",
       "      <td>219.310</td>\n",
       "      <td>4764443.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date    Open      High       Low    Close     Volume  \\\n",
       "Date                                                                    \n",
       "2015-12-31 2015-12-31  238.51  243.4500  238.3700  240.010  2715038.0   \n",
       "2015-12-30 2015-12-30  236.60  243.6340  235.6707  238.090  3697921.0   \n",
       "2015-12-29 2015-12-29  230.06  237.7200  229.5470  237.190  2406290.0   \n",
       "2015-12-28 2015-12-28  231.49  231.9800  225.5400  228.950  1901304.0   \n",
       "2015-12-24 2015-12-24  230.56  231.8800  228.2800  230.570   710277.0   \n",
       "...               ...     ...       ...       ...      ...        ...   \n",
       "2015-01-08 2015-01-08  212.81  213.7999  210.0100  210.615  3442509.0   \n",
       "2015-01-07 2015-01-07  213.35  214.7800  209.7800  210.950  2968390.0   \n",
       "2015-01-06 2015-01-06  210.06  214.2000  204.2100  211.280  6261936.0   \n",
       "2015-01-05 2015-01-05  214.55  216.5000  207.1626  210.090  5368477.0   \n",
       "2015-01-02 2015-01-02  222.87  223.2500  213.2600  219.310  4764443.0   \n",
       "\n",
       "            ExDividend  SplitRatio  AdjOpen   AdjHigh    AdjLow  AdjClose  \\\n",
       "Date                                                                        \n",
       "2015-12-31         0.0         1.0   238.51  243.4500  238.3700   240.010   \n",
       "2015-12-30         0.0         1.0   236.60  243.6340  235.6707   238.090   \n",
       "2015-12-29         0.0         1.0   230.06  237.7200  229.5470   237.190   \n",
       "2015-12-28         0.0         1.0   231.49  231.9800  225.5400   228.950   \n",
       "2015-12-24         0.0         1.0   230.56  231.8800  228.2800   230.570   \n",
       "...                ...         ...      ...       ...       ...       ...   \n",
       "2015-01-08         0.0         1.0   212.81  213.7999  210.0100   210.615   \n",
       "2015-01-07         0.0         1.0   213.35  214.7800  209.7800   210.950   \n",
       "2015-01-06         0.0         1.0   210.06  214.2000  204.2100   211.280   \n",
       "2015-01-05         0.0         1.0   214.55  216.5000  207.1626   210.090   \n",
       "2015-01-02         0.0         1.0   222.87  223.2500  213.2600   219.310   \n",
       "\n",
       "            AdjVolume  \n",
       "Date                   \n",
       "2015-12-31  2715038.0  \n",
       "2015-12-30  3697921.0  \n",
       "2015-12-29  2406290.0  \n",
       "2015-12-28  1901304.0  \n",
       "2015-12-24   710277.0  \n",
       "...               ...  \n",
       "2015-01-08  3442509.0  \n",
       "2015-01-07  2968390.0  \n",
       "2015-01-06  6261936.0  \n",
       "2015-01-05  5368477.0  \n",
       "2015-01-02  4764443.0  \n",
       "\n",
       "[252 rows x 13 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla['2015']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>ExDividend</th>\n",
       "      <th>SplitRatio</th>\n",
       "      <th>AdjOpen</th>\n",
       "      <th>AdjHigh</th>\n",
       "      <th>AdjLow</th>\n",
       "      <th>AdjClose</th>\n",
       "      <th>AdjVolume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>264.80</td>\n",
       "      <td>270.92</td>\n",
       "      <td>264.000</td>\n",
       "      <td>268.26</td>\n",
       "      <td>3086935.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>264.80</td>\n",
       "      <td>270.92</td>\n",
       "      <td>264.000</td>\n",
       "      <td>268.26</td>\n",
       "      <td>3086935.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-29</th>\n",
       "      <td>2015-06-29</td>\n",
       "      <td>261.95</td>\n",
       "      <td>265.95</td>\n",
       "      <td>260.700</td>\n",
       "      <td>262.02</td>\n",
       "      <td>3478909.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>261.95</td>\n",
       "      <td>265.95</td>\n",
       "      <td>260.700</td>\n",
       "      <td>262.02</td>\n",
       "      <td>3478909.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-26</th>\n",
       "      <td>2015-06-26</td>\n",
       "      <td>268.89</td>\n",
       "      <td>269.11</td>\n",
       "      <td>266.000</td>\n",
       "      <td>267.09</td>\n",
       "      <td>3838434.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>268.89</td>\n",
       "      <td>269.11</td>\n",
       "      <td>266.000</td>\n",
       "      <td>267.09</td>\n",
       "      <td>3838434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-25</th>\n",
       "      <td>2015-06-25</td>\n",
       "      <td>266.45</td>\n",
       "      <td>271.41</td>\n",
       "      <td>265.250</td>\n",
       "      <td>268.79</td>\n",
       "      <td>2849204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>266.45</td>\n",
       "      <td>271.41</td>\n",
       "      <td>265.250</td>\n",
       "      <td>268.79</td>\n",
       "      <td>2849204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-24</th>\n",
       "      <td>2015-06-24</td>\n",
       "      <td>266.98</td>\n",
       "      <td>267.35</td>\n",
       "      <td>263.720</td>\n",
       "      <td>265.17</td>\n",
       "      <td>2412293.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>266.98</td>\n",
       "      <td>267.35</td>\n",
       "      <td>263.720</td>\n",
       "      <td>265.17</td>\n",
       "      <td>2412293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-23</th>\n",
       "      <td>2015-06-23</td>\n",
       "      <td>260.32</td>\n",
       "      <td>268.00</td>\n",
       "      <td>258.570</td>\n",
       "      <td>267.67</td>\n",
       "      <td>3870818.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>260.32</td>\n",
       "      <td>268.00</td>\n",
       "      <td>258.570</td>\n",
       "      <td>267.67</td>\n",
       "      <td>3870818.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-22</th>\n",
       "      <td>2015-06-22</td>\n",
       "      <td>262.15</td>\n",
       "      <td>264.40</td>\n",
       "      <td>255.690</td>\n",
