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yagays/coincheckJPY.ipynb

Created September 4, 2016 10:37
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coincheckJPY.ipynb
{
"cells": [
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"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"from datetime import datetime"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
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},
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"source": [
"df = pd.read_csv(\"http://api.bitcoincharts.com/v1/trades.csv?symbol=coincheckJPY\",\n",
" header=None,\n",
" parse_dates=True,\n",
" date_parser=lambda x: datetime.fromtimestamp(float(x)),\n",
" index_col='datetime',\n",
" names=['datetime', 'price', 'amount'])"
]
},
{
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{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>price</th>\n",
" <th>amount</th>\n",
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" <tr>\n",
" <th>datetime</th>\n",
" <th></th>\n",
" <th></th>\n",
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" <td>59106.0</td>\n",
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" <td>59117.0</td>\n",
" <td>0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-08-30 20:24:00</th>\n",
" <td>59117.0</td>\n",
" <td>0.227700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-08-30 20:23:44</th>\n",
" <td>59117.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2016-08-30 20:18:38</th>\n",
" <td>59117.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2016-08-30 20:10:35</th>\n",
" <td>59140.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2016-08-30 19:57:47</th>\n",
" <td>59117.0</td>\n",
" <td>1.470000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-08-30 19:42:27</th>\n",
" <td>59121.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2016-08-30 19:40:30</th>\n",
" <td>59133.0</td>\n",
" <td>0.777907</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>6994 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" price amount\n",
"datetime \n",
"2016-09-04 19:26:38 62298.0 0.500000\n",
"2016-09-04 19:10:58 62265.0 0.504143\n",
"2016-09-04 19:10:58 62265.0 1.000000\n",
"2016-09-04 19:10:58 62269.0 0.331000\n",
"2016-09-04 19:08:57 62277.0 3.126399\n",
"2016-09-04 19:08:57 62277.0 1.000000\n",
"2016-09-04 19:08:57 62279.0 0.038333\n",
"2016-09-04 19:01:47 62288.0 0.229719\n",
"2016-09-04 19:01:36 62288.0 1.000000\n",
"2016-09-04 19:01:24 62283.0 1.062988\n",
"2016-09-04 19:01:24 62283.0 1.000000\n",
"2016-09-04 18:55:08 62345.0 2.600000\n",
"2016-09-04 18:54:35 62345.0 1.000000\n",
"2016-09-04 18:54:35 62345.0 1.000000\n",
"2016-09-04 18:52:18 62358.0 0.526991\n",
"2016-09-04 18:52:12 62357.0 0.752390\n",
"2016-09-04 18:52:10 62357.0 0.752390\n",
"2016-09-04 18:52:04 62357.0 0.520224\n",
"2016-09-04 18:51:18 62381.0 0.016262\n",
"2016-09-04 18:51:16 62381.0 0.016262\n",
"2016-09-04 18:50:57 62352.0 0.573233\n",
"2016-09-04 18:50:57 62353.0 0.213384\n",
