final_project_Xiaonan_Yang.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In the past year, Bitcoin has been fluctuated frequently - the highest market price was over USD19,000, the lowest market price was around USD2,000, and there was approximately 6 main fluctuations. In this project, my goal is to find out when the best timing is to buy Bitcoin. To achieve this goal, the mean value of Bitcoin's market price and snapped price will be viuslized. The dataset is download from coingecko.com\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>snapped_at</th>\n",
       "      <th>price</th>\n",
       "      <th>market_cap</th>\n",
       "      <th>total_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-04-28 00:00:00 UTC</td>\n",
       "      <td>135.300000</td>\n",
       "      <td>1.500518e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-04-29 00:00:00 UTC</td>\n",
       "      <td>141.960000</td>\n",
       "      <td>1.575032e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-04-30 00:00:00 UTC</td>\n",
       "      <td>135.300000</td>\n",
       "      <td>1.501657e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-05-01 00:00:00 UTC</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>1.298952e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-05-02 00:00:00 UTC</td>\n",
       "      <td>103.430000</td>\n",
       "      <td>1.148668e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-05-03 00:00:00 UTC</td>\n",
       "      <td>91.010000</td>\n",
       "      <td>1.011066e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013-05-04 00:00:00 UTC</td>\n",
       "      <td>111.250000</td>\n",
       "      <td>1.236352e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-05-05 00:00:00 UTC</td>\n",
       "      <td>116.790000</td>\n",
       "      <td>1.298378e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013-05-06 00:00:00 UTC</td>\n",
       "      <td>118.330000</td>\n",
       "      <td>1.315992e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013-05-07 00:00:00 UTC</td>\n",
       "      <td>106.400000</td>\n",
       "      <td>1.183766e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013-05-08 00:00:00 UTC</td>\n",
       "      <td>112.640000</td>\n",
       "      <td>1.253655e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013-05-09 00:00:00 UTC</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>1.258131e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013-05-10 00:00:00 UTC</td>\n",
       "      <td>118.780000</td>\n",
       "      <td>1.322989e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013-05-11 00:00:00 UTC</td>\n",
       "      <td>113.010000</td>\n",
       "      <td>1.259239e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013-05-12 00:00:00 UTC</td>\n",
       "      <td>114.713000</td>\n",
       "      <td>1.278709e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013-05-13 00:00:00 UTC</td>\n",
       "      <td>117.180000</td>\n",
       "      <td>1.306721e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013-05-14 00:00:00 UTC</td>\n",
       "      <td>114.500000</td>\n",
       "      <td>1.277279e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2013-05-15 00:00:00 UTC</td>\n",
       "      <td>114.156000</td>\n",
       "      <td>1.273850e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013-05-16 00:00:00 UTC</td>\n",
       "      <td>115.500000</td>\n",
       "      <td>1.289277e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013-05-17 00:00:00 UTC</td>\n",
       "      <td>123.100000</td>\n",
       "      <td>1.374605e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013-05-18 00:00:00 UTC</td>\n",
       "      <td>123.880000</td>\n",
       "      <td>1.383755e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013-05-19 00:00:00 UTC</td>\n",
       "      <td>120.501000</td>\n",
       "      <td>1.346499e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013-05-20 00:00:00 UTC</td>\n",
       "      <td>122.580000</td>\n",
       "      <td>1.370132e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013-05-21 00:00:00 UTC</td>\n",
       "      <td>122.900000</td>\n",
       "      <td>1.374154e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013-05-22 00:00:00 UTC</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>1.375792e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2013-05-23 00:00:00 UTC</td>\n",
       "      <td>125.748000</td>\n",
       "      <td>1.407042e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2013-05-24 00:00:00 UTC</td>\n",
       "      <td>131.700000</td>\n",
       "      <td>1.474181e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2013-05-25 00:00:00 UTC</td>\n",
       "      <td>130.770000</td>\n",
       "      <td>1.464317e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2013-05-26 00:00:00 UTC</td>\n",
       "      <td>134.600000</td>\n",
       "      <td>1.507769e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2013-05-27 00:00:00 UTC</td>\n",
       "      <td>128.985000</td>\n",
       "      <td>1.445445e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860</th>\n",
       "      <td>2018-06-03 00:00:00 UTC</td>\n",
       "      <td>7567.512938</td>\n",
       "      <td>1.291850e+11</td>\n",
       "      <td>1.531230e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1861</th>\n",
       "      <td>2018-06-04 00:00:00 UTC</td>\n",
       "      <td>7639.879446</td>\n",
       "      <td>1.304361e+11</td>\n",
       "      <td>1.432775e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>2018-06-05 00:00:00 UTC</td>\n",
       "      <td>7463.654996</td>\n",
       "      <td>1.274444e+11</td>\n",
       "      <td>1.643489e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>2018-06-06 00:00:00 UTC</td>\n",
       "      <td>7569.011340</td>\n",
       "      <td>1.292600e+11</td>\n",
       "      <td>1.754014e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1864</th>\n",
       "      <td>2018-06-07 00:00:00 UTC</td>\n",
       "      <td>7605.578489</td>\n",
       "      <td>1.298984e+11</td>\n",
       "      <td>1.562706e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>2018-06-08 00:00:00 UTC</td>\n",
       "      <td>7613.777393</td>\n",
       "      <td>1.300521e+11</td>\n",