       "      <td>259.79</td>\n",
       "      <td>4561079.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>262.15</td>\n",
       "      <td>264.40</td>\n",
       "      <td>255.690</td>\n",
       "      <td>259.79</td>\n",
       "      <td>4561079.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-19</th>\n",
       "      <td>2015-06-19</td>\n",
       "      <td>262.40</td>\n",
       "      <td>263.80</td>\n",
       "      <td>260.100</td>\n",
       "      <td>262.51</td>\n",
       "      <td>2463013.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>262.40</td>\n",
       "      <td>263.80</td>\n",
       "      <td>260.100</td>\n",
       "      <td>262.51</td>\n",
       "      <td>2463013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-18</th>\n",
       "      <td>2015-06-18</td>\n",
       "      <td>262.00</td>\n",
       "      <td>263.46</td>\n",
       "      <td>260.020</td>\n",
       "      <td>261.89</td>\n",
       "      <td>2782703.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>262.00</td>\n",
       "      <td>263.46</td>\n",
       "      <td>260.020</td>\n",
       "      <td>261.89</td>\n",
       "      <td>2782703.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-17</th>\n",
       "      <td>2015-06-17</td>\n",
       "      <td>252.17</td>\n",
       "      <td>264.36</td>\n",
       "      <td>252.020</td>\n",
       "      <td>260.41</td>\n",
       "      <td>5512920.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252.17</td>\n",
       "      <td>264.36</td>\n",
       "      <td>252.020</td>\n",
       "      <td>260.41</td>\n",
       "      <td>5512920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-16</th>\n",
       "      <td>2015-06-16</td>\n",
       "      <td>250.13</td>\n",
       "      <td>253.44</td>\n",
       "      <td>249.095</td>\n",
       "      <td>253.12</td>\n",
       "      <td>1984678.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>250.13</td>\n",
       "      <td>253.44</td>\n",
       "      <td>249.095</td>\n",
       "      <td>253.12</td>\n",
       "      <td>1984678.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-15</th>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>249.70</td>\n",
       "      <td>251.28</td>\n",
       "      <td>246.010</td>\n",
       "      <td>250.38</td>\n",
       "      <td>2186175.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>249.70</td>\n",
       "      <td>251.28</td>\n",
       "      <td>246.010</td>\n",
       "      <td>250.38</td>\n",
       "      <td>2186175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-12</th>\n",
       "      <td>2015-06-12</td>\n",
       "      <td>250.21</td>\n",
       "      <td>253.46</td>\n",
       "      <td>250.210</td>\n",
       "      <td>250.69</td>\n",
       "      <td>1422335.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>250.21</td>\n",
       "      <td>253.46</td>\n",
       "      <td>250.210</td>\n",
       "      <td>250.69</td>\n",
       "      <td>1422335.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-11</th>\n",
       "      <td>2015-06-11</td>\n",
       "      <td>253.26</td>\n",
       "      <td>254.37</td>\n",
       "      <td>250.430</td>\n",
       "      <td>251.41</td>\n",
       "      <td>2044058.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>253.26</td>\n",
       "      <td>254.37</td>\n",
       "      <td>250.430</td>\n",
       "      <td>251.41</td>\n",
       "      <td>2044058.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-10</th>\n",
       "      <td>2015-06-10</td>\n",
       "      <td>251.90</td>\n",
       "      <td>254.00</td>\n",
       "      <td>248.500</td>\n",
       "      <td>250.70</td>\n",
       "      <td>3454453.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>251.90</td>\n",
       "      <td>254.00</td>\n",
       "      <td>248.500</td>\n",
       "      <td>250.70</td>\n",
       "      <td>3454453.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-09</th>\n",
       "      <td>2015-06-09</td>\n",
       "      <td>255.40</td>\n",
       "      <td>257.74</td>\n",
       "      <td>254.140</td>\n",
       "      <td>256.00</td>\n",
       "      <td>2611146.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>255.40</td>\n",
       "      <td>257.74</td>\n",
       "      <td>254.140</td>\n",
       "      <td>256.00</td>\n",
       "      <td>2611146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-08</th>\n",
       "      <td>2015-06-08</td>\n",
       "      <td>250.85</td>\n",
       "      <td>258.75</td>\n",
       "      <td>250.310</td>\n",
       "      <td>256.29</td>\n",
       "      <td>5016997.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>250.85</td>\n",
       "      <td>258.75</td>\n",
       "      <td>250.310</td>\n",
       "      <td>256.29</td>\n",
       "      <td>5016997.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-05</th>\n",
       "      <td>2015-06-05</td>\n",
       "      <td>246.00</td>\n",
       "      <td>249.70</td>\n",
       "      <td>245.680</td>\n",
       "      <td>249.14</td>\n",
       "      <td>3022026.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>246.00</td>\n",
       "      <td>249.70</td>\n",
       "      <td>245.680</td>\n",
       "      <td>249.14</td>\n",
       "      <td>3022026.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-04</th>\n",
       "      <td>2015-06-04</td>\n",
       "      <td>247.50</td>\n",
       "      <td>249.30</td>\n",
       "      <td>245.710</td>\n",
       "      <td>245.92</td>\n",
       "      <td>2453615.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>247.50</td>\n",
       "      <td>249.30</td>\n",
       "      <td>245.710</td>\n",
       "      <td>245.92</td>\n",
       "      <td>2453615.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-03</th>\n",
       "      <td>2015-06-03</td>\n",
       "      <td>248.20</td>\n",
       "      <td>250.72</td>\n",
       "      <td>247.010</td>\n",
       "      <td>248.99</td>\n",
       "      <td>1781505.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>248.20</td>\n",