       "      <td>1.459895e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>2018-06-09 00:00:00 UTC</td>\n",
       "      <td>7561.166101</td>\n",
       "      <td>1.291671e+11</td>\n",
       "      <td>1.362045e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1867</th>\n",
       "      <td>2018-06-10 00:00:00 UTC</td>\n",
       "      <td>7415.001379</td>\n",
       "      <td>1.266835e+11</td>\n",
       "      <td>1.102263e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1868</th>\n",
       "      <td>2018-06-11 00:00:00 UTC</td>\n",
       "      <td>6767.499485</td>\n",
       "      <td>1.156319e+11</td>\n",
       "      <td>2.788498e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1869</th>\n",
       "      <td>2018-06-12 00:00:00 UTC</td>\n",
       "      <td>6880.133649</td>\n",
       "      <td>1.175681e+11</td>\n",
       "      <td>1.973449e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1870</th>\n",
       "      <td>2018-06-13 00:00:00 UTC</td>\n",
       "      <td>6581.828075</td>\n",
       "      <td>1.124829e+11</td>\n",
       "      <td>2.060764e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>2018-06-14 00:00:00 UTC</td>\n",
       "      <td>6334.098906</td>\n",
       "      <td>1.082602e+11</td>\n",
       "      <td>2.571113e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>2018-06-15 00:00:00 UTC</td>\n",
       "      <td>6644.720680</td>\n",
       "      <td>1.135824e+11</td>\n",
       "      <td>2.354179e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1873</th>\n",
       "      <td>2018-06-16 00:00:00 UTC</td>\n",
       "      <td>6406.807050</td>\n",
       "      <td>1.095277e+11</td>\n",
       "      <td>1.740753e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>2018-06-17 00:00:00 UTC</td>\n",
       "      <td>6478.815924</td>\n",
       "      <td>1.107722e+11</td>\n",
       "      <td>1.306649e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>2018-06-18 00:00:00 UTC</td>\n",
       "      <td>6418.850002</td>\n",
       "      <td>1.097596e+11</td>\n",
       "      <td>1.120786e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1876</th>\n",
       "      <td>2018-06-19 00:00:00 UTC</td>\n",
       "      <td>6677.533403</td>\n",
       "      <td>1.141965e+11</td>\n",
       "      <td>1.750367e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>2018-06-20 00:00:00 UTC</td>\n",
       "      <td>6708.681820</td>\n",
       "      <td>1.147422e+11</td>\n",
       "      <td>1.649450e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1878</th>\n",
       "      <td>2018-06-21 00:00:00 UTC</td>\n",
       "      <td>6714.375461</td>\n",
       "      <td>1.148525e+11</td>\n",
       "      <td>1.626421e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1879</th>\n",
       "      <td>2018-06-22 00:00:00 UTC</td>\n",
       "      <td>6690.832152</td>\n",
       "      <td>1.144639e+11</td>\n",
       "      <td>1.400178e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>2018-06-23 00:00:00 UTC</td>\n",
       "      <td>6088.579442</td>\n",
       "      <td>1.041724e+11</td>\n",
       "      <td>2.932411e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>2018-06-24 00:00:00 UTC</td>\n",
       "      <td>6169.437264</td>\n",
       "      <td>1.055683e+11</td>\n",
       "      <td>1.635693e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>2018-06-25 00:00:00 UTC</td>\n",
       "      <td>6159.707194</td>\n",
       "      <td>1.054151e+11</td>\n",
       "      <td>2.452039e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>2018-06-26 00:00:00 UTC</td>\n",
       "      <td>6241.848712</td>\n",
       "      <td>1.068327e+11</td>\n",
       "      <td>4.326985e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>2018-06-27 00:00:00 UTC</td>\n",
       "      <td>6069.872606</td>\n",
       "      <td>1.039006e+11</td>\n",
       "      <td>3.248018e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td>2018-06-28 00:00:00 UTC</td>\n",
       "      <td>6117.036410</td>\n",
       "      <td>1.047172e+11</td>\n",
       "      <td>2.057222e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <td>2018-06-29 00:00:00 UTC</td>\n",
       "      <td>5879.988974</td>\n",
       "      <td>1.006708e+11</td>\n",
       "      <td>1.928321e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <td>2018-06-30 00:00:00 UTC</td>\n",
       "      <td>6182.256748</td>\n",
       "      <td>1.058574e+11</td>\n",
       "      <td>2.341619e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>2018-07-01 00:00:00 UTC</td>\n",
       "      <td>6366.499468</td>\n",
       "      <td>1.090234e+11</td>\n",
       "      <td>2.086843e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>2018-07-02 00:00:00 UTC</td>\n",
       "      <td>6312.877243</td>\n",
       "      <td>1.081167e+11</td>\n",
       "      <td>2.601786e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1890 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   snapped_at        price    market_cap  total_volume\n",
       "0     2013-04-28 00:00:00 UTC   135.300000  1.500518e+09  0.000000e+00\n",
       "1     2013-04-29 00:00:00 UTC   141.960000  1.575032e+09  0.000000e+00\n",
       "2     2013-04-30 00:00:00 UTC   135.300000  1.501657e+09  0.000000e+00\n",
       "3     2013-05-01 00:00:00 UTC   117.000000  1.298952e+09  0.000000e+00\n",
       "4     2013-05-02 00:00:00 UTC   103.430000  1.148668e+09  0.000000e+00\n",
       "5     2013-05-03 00:00:00 UTC    91.010000  1.011066e+09  0.000000e+00\n",
       "6     2013-05-04 00:00:00 UTC   111.250000  1.236352e+09  0.000000e+00\n",
       "7     2013-05-05 00:00:00 UTC   116.790000  1.298378e+09  0.000000e+00\n",
       "8     2013-05-06 00:00:00 UTC   118.330000  1.315992e+09  0.000000e+00\n",
       "9     2013-05-07 00:00:00 UTC   106.400000  1.183766e+09  0.000000e+00\n",
       "10    2013-05-08 00:00:00 UTC   112.640000  1.253655e+09  0.000000e+00\n",
       "11    2013-05-09 00:00:00 UTC   113.000000  1.258131e+09  0.000000e+00\n",
       "12    2013-05-10 00:00:00 UTC   118.780000  1.322989e+09  0.000000e+00\n",
       "13    2013-05-11 00:00:00 UTC   113.010000  1.259239e+09  0.000000e+00\n",
       "14    2013-05-12 00:00:00 UTC   114.713000  1.278709e+09  0.000000e+00\n",
       "15    2013-05-13 00:00:00 UTC   117.180000  1.306721e+09  0.000000e+00\n",