       "      <td>250.72</td>\n",
       "      <td>247.010</td>\n",
       "      <td>248.99</td>\n",
       "      <td>1781505.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-02</th>\n",
       "      <td>2015-06-02</td>\n",
       "      <td>248.92</td>\n",
       "      <td>249.40</td>\n",
       "      <td>246.300</td>\n",
       "      <td>248.35</td>\n",
       "      <td>2134809.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>248.92</td>\n",
       "      <td>249.40</td>\n",
       "      <td>246.300</td>\n",
       "      <td>248.35</td>\n",
       "      <td>2134809.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>2015-06-01</td>\n",
       "      <td>251.41</td>\n",
       "      <td>251.60</td>\n",
       "      <td>247.470</td>\n",
       "      <td>249.45</td>\n",
       "      <td>2505057.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>251.41</td>\n",
       "      <td>251.60</td>\n",
       "      <td>247.470</td>\n",
       "      <td>249.45</td>\n",
       "      <td>2505057.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date    Open    High      Low   Close     Volume  ExDividend  \\\n",
       "Date                                                                            \n",
       "2015-06-30 2015-06-30  264.80  270.92  264.000  268.26  3086935.0         0.0   \n",
       "2015-06-29 2015-06-29  261.95  265.95  260.700  262.02  3478909.0         0.0   \n",
       "2015-06-26 2015-06-26  268.89  269.11  266.000  267.09  3838434.0         0.0   \n",
       "2015-06-25 2015-06-25  266.45  271.41  265.250  268.79  2849204.0         0.0   \n",
       "2015-06-24 2015-06-24  266.98  267.35  263.720  265.17  2412293.0         0.0   \n",
       "2015-06-23 2015-06-23  260.32  268.00  258.570  267.67  3870818.0         0.0   \n",
       "2015-06-22 2015-06-22  262.15  264.40  255.690  259.79  4561079.0         0.0   \n",
       "2015-06-19 2015-06-19  262.40  263.80  260.100  262.51  2463013.0         0.0   \n",
       "2015-06-18 2015-06-18  262.00  263.46  260.020  261.89  2782703.0         0.0   \n",
       "2015-06-17 2015-06-17  252.17  264.36  252.020  260.41  5512920.0         0.0   \n",
       "2015-06-16 2015-06-16  250.13  253.44  249.095  253.12  1984678.0         0.0   \n",
       "2015-06-15 2015-06-15  249.70  251.28  246.010  250.38  2186175.0         0.0   \n",
       "2015-06-12 2015-06-12  250.21  253.46  250.210  250.69  1422335.0         0.0   \n",
       "2015-06-11 2015-06-11  253.26  254.37  250.430  251.41  2044058.0         0.0   \n",
       "2015-06-10 2015-06-10  251.90  254.00  248.500  250.70  3454453.0         0.0   \n",
       "2015-06-09 2015-06-09  255.40  257.74  254.140  256.00  2611146.0         0.0   \n",
       "2015-06-08 2015-06-08  250.85  258.75  250.310  256.29  5016997.0         0.0   \n",
       "2015-06-05 2015-06-05  246.00  249.70  245.680  249.14  3022026.0         0.0   \n",
       "2015-06-04 2015-06-04  247.50  249.30  245.710  245.92  2453615.0         0.0   \n",
       "2015-06-03 2015-06-03  248.20  250.72  247.010  248.99  1781505.0         0.0   \n",
       "2015-06-02 2015-06-02  248.92  249.40  246.300  248.35  2134809.0         0.0   \n",
       "2015-06-01 2015-06-01  251.41  251.60  247.470  249.45  2505057.0         0.0   \n",
       "\n",
       "            SplitRatio  AdjOpen  AdjHigh   AdjLow  AdjClose  AdjVolume  \n",
       "Date                                                                    \n",
       "2015-06-30         1.0   264.80   270.92  264.000    268.26  3086935.0  \n",
       "2015-06-29         1.0   261.95   265.95  260.700    262.02  3478909.0  \n",
       "2015-06-26         1.0   268.89   269.11  266.000    267.09  3838434.0  \n",
       "2015-06-25         1.0   266.45   271.41  265.250    268.79  2849204.0  \n",
       "2015-06-24         1.0   266.98   267.35  263.720    265.17  2412293.0  \n",
       "2015-06-23         1.0   260.32   268.00  258.570    267.67  3870818.0  \n",
       "2015-06-22         1.0   262.15   264.40  255.690    259.79  4561079.0  \n",
       "2015-06-19         1.0   262.40   263.80  260.100    262.51  2463013.0  \n",
       "2015-06-18         1.0   262.00   263.46  260.020    261.89  2782703.0  \n",
       "2015-06-17         1.0   252.17   264.36  252.020    260.41  5512920.0  \n",
       "2015-06-16         1.0   250.13   253.44  249.095    253.12  1984678.0  \n",
       "2015-06-15         1.0   249.70   251.28  246.010    250.38  2186175.0  \n",
       "2015-06-12         1.0   250.21   253.46  250.210    250.69  1422335.0  \n",
       "2015-06-11         1.0   253.26   254.37  250.430    251.41  2044058.0  \n",
       "2015-06-10         1.0   251.90   254.00  248.500    250.70  3454453.0  \n",
       "2015-06-09         1.0   255.40   257.74  254.140    256.00  2611146.0  \n",
       "2015-06-08         1.0   250.85   258.75  250.310    256.29  5016997.0  \n",
       "2015-06-05         1.0   246.00   249.70  245.680    249.14  3022026.0  \n",
       "2015-06-04         1.0   247.50   249.30  245.710    245.92  2453615.0  \n",
       "2015-06-03         1.0   248.20   250.72  247.010    248.99  1781505.0  \n",
       "2015-06-02         1.0   248.92   249.40  246.300    248.35  2134809.0  \n",
       "2015-06-01         1.0   251.41   251.60  247.470    249.45  2505057.0  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla['2015-06']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>ExDividend</th>\n",
       "      <th>SplitRatio</th>\n",
       "      <th>AdjOpen</th>\n",
       "      <th>AdjHigh</th>\n",
       "      <th>AdjLow</th>\n",
       "      <th>AdjClose</th>\n",
       "      <th>AdjVolume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-06-05</th>\n",
       "      <td>2015-06-05</td>\n",
       "      <td>246.0</td>\n",
       "      <td>249.7</td>\n",
       "      <td>245.68</td>\n",
       "      <td>249.14</td>\n",
       "      <td>3022026.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>249.7</td>\n",
       "      <td>245.68</td>\n",
       "      <td>249.14</td>\n",