       "16    2013-05-14 00:00:00 UTC   114.500000  1.277279e+09  0.000000e+00\n",
       "17    2013-05-15 00:00:00 UTC   114.156000  1.273850e+09  0.000000e+00\n",
       "18    2013-05-16 00:00:00 UTC   115.500000  1.289277e+09  0.000000e+00\n",
       "19    2013-05-17 00:00:00 UTC   123.100000  1.374605e+09  0.000000e+00\n",
       "20    2013-05-18 00:00:00 UTC   123.880000  1.383755e+09  0.000000e+00\n",
       "21    2013-05-19 00:00:00 UTC   120.501000  1.346499e+09  0.000000e+00\n",
       "22    2013-05-20 00:00:00 UTC   122.580000  1.370132e+09  0.000000e+00\n",
       "23    2013-05-21 00:00:00 UTC   122.900000  1.374154e+09  0.000000e+00\n",
       "24    2013-05-22 00:00:00 UTC   123.000000  1.375792e+09  0.000000e+00\n",
       "25    2013-05-23 00:00:00 UTC   125.748000  1.407042e+09  0.000000e+00\n",
       "26    2013-05-24 00:00:00 UTC   131.700000  1.474181e+09  0.000000e+00\n",
       "27    2013-05-25 00:00:00 UTC   130.770000  1.464317e+09  0.000000e+00\n",
       "28    2013-05-26 00:00:00 UTC   134.600000  1.507769e+09  0.000000e+00\n",
       "29    2013-05-27 00:00:00 UTC   128.985000  1.445445e+09  0.000000e+00\n",
       "...                       ...          ...           ...           ...\n",
       "1860  2018-06-03 00:00:00 UTC  7567.512938  1.291850e+11  1.531230e+09\n",
       "1861  2018-06-04 00:00:00 UTC  7639.879446  1.304361e+11  1.432775e+09\n",
       "1862  2018-06-05 00:00:00 UTC  7463.654996  1.274444e+11  1.643489e+09\n",
       "1863  2018-06-06 00:00:00 UTC  7569.011340  1.292600e+11  1.754014e+09\n",
       "1864  2018-06-07 00:00:00 UTC  7605.578489  1.298984e+11  1.562706e+09\n",
       "1865  2018-06-08 00:00:00 UTC  7613.777393  1.300521e+11  1.459895e+09\n",
       "1866  2018-06-09 00:00:00 UTC  7561.166101  1.291671e+11  1.362045e+09\n",
       "1867  2018-06-10 00:00:00 UTC  7415.001379  1.266835e+11  1.102263e+09\n",
       "1868  2018-06-11 00:00:00 UTC  6767.499485  1.156319e+11  2.788498e+09\n",
       "1869  2018-06-12 00:00:00 UTC  6880.133649  1.175681e+11  1.973449e+09\n",
       "1870  2018-06-13 00:00:00 UTC  6581.828075  1.124829e+11  2.060764e+09\n",
       "1871  2018-06-14 00:00:00 UTC  6334.098906  1.082602e+11  2.571113e+09\n",
       "1872  2018-06-15 00:00:00 UTC  6644.720680  1.135824e+11  2.354179e+09\n",
       "1873  2018-06-16 00:00:00 UTC  6406.807050  1.095277e+11  1.740753e+09\n",
       "1874  2018-06-17 00:00:00 UTC  6478.815924  1.107722e+11  1.306649e+09\n",
       "1875  2018-06-18 00:00:00 UTC  6418.850002  1.097596e+11  1.120786e+09\n",
       "1876  2018-06-19 00:00:00 UTC  6677.533403  1.141965e+11  1.750367e+09\n",
       "1877  2018-06-20 00:00:00 UTC  6708.681820  1.147422e+11  1.649450e+09\n",
       "1878  2018-06-21 00:00:00 UTC  6714.375461  1.148525e+11  1.626421e+09\n",
       "1879  2018-06-22 00:00:00 UTC  6690.832152  1.144639e+11  1.400178e+09\n",
       "1880  2018-06-23 00:00:00 UTC  6088.579442  1.041724e+11  2.932411e+09\n",
       "1881  2018-06-24 00:00:00 UTC  6169.437264  1.055683e+11  1.635693e+09\n",
       "1882  2018-06-25 00:00:00 UTC  6159.707194  1.054151e+11  2.452039e+09\n",
       "1883  2018-06-26 00:00:00 UTC  6241.848712  1.068327e+11  4.326985e+09\n",
       "1884  2018-06-27 00:00:00 UTC  6069.872606  1.039006e+11  3.248018e+09\n",
       "1885  2018-06-28 00:00:00 UTC  6117.036410  1.047172e+11  2.057222e+09\n",
       "1886  2018-06-29 00:00:00 UTC  5879.988974  1.006708e+11  1.928321e+09\n",
       "1887  2018-06-30 00:00:00 UTC  6182.256748  1.058574e+11  2.341619e+09\n",
       "1888  2018-07-01 00:00:00 UTC  6366.499468  1.090234e+11  2.086843e+09\n",
       "1889  2018-07-02 00:00:00 UTC  6312.877243  1.081167e+11  2.601786e+09\n",
       "\n",
       "[1890 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import the data set\n",
    "data = pd.read_csv(r'C:\\Users\\seany\\OneDrive\\Documents\\btc_usd.csv')\n",
    "#create a dataframe of this dataset\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>snapped_at</th>\n",
       "      <th>price</th>\n",
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       "      <th>2013-04-28 00:00:00 UTC</th>\n",
       "      <td>2013-04-28 00:00:00 UTC</td>\n",
       "      <td>135.300000</td>\n",
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       "    <tr>\n",
       "      <th>2013-04-29 00:00:00 UTC</th>\n",
       "      <td>2013-04-29 00:00:00 UTC</td>\n",
       "      <td>141.960000</td>\n",
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       "    <tr>\n",
       "      <th>2013-04-30 00:00:00 UTC</th>\n",
       "      <td>2013-04-30 00:00:00 UTC</td>\n",
       "      <td>135.300000</td>\n",
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       "      <th>2013-05-01 00:00:00 UTC</th>\n",
       "      <td>2013-05-01 00:00:00 UTC</td>\n",
       "      <td>117.000000</td>\n",
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       "    <tr>\n",
       "      <th>2013-05-02 00:00:00 UTC</th>\n",
       "      <td>2013-05-02 00:00:00 UTC</td>\n",
       "      <td>103.430000</td>\n",
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       "    <tr>\n",
       "      <th>2013-05-03 00:00:00 UTC</th>\n",
       "      <td>2013-05-03 00:00:00 UTC</td>\n",
       "      <td>91.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-04 00:00:00 UTC</th>\n",
       "      <td>2013-05-04 00:00:00 UTC</td>\n",
       "      <td>111.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-05 00:00:00 UTC</th>\n",
       "      <td>2013-05-05 00:00:00 UTC</td>\n",
       "      <td>116.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-06 00:00:00 UTC</th>\n",
       "      <td>2013-05-06 00:00:00 UTC</td>\n",
       "      <td>118.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-07 00:00:00 UTC</th>\n",
       "      <td>2013-05-07 00:00:00 UTC</td>\n",
       "      <td>106.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-08 00:00:00 UTC</th>\n",
       "      <td>2013-05-08 00:00:00 UTC</td>\n",
       "      <td>112.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-09 00:00:00 UTC</th>\n",
       "      <td>2013-05-09 00:00:00 UTC</td>\n",
       "      <td>113.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-10 00:00:00 UTC</th>\n",
       "      <td>2013-05-10 00:00:00 UTC</td>\n",