       "      <td>3022026.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date   Open   High     Low   Close     Volume  ExDividend  \\\n",
       "Date                                                                         \n",
       "2015-06-05 2015-06-05  246.0  249.7  245.68  249.14  3022026.0         0.0   \n",
       "\n",
       "            SplitRatio  AdjOpen  AdjHigh  AdjLow  AdjClose  AdjVolume  \n",
       "Date                                                                   \n",
       "2015-06-05         1.0    246.0    249.7  245.68    249.14  3022026.0  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla['2015-06-05']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>ExDividend</th>\n",
       "      <th>SplitRatio</th>\n",
       "      <th>AdjOpen</th>\n",
       "      <th>AdjHigh</th>\n",
       "      <th>AdjLow</th>\n",
       "      <th>AdjClose</th>\n",
       "      <th>AdjVolume</th>\n",
       "      <th>refdat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-27</th>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>304.00</td>\n",
       "      <td>304.27</td>\n",
       "      <td>277.18</td>\n",
       "      <td>279.18</td>\n",
       "      <td>13696168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>304.00</td>\n",
       "      <td>304.27</td>\n",
       "      <td>277.18</td>\n",
       "      <td>279.18</td>\n",
       "      <td>13696168.0</td>\n",
       "      <td>2828 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-26</th>\n",
       "      <td>2018-03-26</td>\n",
       "      <td>307.34</td>\n",
       "      <td>307.59</td>\n",
       "      <td>291.36</td>\n",
       "      <td>304.18</td>\n",
       "      <td>8324639.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>307.34</td>\n",
       "      <td>307.59</td>\n",
       "      <td>291.36</td>\n",
       "      <td>304.18</td>\n",
       "      <td>8324639.0</td>\n",
       "      <td>2827 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-23</th>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>311.25</td>\n",
       "      <td>311.61</td>\n",
       "      <td>300.45</td>\n",
       "      <td>301.54</td>\n",
       "      <td>6600538.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>311.25</td>\n",
       "      <td>311.61</td>\n",
       "      <td>300.45</td>\n",
       "      <td>301.54</td>\n",
       "      <td>6600538.0</td>\n",
       "      <td>2824 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-22</th>\n",
       "      <td>2018-03-22</td>\n",
       "      <td>313.89</td>\n",
       "      <td>318.82</td>\n",
       "      <td>308.18</td>\n",
       "      <td>309.10</td>\n",
       "      <td>4914307.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>313.89</td>\n",
       "      <td>318.82</td>\n",
       "      <td>308.18</td>\n",
       "      <td>309.10</td>\n",
       "      <td>4914307.0</td>\n",
       "      <td>2823 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-21</th>\n",
       "      <td>2018-03-21</td>\n",
       "      <td>310.25</td>\n",
       "      <td>322.44</td>\n",
       "      <td>310.19</td>\n",
       "      <td>316.53</td>\n",
       "      <td>5927881.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>310.25</td>\n",
       "      <td>322.44</td>\n",
       "      <td>310.19</td>\n",
       "      <td>316.53</td>\n",
       "      <td>5927881.0</td>\n",
       "      <td>2822 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date    Open    High     Low   Close      Volume  ExDividend  \\\n",
       "Date                                                                            \n",
       "2018-03-27 2018-03-27  304.00  304.27  277.18  279.18  13696168.0         0.0   \n",
       "2018-03-26 2018-03-26  307.34  307.59  291.36  304.18   8324639.0         0.0   \n",
       "2018-03-23 2018-03-23  311.25  311.61  300.45  301.54   6600538.0         0.0   \n",
       "2018-03-22 2018-03-22  313.89  318.82  308.18  309.10   4914307.0         0.0   \n",
       "2018-03-21 2018-03-21  310.25  322.44  310.19  316.53   5927881.0         0.0   \n",
       "\n",
       "            SplitRatio  AdjOpen  AdjHigh  AdjLow  AdjClose   AdjVolume  \\\n",
       "Date                                                                     \n",
       "2018-03-27         1.0   304.00   304.27  277.18    279.18  13696168.0   \n",
       "2018-03-26         1.0   307.34   307.59  291.36    304.18   8324639.0   \n",
       "2018-03-23         1.0   311.25   311.61  300.45    301.54   6600538.0   \n",
       "2018-03-22         1.0   313.89   318.82  308.18    309.10   4914307.0   \n",
       "2018-03-21         1.0   310.25   322.44  310.19    316.53   5927881.0   \n",
       "\n",
       "              refdat  \n",
       "Date                  \n",
       "2018-03-27 2828 days  \n",
       "2018-03-26 2827 days  \n",
       "2018-03-23 2824 days  \n",
       "2018-03-22 2823 days  \n",
       "2018-03-21 2822 days  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla['refdat'] = tesla.Date - tesla.Date.min()\n",
    "tesla.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>ExDividend</th>\n",
       "      <th>SplitRatio</th>\n",
       "      <th>AdjOpen</th>\n",
       "      <th>AdjHigh</th>\n",
       "      <th>AdjLow</th>\n",
       "      <th>AdjClose</th>\n",
       "      <th>AdjVolume</th>\n",
       "      <th>refdat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-07-06</th>\n",
       "      <td>2010-07-06</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>15.83</td>\n",
       "      <td>16.11</td>\n",
       "      <td>6866900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>15.83</td>\n",
       "      <td>16.11</td>\n",
       "      <td>6866900.0</td>\n",
       "      <td>7 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-02</th>\n",
       "      <td>2010-07-02</td>\n",
       "      <td>23.00</td>\n",
       "      <td>23.1000</td>\n",
       "      <td>18.71</td>\n",
       "      <td>19.20</td>\n",
       "      <td>5139800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>23.1000</td>\n",
       "      <td>18.71</td>\n",
       "      <td>19.20</td>\n",
       "      <td>5139800.0</td>\n",
       "      <td>3 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>25.00</td>\n",
       "      <td>25.9200</td>\n",
       "      <td>20.27</td>\n",
       "      <td>21.96</td>\n",
       "      <td>8218800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>25.9200</td>\n",
       "      <td>20.27</td>\n",
       "      <td>21.96</td>\n",
       "      <td>8218800.0</td>\n",
       "      <td>2 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>25.79</td>\n",
       "      <td>30.4192</td>\n",
       "      <td>23.30</td>\n",
       "      <td>23.83</td>\n",
       "      <td>17187100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.79</td>\n",
       "      <td>30.4192</td>\n",
       "      <td>23.30</td>\n",
       "      <td>23.83</td>\n",
       "      <td>17187100.0</td>\n",
       "      <td>1 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-29</th>\n",
       "      <td>2010-06-29</td>\n",
       "      <td>19.00</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>17.54</td>\n",
       "      <td>23.89</td>\n",
       "      <td>18766300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>17.54</td>\n",
       "      <td>23.89</td>\n",
       "      <td>18766300.0</td>\n",
       "      <td>0 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date   Open     High    Low  Close      Volume  ExDividend  \\\n",
       "Date                                                                          \n",
       "2010-07-06 2010-07-06  20.00  20.0000  15.83  16.11   6866900.0         0.0   \n",
       "2010-07-02 2010-07-02  23.00  23.1000  18.71  19.20   5139800.0         0.0   \n",
       "2010-07-01 2010-07-01  25.00  25.9200  20.27  21.96   8218800.0         0.0   \n",
       "2010-06-30 2010-06-30  25.79  30.4192  23.30  23.83  17187100.0         0.0   \n",
       "2010-06-29 2010-06-29  19.00  25.0000  17.54  23.89  18766300.0         0.0   \n",
       "\n",
       "            SplitRatio  AdjOpen  AdjHigh  AdjLow  AdjClose   AdjVolume refdat  \n",
       "Date                                                                           \n",
       "2010-07-06         1.0    20.00  20.0000   15.83     16.11   6866900.0 7 days  \n",
       "2010-07-02         1.0    23.00  23.1000   18.71     19.20   5139800.0 3 days  \n",
       "2010-07-01         1.0    25.00  25.9200   20.27     21.96   8218800.0 2 days  \n",
       "2010-06-30         1.0    25.79  30.4192   23.30     23.83  17187100.0 1 days  \n",
       "2010-06-29         1.0    19.00  25.0000   17.54     23.89  18766300.0 0 days  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>ExDividend</th>\n",
       "      <th>SplitRatio</th>\n",
       "      <th>AdjOpen</th>\n",
       "      <th>AdjHigh</th>\n",
       "      <th>AdjLow</th>\n",
       "      <th>AdjClose</th>\n",
       "      <th>AdjVolume</th>\n",
       "      <th>refdat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>252.00</td>\n",
       "      <td>252.40</td>\n",
       "      <td>242.34</td>\n",
       "      <td>248.40</td>\n",
       "      <td>4879816.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252.00</td>\n",
       "      <td>252.40</td>\n",
       "      <td>242.34</td>\n",
       "      <td>248.40</td>\n",
       "      <td>4879816.0</td>\n",
       "      <td>1919 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-29</th>\n",
       "      <td>2015-09-29</td>\n",
       "      <td>250.46</td>\n",
       "      <td>254.73</td>\n",
       "      <td>245.46</td>\n",
       "      <td>246.65</td>\n",
       "      <td>3703154.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>250.46</td>\n",
       "      <td>254.73</td>\n",
       "      <td>245.46</td>\n",
       "      <td>246.65</td>\n",
       "      <td>3703154.0</td>\n",
       "      <td>1918 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-28</th>\n",
       "      <td>2015-09-28</td>\n",
       "      <td>257.35</td>\n",
       "      <td>259.79</td>\n",
       "      <td>246.61</td>\n",
       "      <td>248.43</td>\n",
       "      <td>4881802.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>257.35</td>\n",
       "      <td>259.79</td>\n",
       "      <td>246.61</td>\n",
       "      <td>248.43</td>\n",
       "      <td>4881802.0</td>\n",
       "      <td>1917 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-25</th>\n",
       "      <td>2015-09-25</td>\n",
       "      <td>266.61</td>\n",
       "      <td>266.91</td>\n",
       "      <td>256.15</td>\n",
       "      <td>256.91</td>\n",
       "      <td>3773392.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>266.61</td>\n",
       "      <td>266.91</td>\n",
       "      <td>256.15</td>\n",
       "      <td>256.91</td>\n",
       "      <td>3773392.0</td>\n",
       "      <td>1914 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-24</th>\n",
       "      <td>2015-09-24</td>\n",
       "      <td>259.53</td>\n",
       "      <td>263.45</td>\n",
       "      <td>256.21</td>\n",
       "      <td>263.12</td>\n",
       "      <td>3448191.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>259.53</td>\n",
       "      <td>263.45</td>\n",
       "      <td>256.21</td>\n",
       "      <td>263.12</td>\n",
       "      <td>3448191.0</td>\n",
       "      <td>1913 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-05</th>\n",
       "      <td>2015-06-05</td>\n",
       "      <td>246.00</td>\n",
       "      <td>249.70</td>\n",
       "      <td>245.68</td>\n",
       "      <td>249.14</td>\n",
       "      <td>3022026.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>246.00</td>\n",
       "      <td>249.70</td>\n",
       "      <td>245.68</td>\n",
       "      <td>249.14</td>\n",
       "      <td>3022026.0</td>\n",
       "      <td>1802 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-04</th>\n",
       "      <td>2015-06-04</td>\n",
       "      <td>247.50</td>\n",
       "      <td>249.30</td>\n",
       "      <td>245.71</td>\n",
       "      <td>245.92</td>\n",