       "      <td>118.780000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-11 00:00:00 UTC</th>\n",
       "      <td>2013-05-11 00:00:00 UTC</td>\n",
       "      <td>113.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-12 00:00:00 UTC</th>\n",
       "      <td>2013-05-12 00:00:00 UTC</td>\n",
       "      <td>114.713000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-13 00:00:00 UTC</th>\n",
       "      <td>2013-05-13 00:00:00 UTC</td>\n",
       "      <td>117.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-14 00:00:00 UTC</th>\n",
       "      <td>2013-05-14 00:00:00 UTC</td>\n",
       "      <td>114.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-15 00:00:00 UTC</th>\n",
       "      <td>2013-05-15 00:00:00 UTC</td>\n",
       "      <td>114.156000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-16 00:00:00 UTC</th>\n",
       "      <td>2013-05-16 00:00:00 UTC</td>\n",
       "      <td>115.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-17 00:00:00 UTC</th>\n",
       "      <td>2013-05-17 00:00:00 UTC</td>\n",
       "      <td>123.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-18 00:00:00 UTC</th>\n",
       "      <td>2013-05-18 00:00:00 UTC</td>\n",
       "      <td>123.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-19 00:00:00 UTC</th>\n",
       "      <td>2013-05-19 00:00:00 UTC</td>\n",
       "      <td>120.501000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-20 00:00:00 UTC</th>\n",
       "      <td>2013-05-20 00:00:00 UTC</td>\n",
       "      <td>122.580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-21 00:00:00 UTC</th>\n",
       "      <td>2013-05-21 00:00:00 UTC</td>\n",
       "      <td>122.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-22 00:00:00 UTC</th>\n",
       "      <td>2013-05-22 00:00:00 UTC</td>\n",
       "      <td>123.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-23 00:00:00 UTC</th>\n",
       "      <td>2013-05-23 00:00:00 UTC</td>\n",
       "      <td>125.748000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-24 00:00:00 UTC</th>\n",
       "      <td>2013-05-24 00:00:00 UTC</td>\n",
       "      <td>131.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-25 00:00:00 UTC</th>\n",
       "      <td>2013-05-25 00:00:00 UTC</td>\n",
       "      <td>130.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-26 00:00:00 UTC</th>\n",
       "      <td>2013-05-26 00:00:00 UTC</td>\n",
       "      <td>134.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-27 00:00:00 UTC</th>\n",
       "      <td>2013-05-27 00:00:00 UTC</td>\n",
       "      <td>128.985000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-03 00:00:00 UTC</th>\n",
       "      <td>2018-06-03 00:00:00 UTC</td>\n",
       "      <td>7567.512938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-04 00:00:00 UTC</th>\n",
       "      <td>2018-06-04 00:00:00 UTC</td>\n",
       "      <td>7639.879446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-05 00:00:00 UTC</th>\n",
       "      <td>2018-06-05 00:00:00 UTC</td>\n",
       "      <td>7463.654996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-06 00:00:00 UTC</th>\n",
       "      <td>2018-06-06 00:00:00 UTC</td>\n",
       "      <td>7569.011340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-07 00:00:00 UTC</th>\n",
       "      <td>2018-06-07 00:00:00 UTC</td>\n",
       "      <td>7605.578489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-08 00:00:00 UTC</th>\n",
       "      <td>2018-06-08 00:00:00 UTC</td>\n",
       "      <td>7613.777393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-09 00:00:00 UTC</th>\n",
       "      <td>2018-06-09 00:00:00 UTC</td>\n",
       "      <td>7561.166101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-10 00:00:00 UTC</th>\n",
       "      <td>2018-06-10 00:00:00 UTC</td>\n",
       "      <td>7415.001379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-11 00:00:00 UTC</th>\n",
       "      <td>2018-06-11 00:00:00 UTC</td>\n",
       "      <td>6767.499485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-12 00:00:00 UTC</th>\n",
       "      <td>2018-06-12 00:00:00 UTC</td>\n",
       "      <td>6880.133649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-13 00:00:00 UTC</th>\n",
       "      <td>2018-06-13 00:00:00 UTC</td>\n",
       "      <td>6581.828075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-14 00:00:00 UTC</th>\n",
       "      <td>2018-06-14 00:00:00 UTC</td>\n",
       "      <td>6334.098906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-15 00:00:00 UTC</th>\n",
       "      <td>2018-06-15 00:00:00 UTC</td>\n",
       "      <td>6644.720680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-16 00:00:00 UTC</th>\n",
       "      <td>2018-06-16 00:00:00 UTC</td>\n",
       "      <td>6406.807050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-17 00:00:00 UTC</th>\n",
       "      <td>2018-06-17 00:00:00 UTC</td>\n",
       "      <td>6478.815924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-18 00:00:00 UTC</th>\n",
       "      <td>2018-06-18 00:00:00 UTC</td>\n",
       "      <td>6418.850002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-19 00:00:00 UTC</th>\n",
       "      <td>2018-06-19 00:00:00 UTC</td>\n",
       "      <td>6677.533403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-20 00:00:00 UTC</th>\n",
       "      <td>2018-06-20 00:00:00 UTC</td>\n",
       "      <td>6708.681820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-21 00:00:00 UTC</th>\n",
       "      <td>2018-06-21 00:00:00 UTC</td>\n",
       "      <td>6714.375461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-22 00:00:00 UTC</th>\n",
       "      <td>2018-06-22 00:00:00 UTC</td>\n",
       "      <td>6690.832152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-23 00:00:00 UTC</th>\n",
       "      <td>2018-06-23 00:00:00 UTC</td>\n",
       "      <td>6088.579442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-24 00:00:00 UTC</th>\n",
       "      <td>2018-06-24 00:00:00 UTC</td>\n",
       "      <td>6169.437264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-25 00:00:00 UTC</th>\n",
       "      <td>2018-06-25 00:00:00 UTC</td>\n",
       "      <td>6159.707194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-26 00:00:00 UTC</th>\n",
       "      <td>2018-06-26 00:00:00 UTC</td>\n",