       "      <td>2453615.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>247.50</td>\n",
       "      <td>249.30</td>\n",
       "      <td>245.71</td>\n",
       "      <td>245.92</td>\n",
       "      <td>2453615.0</td>\n",
       "      <td>1801 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-03</th>\n",
       "      <td>2015-06-03</td>\n",
       "      <td>248.20</td>\n",
       "      <td>250.72</td>\n",
       "      <td>247.01</td>\n",
       "      <td>248.99</td>\n",
       "      <td>1781505.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>248.20</td>\n",
       "      <td>250.72</td>\n",
       "      <td>247.01</td>\n",
       "      <td>248.99</td>\n",
       "      <td>1781505.0</td>\n",
       "      <td>1800 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-02</th>\n",
       "      <td>2015-06-02</td>\n",
       "      <td>248.92</td>\n",
       "      <td>249.40</td>\n",
       "      <td>246.30</td>\n",
       "      <td>248.35</td>\n",
       "      <td>2134809.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>248.92</td>\n",
       "      <td>249.40</td>\n",
       "      <td>246.30</td>\n",
       "      <td>248.35</td>\n",
       "      <td>2134809.0</td>\n",
       "      <td>1799 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>2015-06-01</td>\n",
       "      <td>251.41</td>\n",
       "      <td>251.60</td>\n",
       "      <td>247.47</td>\n",
       "      <td>249.45</td>\n",
       "      <td>2505057.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>251.41</td>\n",
       "      <td>251.60</td>\n",
       "      <td>247.47</td>\n",
       "      <td>249.45</td>\n",
       "      <td>2505057.0</td>\n",
       "      <td>1798 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date    Open    High     Low   Close     Volume  ExDividend  \\\n",
       "Date                                                                           \n",
       "2015-09-30 2015-09-30  252.00  252.40  242.34  248.40  4879816.0         0.0   \n",
       "2015-09-29 2015-09-29  250.46  254.73  245.46  246.65  3703154.0         0.0   \n",
       "2015-09-28 2015-09-28  257.35  259.79  246.61  248.43  4881802.0         0.0   \n",
       "2015-09-25 2015-09-25  266.61  266.91  256.15  256.91  3773392.0         0.0   \n",
       "2015-09-24 2015-09-24  259.53  263.45  256.21  263.12  3448191.0         0.0   \n",
       "...               ...     ...     ...     ...     ...        ...         ...   \n",
       "2015-06-05 2015-06-05  246.00  249.70  245.68  249.14  3022026.0         0.0   \n",
       "2015-06-04 2015-06-04  247.50  249.30  245.71  245.92  2453615.0         0.0   \n",
       "2015-06-03 2015-06-03  248.20  250.72  247.01  248.99  1781505.0         0.0   \n",
       "2015-06-02 2015-06-02  248.92  249.40  246.30  248.35  2134809.0         0.0   \n",
       "2015-06-01 2015-06-01  251.41  251.60  247.47  249.45  2505057.0         0.0   \n",
       "\n",
       "            SplitRatio  AdjOpen  AdjHigh  AdjLow  AdjClose  AdjVolume  \\\n",
       "Date                                                                    \n",
       "2015-09-30         1.0   252.00   252.40  242.34    248.40  4879816.0   \n",
       "2015-09-29         1.0   250.46   254.73  245.46    246.65  3703154.0   \n",
       "2015-09-28         1.0   257.35   259.79  246.61    248.43  4881802.0   \n",
       "2015-09-25         1.0   266.61   266.91  256.15    256.91  3773392.0   \n",
       "2015-09-24         1.0   259.53   263.45  256.21    263.12  3448191.0   \n",
       "...                ...      ...      ...     ...       ...        ...   \n",
       "2015-06-05         1.0   246.00   249.70  245.68    249.14  3022026.0   \n",
       "2015-06-04         1.0   247.50   249.30  245.71    245.92  2453615.0   \n",
       "2015-06-03         1.0   248.20   250.72  247.01    248.99  1781505.0   \n",
       "2015-06-02         1.0   248.92   249.40  246.30    248.35  2134809.0   \n",
       "2015-06-01         1.0   251.41   251.60  247.47    249.45  2505057.0   \n",
       "\n",
       "              refdat  \n",
       "Date                  \n",
       "2015-09-30 1919 days  \n",
       "2015-09-29 1918 days  \n",
       "2015-09-28 1917 days  \n",
       "2015-09-25 1914 days  \n",
       "2015-09-24 1913 days  \n",
       "...              ...  \n",
       "2015-06-05 1802 days  \n",
       "2015-06-04 1801 days  \n",
       "2015-06-03 1800 days  \n",
       "2015-06-02 1799 days  \n",
       "2015-06-01 1798 days  \n",
       "\n",
       "[86 rows x 14 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tesla['2015-09':'2015-06']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Cases_Guinea</th>\n",
       "      <th>Cases_Liberia</th>\n",
       "      <th>Cases_SierraLeone</th>\n",
       "      <th>Cases_Nigeria</th>\n",
       "      <th>Cases_Senegal</th>\n",
       "      <th>Cases_UnitedStates</th>\n",
       "      <th>Cases_Spain</th>\n",
       "      <th>Cases_Mali</th>\n",
       "      <th>Deaths_Guinea</th>\n",
       "      <th>Deaths_Liberia</th>\n",
       "      <th>Deaths_SierraLeone</th>\n",
       "      <th>Deaths_Nigeria</th>\n",
       "      <th>Deaths_Senegal</th>\n",
       "      <th>Deaths_UnitedStates</th>\n",
       "      <th>Deaths_Spain</th>\n",
       "      <th>Deaths_Mali</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/5/2015</td>\n",
       "      <td>289</td>\n",
       "      <td>2776.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10030.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1786.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2977.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/4/2015</td>\n",
       "      <td>288</td>\n",
       "      <td>2775.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9780.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1781.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2943.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2015</td>\n",
       "      <td>287</td>\n",
       "      <td>2769.0</td>\n",
       "      <td>8166.0</td>\n",
       "      <td>9722.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1767.0</td>\n",