       "      <td>6241.848712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-27 00:00:00 UTC</th>\n",
       "      <td>2018-06-27 00:00:00 UTC</td>\n",
       "      <td>6069.872606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-28 00:00:00 UTC</th>\n",
       "      <td>2018-06-28 00:00:00 UTC</td>\n",
       "      <td>6117.036410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29 00:00:00 UTC</th>\n",
       "      <td>2018-06-29 00:00:00 UTC</td>\n",
       "      <td>5879.988974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30 00:00:00 UTC</th>\n",
       "      <td>2018-06-30 00:00:00 UTC</td>\n",
       "      <td>6182.256748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01 00:00:00 UTC</th>\n",
       "      <td>2018-07-01 00:00:00 UTC</td>\n",
       "      <td>6366.499468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-02 00:00:00 UTC</th>\n",
       "      <td>2018-07-02 00:00:00 UTC</td>\n",
       "      <td>6312.877243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1890 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      snapped_at        price\n",
       "snapped_at                                                   \n",
       "2013-04-28 00:00:00 UTC  2013-04-28 00:00:00 UTC   135.300000\n",
       "2013-04-29 00:00:00 UTC  2013-04-29 00:00:00 UTC   141.960000\n",
       "2013-04-30 00:00:00 UTC  2013-04-30 00:00:00 UTC   135.300000\n",
       "2013-05-01 00:00:00 UTC  2013-05-01 00:00:00 UTC   117.000000\n",
       "2013-05-02 00:00:00 UTC  2013-05-02 00:00:00 UTC   103.430000\n",
       "2013-05-03 00:00:00 UTC  2013-05-03 00:00:00 UTC    91.010000\n",
       "2013-05-04 00:00:00 UTC  2013-05-04 00:00:00 UTC   111.250000\n",
       "2013-05-05 00:00:00 UTC  2013-05-05 00:00:00 UTC   116.790000\n",
       "2013-05-06 00:00:00 UTC  2013-05-06 00:00:00 UTC   118.330000\n",
       "2013-05-07 00:00:00 UTC  2013-05-07 00:00:00 UTC   106.400000\n",
       "2013-05-08 00:00:00 UTC  2013-05-08 00:00:00 UTC   112.640000\n",
       "2013-05-09 00:00:00 UTC  2013-05-09 00:00:00 UTC   113.000000\n",
       "2013-05-10 00:00:00 UTC  2013-05-10 00:00:00 UTC   118.780000\n",
       "2013-05-11 00:00:00 UTC  2013-05-11 00:00:00 UTC   113.010000\n",
       "2013-05-12 00:00:00 UTC  2013-05-12 00:00:00 UTC   114.713000\n",
       "2013-05-13 00:00:00 UTC  2013-05-13 00:00:00 UTC   117.180000\n",
       "2013-05-14 00:00:00 UTC  2013-05-14 00:00:00 UTC   114.500000\n",
       "2013-05-15 00:00:00 UTC  2013-05-15 00:00:00 UTC   114.156000\n",
       "2013-05-16 00:00:00 UTC  2013-05-16 00:00:00 UTC   115.500000\n",
       "2013-05-17 00:00:00 UTC  2013-05-17 00:00:00 UTC   123.100000\n",
       "2013-05-18 00:00:00 UTC  2013-05-18 00:00:00 UTC   123.880000\n",
       "2013-05-19 00:00:00 UTC  2013-05-19 00:00:00 UTC   120.501000\n",
       "2013-05-20 00:00:00 UTC  2013-05-20 00:00:00 UTC   122.580000\n",
       "2013-05-21 00:00:00 UTC  2013-05-21 00:00:00 UTC   122.900000\n",
       "2013-05-22 00:00:00 UTC  2013-05-22 00:00:00 UTC   123.000000\n",
       "2013-05-23 00:00:00 UTC  2013-05-23 00:00:00 UTC   125.748000\n",
       "2013-05-24 00:00:00 UTC  2013-05-24 00:00:00 UTC   131.700000\n",
       "2013-05-25 00:00:00 UTC  2013-05-25 00:00:00 UTC   130.770000\n",
       "2013-05-26 00:00:00 UTC  2013-05-26 00:00:00 UTC   134.600000\n",
       "2013-05-27 00:00:00 UTC  2013-05-27 00:00:00 UTC   128.985000\n",
       "...                                          ...          ...\n",
       "2018-06-03 00:00:00 UTC  2018-06-03 00:00:00 UTC  7567.512938\n",
       "2018-06-04 00:00:00 UTC  2018-06-04 00:00:00 UTC  7639.879446\n",
       "2018-06-05 00:00:00 UTC  2018-06-05 00:00:00 UTC  7463.654996\n",
       "2018-06-06 00:00:00 UTC  2018-06-06 00:00:00 UTC  7569.011340\n",
       "2018-06-07 00:00:00 UTC  2018-06-07 00:00:00 UTC  7605.578489\n",
       "2018-06-08 00:00:00 UTC  2018-06-08 00:00:00 UTC  7613.777393\n",
       "2018-06-09 00:00:00 UTC  2018-06-09 00:00:00 UTC  7561.166101\n",
       "2018-06-10 00:00:00 UTC  2018-06-10 00:00:00 UTC  7415.001379\n",
       "2018-06-11 00:00:00 UTC  2018-06-11 00:00:00 UTC  6767.499485\n",
       "2018-06-12 00:00:00 UTC  2018-06-12 00:00:00 UTC  6880.133649\n",
       "2018-06-13 00:00:00 UTC  2018-06-13 00:00:00 UTC  6581.828075\n",
       "2018-06-14 00:00:00 UTC  2018-06-14 00:00:00 UTC  6334.098906\n",
       "2018-06-15 00:00:00 UTC  2018-06-15 00:00:00 UTC  6644.720680\n",
       "2018-06-16 00:00:00 UTC  2018-06-16 00:00:00 UTC  6406.807050\n",
       "2018-06-17 00:00:00 UTC  2018-06-17 00:00:00 UTC  6478.815924\n",
       "2018-06-18 00:00:00 UTC  2018-06-18 00:00:00 UTC  6418.850002\n",
       "2018-06-19 00:00:00 UTC  2018-06-19 00:00:00 UTC  6677.533403\n",
       "2018-06-20 00:00:00 UTC  2018-06-20 00:00:00 UTC  6708.681820\n",
       "2018-06-21 00:00:00 UTC  2018-06-21 00:00:00 UTC  6714.375461\n",
       "2018-06-22 00:00:00 UTC  2018-06-22 00:00:00 UTC  6690.832152\n",
       "2018-06-23 00:00:00 UTC  2018-06-23 00:00:00 UTC  6088.579442\n",
       "2018-06-24 00:00:00 UTC  2018-06-24 00:00:00 UTC  6169.437264\n",
       "2018-06-25 00:00:00 UTC  2018-06-25 00:00:00 UTC  6159.707194\n",
       "2018-06-26 00:00:00 UTC  2018-06-26 00:00:00 UTC  6241.848712\n",
       "2018-06-27 00:00:00 UTC  2018-06-27 00:00:00 UTC  6069.872606\n",
       "2018-06-28 00:00:00 UTC  2018-06-28 00:00:00 UTC  6117.036410\n",
       "2018-06-29 00:00:00 UTC  2018-06-29 00:00:00 UTC  5879.988974\n",
       "2018-06-30 00:00:00 UTC  2018-06-30 00:00:00 UTC  6182.256748\n",
       "2018-07-01 00:00:00 UTC  2018-07-01 00:00:00 UTC  6366.499468\n",
       "2018-07-02 00:00:00 UTC  2018-07-02 00:00:00 UTC  6312.877243\n",
       "\n",
       "[1890 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#two of clomuns in this dataset is useless for this project, so I deleted them from the dataframe\n",
    "df.drop(columns=['market_cap','total_volume'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of                                       snapped_at        price    market_cap  \\\n",
       "snapped_at                                                                    \n",
       "2013-04-28 00:00:00 UTC  2013-04-28 00:00:00 UTC   135.300000  1.500518e+09   \n",
       "2013-04-29 00:00:00 UTC  2013-04-29 00:00:00 UTC   141.960000  1.575032e+09   \n",
       "2013-04-30 00:00:00 UTC  2013-04-30 00:00:00 UTC   135.300000  1.501657e+09   \n",
       "2013-05-01 00:00:00 UTC  2013-05-01 00:00:00 UTC   117.000000  1.298952e+09   \n",
       "2013-05-02 00:00:00 UTC  2013-05-02 00:00:00 UTC   103.430000  1.148668e+09   \n",
       "2013-05-03 00:00:00 UTC  2013-05-03 00:00:00 UTC    91.010000  1.011066e+09   \n",
       "2013-05-04 00:00:00 UTC  2013-05-04 00:00:00 UTC   111.250000  1.236352e+09   \n",
       "2013-05-05 00:00:00 UTC  2013-05-05 00:00:00 UTC   116.790000  1.298378e+09   \n",
       "2013-05-06 00:00:00 UTC  2013-05-06 00:00:00 UTC   118.330000  1.315992e+09   \n",
       "2013-05-07 00:00:00 UTC  2013-05-07 00:00:00 UTC   106.400000  1.183766e+09   \n",
       "2013-05-08 00:00:00 UTC  2013-05-08 00:00:00 UTC   112.640000  1.253655e+09   \n",
       "2013-05-09 00:00:00 UTC  2013-05-09 00:00:00 UTC   113.000000  1.258131e+09   \n",
       "2013-05-10 00:00:00 UTC  2013-05-10 00:00:00 UTC   118.780000  1.322989e+09   \n",
       "2013-05-11 00:00:00 UTC  2013-05-11 00:00:00 UTC   113.010000  1.259239e+09   \n",
       "2013-05-12 00:00:00 UTC  2013-05-12 00:00:00 UTC   114.713000  1.278709e+09   \n",
       "2013-05-13 00:00:00 UTC  2013-05-13 00:00:00 UTC   117.180000  1.306721e+09   \n",
       "2013-05-14 00:00:00 UTC  2013-05-14 00:00:00 UTC   114.500000  1.277279e+09   \n",
       "2013-05-15 00:00:00 UTC  2013-05-15 00:00:00 UTC   114.156000  1.273850e+09   \n",
       "2013-05-16 00:00:00 UTC  2013-05-16 00:00:00 UTC   115.500000  1.289277e+09   \n",
       "2013-05-17 00:00:00 UTC  2013-05-17 00:00:00 UTC   123.100000  1.374605e+09   \n",
       "2013-05-18 00:00:00 UTC  2013-05-18 00:00:00 UTC   123.880000  1.383755e+09   \n",
       "2013-05-19 00:00:00 UTC  2013-05-19 00:00:00 UTC   120.501000  1.346499e+09   \n",
       "2013-05-20 00:00:00 UTC  2013-05-20 00:00:00 UTC   122.580000  1.370132e+09   \n",
       "2013-05-21 00:00:00 UTC  2013-05-21 00:00:00 UTC   122.900000  1.374154e+09   \n",
       "2013-05-22 00:00:00 UTC  2013-05-22 00:00:00 UTC   123.000000  1.375792e+09   \n",
       "2013-05-23 00:00:00 UTC  2013-05-23 00:00:00 UTC   125.748000  1.407042e+09   \n",
       "2013-05-24 00:00:00 UTC  2013-05-24 00:00:00 UTC   131.700000  1.474181e+09   \n",
       "2013-05-25 00:00:00 UTC  2013-05-25 00:00:00 UTC   130.770000  1.464317e+09   \n",
       "2013-05-26 00:00:00 UTC  2013-05-26 00:00:00 UTC   134.600000  1.507769e+09   \n",
       "2013-05-27 00:00:00 UTC  2013-05-27 00:00:00 UTC   128.985000  1.445445e+09   \n",
       "...                                          ...          ...           ...   \n",
       "2018-06-03 00:00:00 UTC  2018-06-03 00:00:00 UTC  7567.512938  1.291850e+11   \n",
       "2018-06-04 00:00:00 UTC  2018-06-04 00:00:00 UTC  7639.879446  1.304361e+11   \n",
       "2018-06-05 00:00:00 UTC  2018-06-05 00:00:00 UTC  7463.654996  1.274444e+11   \n",
       "2018-06-06 00:00:00 UTC  2018-06-06 00:00:00 UTC  7569.011340  1.292600e+11   \n",
       "2018-06-07 00:00:00 UTC  2018-06-07 00:00:00 UTC  7605.578489  1.298984e+11   \n",
       "2018-06-08 00:00:00 UTC  2018-06-08 00:00:00 UTC  7613.777393  1.300521e+11   \n",
       "2018-06-09 00:00:00 UTC  2018-06-09 00:00:00 UTC  7561.166101  1.291671e+11   \n",
       "2018-06-10 00:00:00 UTC  2018-06-10 00:00:00 UTC  7415.001379  1.266835e+11   \n",
       "2018-06-11 00:00:00 UTC  2018-06-11 00:00:00 UTC  6767.499485  1.156319e+11   \n",
       "2018-06-12 00:00:00 UTC  2018-06-12 00:00:00 UTC  6880.133649  1.175681e+11   \n",
       "2018-06-13 00:00:00 UTC  2018-06-13 00:00:00 UTC  6581.828075  1.124829e+11   \n",
       "2018-06-14 00:00:00 UTC  2018-06-14 00:00:00 UTC  6334.098906  1.082602e+11   \n",
       "2018-06-15 00:00:00 UTC  2018-06-15 00:00:00 UTC  6644.720680  1.135824e+11   \n",
       "2018-06-16 00:00:00 UTC  2018-06-16 00:00:00 UTC  6406.807050  1.095277e+11   \n",
       "2018-06-17 00:00:00 UTC  2018-06-17 00:00:00 UTC  6478.815924  1.107722e+11   \n",
       "2018-06-18 00:00:00 UTC  2018-06-18 00:00:00 UTC  6418.850002  1.097596e+11   \n",
       "2018-06-19 00:00:00 UTC  2018-06-19 00:00:00 UTC  6677.533403  1.141965e+11   \n",
       "2018-06-20 00:00:00 UTC  2018-06-20 00:00:00 UTC  6708.681820  1.147422e+11   \n",
       "2018-06-21 00:00:00 UTC  2018-06-21 00:00:00 UTC  6714.375461  1.148525e+11   \n",
       "2018-06-22 00:00:00 UTC  2018-06-22 00:00:00 UTC  6690.832152  1.144639e+11   \n",
       "2018-06-23 00:00:00 UTC  2018-06-23 00:00:00 UTC  6088.579442  1.041724e+11   \n",
       "2018-06-24 00:00:00 UTC  2018-06-24 00:00:00 UTC  6169.437264  1.055683e+11   \n",
       "2018-06-25 00:00:00 UTC  2018-06-25 00:00:00 UTC  6159.707194  1.054151e+11   \n",
       "2018-06-26 00:00:00 UTC  2018-06-26 00:00:00 UTC  6241.848712  1.068327e+11   \n",
       "2018-06-27 00:00:00 UTC  2018-06-27 00:00:00 UTC  6069.872606  1.039006e+11   \n",
       "2018-06-28 00:00:00 UTC  2018-06-28 00:00:00 UTC  6117.036410  1.047172e+11   \n",
       "2018-06-29 00:00:00 UTC  2018-06-29 00:00:00 UTC  5879.988974  1.006708e+11   \n",
       "2018-06-30 00:00:00 UTC  2018-06-30 00:00:00 UTC  6182.256748  1.058574e+11   \n",
       "2018-07-01 00:00:00 UTC  2018-07-01 00:00:00 UTC  6366.499468  1.090234e+11   \n",
       "2018-07-02 00:00:00 UTC  2018-07-02 00:00:00 UTC  6312.877243  1.081167e+11   \n",
       "\n",
       "                         total_volume          ma7  \n",
       "snapped_at                                          \n",
       "2013-04-28 00:00:00 UTC  0.000000e+00          NaN  \n",
       "2013-04-29 00:00:00 UTC  0.000000e+00          NaN  \n",
       "2013-04-30 00:00:00 UTC  0.000000e+00          NaN  \n",
       "2013-05-01 00:00:00 UTC  0.000000e+00          NaN  \n",
       "2013-05-02 00:00:00 UTC  0.000000e+00          NaN  \n",
       "2013-05-03 00:00:00 UTC  0.000000e+00          NaN  \n",
       "2013-05-04 00:00:00 UTC  0.000000e+00   119.321429  \n",
       "2013-05-05 00:00:00 UTC  0.000000e+00   116.677143  \n",
       "2013-05-06 00:00:00 UTC  0.000000e+00   113.301429  \n",
       "2013-05-07 00:00:00 UTC  0.000000e+00   109.172857  \n",
       "2013-05-08 00:00:00 UTC  0.000000e+00   108.550000  \n",
       "2013-05-09 00:00:00 UTC  0.000000e+00   109.917143  \n",
       "2013-05-10 00:00:00 UTC  0.000000e+00   113.884286  \n",