       "      <td>3496.0</td>\n",
       "      <td>2915.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/2/2015</td>\n",
       "      <td>286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3496.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12/31/2014</td>\n",
       "      <td>284</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>8115.0</td>\n",
       "      <td>9633.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1739.0</td>\n",
       "      <td>3471.0</td>\n",
       "      <td>2827.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone  \\\n",
       "0    1/5/2015  289        2776.0            NaN            10030.0   \n",
       "1    1/4/2015  288        2775.0            NaN             9780.0   \n",
       "2    1/3/2015  287        2769.0         8166.0             9722.0   \n",
       "3    1/2/2015  286           NaN         8157.0                NaN   \n",
       "4  12/31/2014  284        2730.0         8115.0             9633.0   \n",
       "\n",
       "   Cases_Nigeria  Cases_Senegal  Cases_UnitedStates  Cases_Spain  Cases_Mali  \\\n",
       "0            NaN            NaN                 NaN          NaN         NaN   \n",
       "1            NaN            NaN                 NaN          NaN         NaN   \n",
       "2            NaN            NaN                 NaN          NaN         NaN   \n",
       "3            NaN            NaN                 NaN          NaN         NaN   \n",
       "4            NaN            NaN                 NaN          NaN         NaN   \n",
       "\n",
       "   Deaths_Guinea  Deaths_Liberia  Deaths_SierraLeone  Deaths_Nigeria  \\\n",
       "0         1786.0             NaN              2977.0             NaN   \n",
       "1         1781.0             NaN              2943.0             NaN   \n",
       "2         1767.0          3496.0              2915.0             NaN   \n",
       "3            NaN          3496.0                 NaN             NaN   \n",
       "4         1739.0          3471.0              2827.0             NaN   \n",
       "\n",
       "   Deaths_Senegal  Deaths_UnitedStates  Deaths_Spain  Deaths_Mali  \n",
       "0             NaN                  NaN           NaN          NaN  \n",
       "1             NaN                  NaN           NaN          NaN  \n",
       "2             NaN                  NaN           NaN          NaN  \n",
       "3             NaN                  NaN           NaN          NaN  \n",
       "4             NaN                  NaN           NaN          NaN  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebola = pd.read_csv('./time/country_timeseries.csv')\n",
    "ebola5 = ebola.head()\n",
    "ebola5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Cases_Guinea</th>\n",
       "      <th>Cases_Liberia</th>\n",
       "      <th>Cases_SierraLeone</th>\n",
       "      <th>Cases_Nigeria</th>\n",
       "      <th>Cases_Senegal</th>\n",
       "      <th>Cases_UnitedStates</th>\n",
       "      <th>Cases_Spain</th>\n",
       "      <th>Cases_Mali</th>\n",
       "      <th>Deaths_Guinea</th>\n",
       "      <th>Deaths_Liberia</th>\n",
       "      <th>Deaths_SierraLeone</th>\n",
       "      <th>Deaths_Nigeria</th>\n",
       "      <th>Deaths_Senegal</th>\n",
       "      <th>Deaths_UnitedStates</th>\n",
       "      <th>Deaths_Spain</th>\n",
       "      <th>Deaths_Mali</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1/5/2015</th>\n",
       "      <td>1/5/2015</td>\n",
       "      <td>289</td>\n",
       "      <td>2776.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10030.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1786.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2977.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/4/2015</th>\n",
       "      <td>1/4/2015</td>\n",
       "      <td>288</td>\n",
       "      <td>2775.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9780.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1781.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2943.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/3/2015</th>\n",
       "      <td>1/3/2015</td>\n",
       "      <td>287</td>\n",
       "      <td>2769.0</td>\n",
       "      <td>8166.0</td>\n",
       "      <td>9722.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1767.0</td>\n",
       "      <td>3496.0</td>\n",
       "      <td>2915.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/2/2015</th>\n",
       "      <td>1/2/2015</td>\n",
       "      <td>286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8157.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3496.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12/31/2014</th>\n",
       "      <td>12/31/2014</td>\n",
       "      <td>284</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>8115.0</td>\n",
       "      <td>9633.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1739.0</td>\n",
       "      <td>3471.0</td>\n",
       "      <td>2827.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone  \\\n",
       "Date                                                                          \n",
       "1/5/2015      1/5/2015  289        2776.0            NaN            10030.0   \n",
       "1/4/2015      1/4/2015  288        2775.0            NaN             9780.0   \n",
       "1/3/2015      1/3/2015  287        2769.0         8166.0             9722.0   \n",
       "1/2/2015      1/2/2015  286           NaN         8157.0                NaN   \n",
       "12/31/2014  12/31/2014  284        2730.0         8115.0             9633.0   \n",
       "\n",
       "            Cases_Nigeria  Cases_Senegal  Cases_UnitedStates  Cases_Spain  \\\n",
       "Date                                                                        \n",
       "1/5/2015              NaN            NaN                 NaN          NaN   \n",
       "1/4/2015              NaN            NaN                 NaN          NaN   \n",
       "1/3/2015              NaN            NaN                 NaN          NaN   \n",