       "2013-05-11 00:00:00 UTC  0.000000e+00   114.135714  \n",
       "2013-05-12 00:00:00 UTC  0.000000e+00   113.839000  \n",
       "2013-05-13 00:00:00 UTC  0.000000e+00   113.674714  \n",
       "2013-05-14 00:00:00 UTC  0.000000e+00   114.831857  \n",
       "2013-05-15 00:00:00 UTC  0.000000e+00   115.048429  \n",
       "2013-05-16 00:00:00 UTC  0.000000e+00   115.405571  \n",
       "2013-05-17 00:00:00 UTC  0.000000e+00   116.022714  \n",
       "2013-05-18 00:00:00 UTC  0.000000e+00   117.575571  \n",
       "2013-05-19 00:00:00 UTC  0.000000e+00   118.402429  \n",
       "2013-05-20 00:00:00 UTC  0.000000e+00   119.173857  \n",
       "2013-05-21 00:00:00 UTC  0.000000e+00   120.373857  \n",
       "2013-05-22 00:00:00 UTC  0.000000e+00   121.637286  \n",
       "2013-05-23 00:00:00 UTC  0.000000e+00   123.101286  \n",
       "2013-05-24 00:00:00 UTC  0.000000e+00   124.329857  \n",
       "2013-05-25 00:00:00 UTC  0.000000e+00   125.314143  \n",
       "2013-05-26 00:00:00 UTC  0.000000e+00   127.328286  \n",
       "2013-05-27 00:00:00 UTC  0.000000e+00   128.243286  \n",
       "...                               ...          ...  \n",
       "2018-06-03 00:00:00 UTC  1.531230e+09  7397.793065  \n",
       "2018-06-04 00:00:00 UTC  1.432775e+09  7442.540994  \n",
       "2018-06-05 00:00:00 UTC  1.643489e+09  7492.910433  \n",
       "2018-06-06 00:00:00 UTC  1.754014e+09  7509.736219  \n",
       "2018-06-07 00:00:00 UTC  1.562706e+09  7541.840066  \n",
       "2018-06-08 00:00:00 UTC  1.459895e+09  7563.784415  \n",
       "2018-06-09 00:00:00 UTC  1.362045e+09  7574.368672  \n",
       "2018-06-10 00:00:00 UTC  1.102263e+09  7552.581306  \n",
       "2018-06-11 00:00:00 UTC  2.788498e+09  7427.955597  \n",
       "2018-06-12 00:00:00 UTC  1.973449e+09  7344.595405  \n",
       "2018-06-13 00:00:00 UTC  2.060764e+09  7203.569224  \n",
       "2018-06-14 00:00:00 UTC  2.571113e+09  7021.929284  \n",
       "2018-06-15 00:00:00 UTC  2.354179e+09  6883.492611  \n",
       "2018-06-16 00:00:00 UTC  1.740753e+09  6718.584175  \n",
       "2018-06-17 00:00:00 UTC  1.306649e+09  6584.843396  \n",
       "2018-06-18 00:00:00 UTC  1.120786e+09  6535.036327  \n",
       "2018-06-19 00:00:00 UTC  1.750367e+09  6506.093434  \n",
       "2018-06-20 00:00:00 UTC  1.649450e+09  6524.215398  \n",
       "2018-06-21 00:00:00 UTC  1.626421e+09  6578.540620  \n",
       "2018-06-22 00:00:00 UTC  1.400178e+09  6585.127973  \n",
       "2018-06-23 00:00:00 UTC  2.932411e+09  6539.666886  \n",
       "2018-06-24 00:00:00 UTC  1.635693e+09  6495.469935  \n",
       "2018-06-25 00:00:00 UTC  2.452039e+09  6458.449534  \n",
       "2018-06-26 00:00:00 UTC  4.326985e+09  6396.208864  \n",
       "2018-06-27 00:00:00 UTC  3.248018e+09  6304.950404  \n",
       "2018-06-28 00:00:00 UTC  2.057222e+09  6219.616254  \n",
       "2018-06-29 00:00:00 UTC  1.928321e+09  6103.781515  \n",
       "2018-06-30 00:00:00 UTC  2.341619e+09  6117.163987  \n",
       "2018-07-01 00:00:00 UTC  2.086843e+09  6145.315730  \n",
       "2018-07-02 00:00:00 UTC  2.601786e+09  6167.197166  \n",
       "\n",
       "[1890 rows x 5 columns]>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change the index of this dataset from number to the date\n",
    "df.index = df['snapped_at']\n",
    "df.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fe74a16978>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create the plot of the price change\n",
    "%pylab inline\n",
    "df['price'].plot(kind = 'line', figsize = [10,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of snapped_at\n",
       "2013-04-28 00:00:00 UTC            NaN\n",
       "2013-04-29 00:00:00 UTC            NaN\n",
       "2013-04-30 00:00:00 UTC            NaN\n",
       "2013-05-01 00:00:00 UTC            NaN\n",
       "2013-05-02 00:00:00 UTC            NaN\n",
       "2013-05-03 00:00:00 UTC            NaN\n",
       "2013-05-04 00:00:00 UTC     119.321429\n",
       "2013-05-05 00:00:00 UTC     116.677143\n",
       "2013-05-06 00:00:00 UTC     113.301429\n",
       "2013-05-07 00:00:00 UTC     109.172857\n",
       "2013-05-08 00:00:00 UTC     108.550000\n",
       "2013-05-09 00:00:00 UTC     109.917143\n",
       "2013-05-10 00:00:00 UTC     113.884286\n",
       "2013-05-11 00:00:00 UTC     114.135714\n",
       "2013-05-12 00:00:00 UTC     113.839000\n",
       "2013-05-13 00:00:00 UTC     113.674714\n",
       "2013-05-14 00:00:00 UTC     114.831857\n",
       "2013-05-15 00:00:00 UTC     115.048429\n",
       "2013-05-16 00:00:00 UTC     115.405571\n",
       "2013-05-17 00:00:00 UTC     116.022714\n",
       "2013-05-18 00:00:00 UTC     117.575571\n",
       "2013-05-19 00:00:00 UTC     118.402429\n",
       "2013-05-20 00:00:00 UTC     119.173857\n",
       "2013-05-21 00:00:00 UTC     120.373857\n",
       "2013-05-22 00:00:00 UTC     121.637286\n",
       "2013-05-23 00:00:00 UTC     123.101286\n",
       "2013-05-24 00:00:00 UTC     124.329857\n",
       "2013-05-25 00:00:00 UTC     125.314143\n",
       "2013-05-26 00:00:00 UTC     127.328286\n",
       "2013-05-27 00:00:00 UTC     128.243286\n",
       "                              ...     \n",
       "2018-06-03 00:00:00 UTC    7397.793065\n",
       "2018-06-04 00:00:00 UTC    7442.540994\n",
       "2018-06-05 00:00:00 UTC    7492.910433\n",
       "2018-06-06 00:00:00 UTC    7509.736219\n",
       "2018-06-07 00:00:00 UTC    7541.840066\n",
       "2018-06-08 00:00:00 UTC    7563.784415\n",
       "2018-06-09 00:00:00 UTC    7574.368672\n",
       "2018-06-10 00:00:00 UTC    7552.581306\n",
       "2018-06-11 00:00:00 UTC    7427.955597\n",
       "2018-06-12 00:00:00 UTC    7344.595405\n",
       "2018-06-13 00:00:00 UTC    7203.569224\n",
       "2018-06-14 00:00:00 UTC    7021.929284\n",
       "2018-06-15 00:00:00 UTC    6883.492611\n",
       "2018-06-16 00:00:00 UTC    6718.584175\n",
       "2018-06-17 00:00:00 UTC    6584.843396\n",
       "2018-06-18 00:00:00 UTC    6535.036327\n",
       "2018-06-19 00:00:00 UTC    6506.093434\n",
       "2018-06-20 00:00:00 UTC    6524.215398\n",
       "2018-06-21 00:00:00 UTC    6578.540620\n",
       "2018-06-22 00:00:00 UTC    6585.127973\n",
       "2018-06-23 00:00:00 UTC    6539.666886\n",
       "2018-06-24 00:00:00 UTC    6495.469935\n",
       "2018-06-25 00:00:00 UTC    6458.449534\n",
       "2018-06-26 00:00:00 UTC    6396.208864\n",