       "1/2/2015              NaN            NaN                 NaN          NaN   \n",
       "12/31/2014            NaN            NaN                 NaN          NaN   \n",
       "\n",
       "            Cases_Mali  Deaths_Guinea  Deaths_Liberia  Deaths_SierraLeone  \\\n",
       "Date                                                                        \n",
       "1/5/2015           NaN         1786.0             NaN              2977.0   \n",
       "1/4/2015           NaN         1781.0             NaN              2943.0   \n",
       "1/3/2015           NaN         1767.0          3496.0              2915.0   \n",
       "1/2/2015           NaN            NaN          3496.0                 NaN   \n",
       "12/31/2014         NaN         1739.0          3471.0              2827.0   \n",
       "\n",
       "            Deaths_Nigeria  Deaths_Senegal  Deaths_UnitedStates  Deaths_Spain  \\\n",
       "Date                                                                            \n",
       "1/5/2015               NaN             NaN                  NaN           NaN   \n",
       "1/4/2015               NaN             NaN                  NaN           NaN   \n",
       "1/3/2015               NaN             NaN                  NaN           NaN   \n",
       "1/2/2015               NaN             NaN                  NaN           NaN   \n",
       "12/31/2014             NaN             NaN                  NaN           NaN   \n",
       "\n",
       "            Deaths_Mali  \n",
       "Date                     \n",
       "1/5/2015            NaN  \n",
       "1/4/2015            NaN  \n",
       "1/3/2015            NaN  \n",
       "1/2/2015            NaN  \n",
       "12/31/2014          NaN  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebola5.index =  ebola5['Date']\n",
    "ebola5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-12-31', '2015-01-01', '2015-01-02', '2015-01-03',\n",
       "               '2015-01-04', '2015-01-05'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "head_range = pd.date_range(start='2014-12-31', end='2015-01-05')\n",
    "head_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Cases_Guinea</th>\n",
       "      <th>Cases_Liberia</th>\n",
       "      <th>Cases_SierraLeone</th>\n",
       "      <th>Cases_Nigeria</th>\n",
       "      <th>Cases_Senegal</th>\n",
       "      <th>Cases_UnitedStates</th>\n",
       "      <th>Cases_Spain</th>\n",
       "      <th>Cases_Mali</th>\n",
       "      <th>Deaths_Guinea</th>\n",
       "      <th>Deaths_Liberia</th>\n",
       "      <th>Deaths_SierraLeone</th>\n",
       "      <th>Deaths_Nigeria</th>\n",
       "      <th>Deaths_Senegal</th>\n",
       "      <th>Deaths_UnitedStates</th>\n",
       "      <th>Deaths_Spain</th>\n",
       "      <th>Deaths_Mali</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone  \\\n",
       "2014-12-31  NaN  NaN           NaN            NaN                NaN   \n",
       "2015-01-01  NaN  NaN           NaN            NaN                NaN   \n",
       "2015-01-02  NaN  NaN           NaN            NaN                NaN   \n",
       "2015-01-03  NaN  NaN           NaN            NaN                NaN   \n",
       "2015-01-04  NaN  NaN           NaN            NaN                NaN   \n",
       "2015-01-05  NaN  NaN           NaN            NaN                NaN   \n",
       "\n",
       "            Cases_Nigeria  Cases_Senegal  Cases_UnitedStates  Cases_Spain  \\\n",
       "2014-12-31            NaN            NaN                 NaN          NaN   \n",
       "2015-01-01            NaN            NaN                 NaN          NaN   \n",
       "2015-01-02            NaN            NaN                 NaN          NaN   \n",
       "2015-01-03            NaN            NaN                 NaN          NaN   \n",
       "2015-01-04            NaN            NaN                 NaN          NaN   \n",
       "2015-01-05            NaN            NaN                 NaN          NaN   \n",
       "\n",
       "            Cases_Mali  Deaths_Guinea  Deaths_Liberia  Deaths_SierraLeone  \\\n",
       "2014-12-31         NaN            NaN             NaN                 NaN   \n",
       "2015-01-01         NaN            NaN             NaN                 NaN   \n",
       "2015-01-02         NaN            NaN             NaN                 NaN   \n",
       "2015-01-03         NaN            NaN             NaN                 NaN   \n",
       "2015-01-04         NaN            NaN             NaN                 NaN   \n",
       "2015-01-05         NaN            NaN             NaN                 NaN   \n",
       "\n",
       "            Deaths_Nigeria  Deaths_Senegal  Deaths_UnitedStates  Deaths_Spain  \\\n",
       "2014-12-31             NaN             NaN                  NaN           NaN   \n",
       "2015-01-01             NaN             NaN                  NaN           NaN   \n",
       "2015-01-02             NaN             NaN                  NaN           NaN   \n",
       "2015-01-03             NaN             NaN                  NaN           NaN   \n",
       "2015-01-04             NaN             NaN                  NaN           NaN   \n",
       "2015-01-05             NaN             NaN                  NaN           NaN   \n",
       "\n",
       "            Deaths_Mali  \n",
       "2014-12-31          NaN  \n",
       "2015-01-01          NaN  \n",
       "2015-01-02          NaN  \n",
       "2015-01-03          NaN  \n",
       "2015-01-04          NaN  \n",
       "2015-01-05          NaN  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebola5.reindex(head_range)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04', '2017-01-05',\n",
       "               '2017-01-06'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pd.date_range('2017-01-01','2017-01-07', freq='B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "ebola.index = ebola.Date\n",
    "fig, ax = plt.subplots()\n",
    "ax = ebola[::-1].iloc[0:, 10:].plot(ax=ax)\n",
    "ax.legend(fontsize=7, loc=2, borderaxespad=0)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}