       "2018-06-27 00:00:00 UTC    6304.950404\n",
       "2018-06-28 00:00:00 UTC    6219.616254\n",
       "2018-06-29 00:00:00 UTC    6103.781515\n",
       "2018-06-30 00:00:00 UTC    6117.163987\n",
       "2018-07-01 00:00:00 UTC    6145.315730\n",
       "2018-07-02 00:00:00 UTC    6167.197166\n",
       "Name: ma7, Length: 1890, dtype: float64>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find the mean value at each week, and name the mean value as \"ma7\"\n",
    "df['ma7'] = df['price'].rolling(window = 7).mean()\n",
    "df['ma7'].head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fe74b5c5c0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the whole graph of the market price and weekly average market price\n",
    "df[['price','ma7']].plot(kind = 'line',figsize =[20,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#find out last month's market price vs average price\n",
    "df2 = df[df['snapped_at'] >= '2018-06-03 00:00:00 UTC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of                                       snapped_at        price    market_cap  \\\n",
       "snapped_at                                                                    \n",
       "2018-06-03 00:00:00 UTC  2018-06-03 00:00:00 UTC  7567.512938  1.291850e+11   \n",
       "2018-06-04 00:00:00 UTC  2018-06-04 00:00:00 UTC  7639.879446  1.304361e+11   \n",
       "2018-06-05 00:00:00 UTC  2018-06-05 00:00:00 UTC  7463.654996  1.274444e+11   \n",
       "2018-06-06 00:00:00 UTC  2018-06-06 00:00:00 UTC  7569.011340  1.292600e+11   \n",
       "2018-06-07 00:00:00 UTC  2018-06-07 00:00:00 UTC  7605.578489  1.298984e+11   \n",
       "2018-06-08 00:00:00 UTC  2018-06-08 00:00:00 UTC  7613.777393  1.300521e+11   \n",
       "2018-06-09 00:00:00 UTC  2018-06-09 00:00:00 UTC  7561.166101  1.291671e+11   \n",
       "2018-06-10 00:00:00 UTC  2018-06-10 00:00:00 UTC  7415.001379  1.266835e+11   \n",
       "2018-06-11 00:00:00 UTC  2018-06-11 00:00:00 UTC  6767.499485  1.156319e+11   \n",
       "2018-06-12 00:00:00 UTC  2018-06-12 00:00:00 UTC  6880.133649  1.175681e+11   \n",
       "2018-06-13 00:00:00 UTC  2018-06-13 00:00:00 UTC  6581.828075  1.124829e+11   \n",
       "2018-06-14 00:00:00 UTC  2018-06-14 00:00:00 UTC  6334.098906  1.082602e+11   \n",
       "2018-06-15 00:00:00 UTC  2018-06-15 00:00:00 UTC  6644.720680  1.135824e+11   \n",
       "2018-06-16 00:00:00 UTC  2018-06-16 00:00:00 UTC  6406.807050  1.095277e+11   \n",
       "2018-06-17 00:00:00 UTC  2018-06-17 00:00:00 UTC  6478.815924  1.107722e+11   \n",
       "2018-06-18 00:00:00 UTC  2018-06-18 00:00:00 UTC  6418.850002  1.097596e+11   \n",
       "2018-06-19 00:00:00 UTC  2018-06-19 00:00:00 UTC  6677.533403  1.141965e+11   \n",
       "2018-06-20 00:00:00 UTC  2018-06-20 00:00:00 UTC  6708.681820  1.147422e+11   \n",
       "2018-06-21 00:00:00 UTC  2018-06-21 00:00:00 UTC  6714.375461  1.148525e+11   \n",
       "2018-06-22 00:00:00 UTC  2018-06-22 00:00:00 UTC  6690.832152  1.144639e+11   \n",
       "2018-06-23 00:00:00 UTC  2018-06-23 00:00:00 UTC  6088.579442  1.041724e+11   \n",
       "2018-06-24 00:00:00 UTC  2018-06-24 00:00:00 UTC  6169.437264  1.055683e+11   \n",
       "2018-06-25 00:00:00 UTC  2018-06-25 00:00:00 UTC  6159.707194  1.054151e+11   \n",
       "2018-06-26 00:00:00 UTC  2018-06-26 00:00:00 UTC  6241.848712  1.068327e+11   \n",
       "2018-06-27 00:00:00 UTC  2018-06-27 00:00:00 UTC  6069.872606  1.039006e+11   \n",
       "2018-06-28 00:00:00 UTC  2018-06-28 00:00:00 UTC  6117.036410  1.047172e+11   \n",
       "2018-06-29 00:00:00 UTC  2018-06-29 00:00:00 UTC  5879.988974  1.006708e+11   \n",
       "2018-06-30 00:00:00 UTC  2018-06-30 00:00:00 UTC  6182.256748  1.058574e+11   \n",
       "2018-07-01 00:00:00 UTC  2018-07-01 00:00:00 UTC  6366.499468  1.090234e+11   \n",
       "2018-07-02 00:00:00 UTC  2018-07-02 00:00:00 UTC  6312.877243  1.081167e+11   \n",
       "\n",
       "                         total_volume          ma7  \n",
       "snapped_at                                          \n",
       "2018-06-03 00:00:00 UTC  1.531230e+09  7397.793065  \n",
       "2018-06-04 00:00:00 UTC  1.432775e+09  7442.540994  \n",
       "2018-06-05 00:00:00 UTC  1.643489e+09  7492.910433  \n",
       "2018-06-06 00:00:00 UTC  1.754014e+09  7509.736219  \n",
       "2018-06-07 00:00:00 UTC  1.562706e+09  7541.840066  \n",
       "2018-06-08 00:00:00 UTC  1.459895e+09  7563.784415  \n",
       "2018-06-09 00:00:00 UTC  1.362045e+09  7574.368672  \n",
       "2018-06-10 00:00:00 UTC  1.102263e+09  7552.581306  \n",
       "2018-06-11 00:00:00 UTC  2.788498e+09  7427.955597  \n",
       "2018-06-12 00:00:00 UTC  1.973449e+09  7344.595405  \n",
       "2018-06-13 00:00:00 UTC  2.060764e+09  7203.569224  \n",
       "2018-06-14 00:00:00 UTC  2.571113e+09  7021.929284  \n",
       "2018-06-15 00:00:00 UTC  2.354179e+09  6883.492611  \n",
       "2018-06-16 00:00:00 UTC  1.740753e+09  6718.584175  \n",
       "2018-06-17 00:00:00 UTC  1.306649e+09  6584.843396  \n",
       "2018-06-18 00:00:00 UTC  1.120786e+09  6535.036327  \n",
       "2018-06-19 00:00:00 UTC  1.750367e+09  6506.093434  \n",
       "2018-06-20 00:00:00 UTC  1.649450e+09  6524.215398  \n",
       "2018-06-21 00:00:00 UTC  1.626421e+09  6578.540620  \n",
       "2018-06-22 00:00:00 UTC  1.400178e+09  6585.127973  \n",
       "2018-06-23 00:00:00 UTC  2.932411e+09  6539.666886  \n",
       "2018-06-24 00:00:00 UTC  1.635693e+09  6495.469935  \n",
       "2018-06-25 00:00:00 UTC  2.452039e+09  6458.449534  \n",
       "2018-06-26 00:00:00 UTC  4.326985e+09  6396.208864  \n",
       "2018-06-27 00:00:00 UTC  3.248018e+09  6304.950404  \n",
       "2018-06-28 00:00:00 UTC  2.057222e+09  6219.616254  \n",
       "2018-06-29 00:00:00 UTC  1.928321e+09  6103.781515  \n",
       "2018-06-30 00:00:00 UTC  2.341619e+09  6117.163987  \n",
       "2018-07-01 00:00:00 UTC  2.086843e+09  6145.315730  \n",
       "2018-07-02 00:00:00 UTC  2.601786e+09  6167.197166  >"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fe74bfb9e8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot last month's market price and average price, so that I can know the best timing to buy the Bitcoin\n",
    "df2[['price','ma7']].plot(kind = 'line',figsize =[20,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}