"2016-09-04 18:50:55 62353.0 0.786616\n",
"2016-09-04 18:50:54 62352.0 0.414867\n",
"2016-09-04 18:50:25 62390.0 0.020010\n",
"2016-09-04 18:50:15 62488.0 1.205413\n",
"2016-09-04 18:50:01 62488.0 1.205413\n",
"2016-09-04 18:49:12 62500.0 0.320020\n",
"2016-09-04 18:49:09 62488.0 0.589175\n",
"2016-09-04 18:48:53 62500.0 0.019843\n",
"... ... ...\n",
"2016-08-30 20:52:19 59088.0 0.100000\n",
"2016-08-30 20:51:37 59088.0 0.100000\n",
"2016-08-30 20:47:59 59050.0 0.489500\n",
"2016-08-30 20:47:47 59050.0 0.100000\n",
"2016-08-30 20:47:46 59091.0 0.027140\n",
"2016-08-30 20:47:38 59091.0 0.100000\n",
"2016-08-30 20:47:32 59091.0 0.500000\n",
"2016-08-30 20:47:25 59091.0 0.500000\n",
"2016-08-30 20:47:08 59091.0 0.600000\n",
"2016-08-30 20:46:27 59091.0 0.472860\n",
"2016-08-30 20:43:16 59090.0 0.100000\n",
"2016-08-30 20:41:52 59095.0 0.100000\n",
"2016-08-30 20:39:00 59106.0 0.600000\n",
"2016-08-30 20:38:58 59106.0 0.600000\n",
"2016-08-30 20:38:39 59106.0 0.100000\n",
"2016-08-30 20:36:40 59106.0 0.100000\n",
"2016-08-30 20:36:32 59106.0 0.100000\n",
"2016-08-30 20:36:14 59106.0 1.000000\n",
"2016-08-30 20:36:00 59117.0 0.600000\n",
"2016-08-30 20:35:51 59117.0 0.600000\n",
"2016-08-30 20:35:34 59117.0 0.500000\n",
"2016-08-30 20:35:27 59117.0 0.500000\n",
"2016-08-30 20:34:11 59117.0 0.100000\n",
"2016-08-30 20:24:00 59117.0 0.227700\n",
"2016-08-30 20:23:44 59117.0 0.762500\n",
"2016-08-30 20:18:38 59117.0 1.367600\n",
"2016-08-30 20:10:35 59140.0 0.339870\n",
"2016-08-30 19:57:47 59117.0 1.470000\n",
"2016-08-30 19:42:27 59121.0 0.818797\n",
"2016-08-30 19:40:30 59133.0 0.777907\n",
"\n",
"[6994 rows x 2 columns]"
]
},
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
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"<matplotlib.axes._subplots.AxesSubplot at 0x1129b31d0>"
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yv/89yLZurZpv5Mi8qlo0vPuu2WWXBRv++c9BtnWr2YoVZl/4gtn99+d2n/bt\nzTZuNHv9dbPvfc+sTx+zDh3MXnyxar65c83uvbeqDMxOOKH+7+LUD+pR898LeFihitYKuNfMnpF0\nhaRRMc9DZjY5po8FfiZpC1AJXGxm6+K5UcBkoC3wlIWRPgATgSmSFgJrKLCRPgArVyadYrWx775w\n1VUN3265enXD3r8u1GV1w3feSdLp47L/+c+wrz5J56CDtl+vYmTlytD2ftttYRYshIlMw4fD3XfD\nBRcE2YMP5na/9evDvn//sM2fH36j6qNdevdO+gjSqazcvvdwGgdf3iELy5eH4ZupYW+1cc89cN55\nwSHmMtuwrqSPMLjlluAcTz45/8+pjeOOg29/G7761XB8221haB6EOv2CBYlzmD07TJffeecwQiJ9\n/aOlS5NFuTKN9LnmmtCMdO21Dfs+zYnqI1C++MVg+6lT4fOfD7/Fr36V31EyNVFWFp69++4N/yyn\nZmob7ePOPwv//neY/VdZmX14V2VlcFhbtjRMsIdMz29sk1R31Ok6ffRRGEFSfbmL//1v24Jz6dJQ\ns3zpJbjooqr3hNCm/PbbYfr/Bx+EWZU33pjfd2lutGkThimmuOYauPPOsPhZv36hItMYfVJO4eBr\n+2wn77+fDHXLZVxvagxyps/dior663P44dvKHnkkdMYtWZLbDMX6kL6MRaZ3/PKXMw/R3LQpSe+3\nX3J9nz6J46/OXnvBtGmhA/jRR8MwvPT7ffxxndVv9uy9N4walRy3aBHG7P/5z2EMvjt+pwo1dQYU\n2kYjd/juvXfV4W650qZN6CRL5513wj1+/vP66XTIIZmHRKZv1Z+dLyoqzAYNMrv+erODD86uh9m2\nskcfNZszJ6T/8IdEPmWK2W9/W/V5K1aY7bab2eOPm119dcj30Udmmzcn1513ntnDDzfM++5obN1q\ntvPOZhs2hL/BPfYwe+ONxFYvvdTUGjpNAbV0+HrNvwZWrgz7FSvq1rSSKcRbapnYq6+uXydYLp2r\nRx+9/feviQkTQjNWRUVYWqL6iobf/W7mha46dqx6fOyxyZDZdesS+de/XrXGCqEW+7WvhWGg//lP\nkP3gB6HTMcXdd8MZZ4QmoVWrtu/ddjTMwpo56faD8HV0wAFhpvmCBWHbddfkfF0mWznFQbN3/rNn\nh5EKZ50VnMmhh4YmnGOOgYcfrv3agw7adhZkNlq2rN1J5zrSIhNnnJE9zxtv1HzuuefCu1efKbxi\nRe3O8zvwfwWeAAAWG0lEQVTfCfsLLwzXp8cygFCopZbjfeGFsIojhOAZAKedFvbphcEee4R9aux4\nJnr0CGP+KyrCtTvvHJze4MHBCc6fHwq7q64KU/FXrw4djW+8Aa+8EtZm2bwZ5sxJCpDqLFsWljiQ\ngp5f+UrYrrii7k1LlZW5LUxWHyorw5o5u+2WVEqefz40uaWa0Lp3D+dT8y6WLct8L6e4afbOf/r0\n4Gi+9CUYOjQ4qu99L6zRcc89NV932GHwpz/V/XktW2au3R9/fPjnfOaZut9z/fptnXOmQik1vC9T\nAXPHHUmNfZddEvmaNWGI6l57BSeYorw8dNSmt9enVjMcOhRGjw5O2SzYd+jQ4HQHD05qnE8+GZxv\n9cICkhFCF19c83vvvXf4AnvzzWQZ3nnzwpo0EL6onn0W/vKXsOT2nnuGEURHHBH02GOP0M7dt2/o\na/jd78Lfw9NPJ45z1qykwPzKV8KwyOHDg71POSUpyHLhuuu2/drJJ2+8AX//e/i67Ngx7CdOhL/+\nNZy/9NKq+VOFV0MuNujswNTUHlRoG9vZ5n/55VWnnaeYO9fswANrvu6ww8zefLPuz9t9d7NVq6rK\nDjjA7MknzSZONDv//MzXrVpl9swzyXFZWWir/clPknbbdu3M7rjDrLLSbL/9MrezjxsXdK/OwIFV\n886eHeTvvltV/oUvhDZ4MDvzzLDVd1mJDz6oOgkodb8WLcw+/rjm66ZPD/n22cfsN78xu+ACs333\nNXvllW3zPvqo2SmnBH1vvtnskUfCtS1bmp1zTtV37NLF7MtfDnZ8/vnwd7BhQ9X7bd0afqvevc3W\nrcv+jimbVZ+sVl+eecbs+OPNfve75P7f+pbZ2rUhnep/GT5822tT/StO8UItbf5N7tRz3bbX+Q8f\nbvbAA9vKKyqCM33iibA2yV//avbUU+H4scfMunUze/vtuj+v+mzgZcvsk87eBx7I/E9qZnbuuck/\n6qpVwSGl/tlvusnss581Ky1NHEumTlczs0WLQsFQnQMOCHnSZ+T+4x+hgAOzqVPNZs4069vXbKed\nzK66Kszm3H13s7vuMtu0qe62qImjjkp02LKl5nypNX++/e3g0MHs7LNze0ZlZdVZxGVlZtOmhdnE\nL74Y7jV4cChUTj458z02bkz0XL++9uel/w5r1uSmY4r1683mz08qDStXhv2WLaFAS7/3E08k10nJ\n7/m1r21737fecudf7BS18z/mmG2no6f4wQ/CFPQTTzQ76SSzIUPCP9sXvxhqhh98UPfnde1q9t57\nIb11q9nQocHKH35o9vLLIV1RUfWaTAubjRgRCouaHNN991XNn/qKWbEiLLg1fnwYKbNqldnttwdH\nl1rQq0uX5LoXXgj7lMP5+tdDLfPDD81uuy2R55MNG5LnV1bWnvfee83efz/UwsFs3rz86PD668Fm\ngwaZPfdczflSheNhh5ldcknY5s9Pzq9dG95h2DCziy5K8qZYsyb5e6iJSy4x23NPs06dzBYvTn6X\nww4zO+igMFrsiitCwZUJMPvVr7aVV1SEvxOneClK519REd6udWuzf/2rTpfWi27dwnDI73zHbNKk\noMNFF4VzlZXheMmScPzPfwbnfOGFYfVDMDvtNLPrrgvOqTZSXxSnnhoKmFRBVVFh9tOfhkIvvXD4\n9rfNFiwIefbcs+q5r361YWxRG2VloYDJlalTLe9NKrmyebPZ5MlhOOqXvlTV2UL4nS+4IDTJPfhg\nkK1YkZxP/8K5+upQyQCzAQPMjjsupJ95ZtsKQK7v26VL1a8cx0lRlM7/qaeSf6C1a+t0ab1I/8cd\nPTr8s1c/P3nytnknTgyOO1tNOMX771utn/R//GPV+6czd26Q/f73tbe5O5mp7qhHjQrNdpMmhfPH\nHGP2t79VnZOQag7K5OBTTj69Db++/SyOY1a789+hRvtMm5Y9T4opU8K+Y8fclrBtCK6/ftulDgAm\nTao643fIkBDGrmXL3CME7b57cA81Udvyyr17h2svuigEonHqRvV5DhMmhL+3gQPD8bJlYS2d1NyH\nn/0s7FPr3FQfRfbxx8laUM8/3zA6O051dijnP3RoCEP38suJc0/HDEpKwvDB+++HuXPDujCNzZ13\nwtoYkaD6mjaPPhrGwv/wh+F4yJAwJDLfC8Ft2JCkaxrj7mwf1QvocePgJz9J5iz06FH1/E9/WnUS\n29lnJ5PVysuTAnjGjDDsNsVVV+VXb8epQk2fBKkNWAK8BcwGZkRZP+DlKH+MuOZ/PDcaWAi8A5yU\nJh9AiNq1EBifJm8DPBDlrwA9atDDxo7d9nM5fXjk4YcHWe/eZrfc0rCfU7mwYcO2bbZPP11V//Ly\nhnn2unXedNCQQOjXycSHH5otXRr6cZ59NsiuuCJcc8YZ2e/dr1/ox3Gc+kJ92vyBf1MtrCIwExgc\n0+cDP4vp3oRQjzsB+xFCMqZWDp1BiAIG2wZzmRDTZ1NLMJdUh2mmIY5myXEu47KbilTgEkjG2jcE\nqQ7vPn0a7hnFDISO9VwZPjz3gnjLltqHwDpOrtTm/HNtbKjeEt3LzF6M6WeBM2P6dOB+M9tiZkui\n8x8kaZ/4dRDnoHIPMCymTwPujumHgBNqVCJqceKJYaXCFO+/XzVfhw45vVOTsNNOYb9qFXz60w3/\nvIZebqBYWbWqbrEGJkxIwlVmo1WrhlkS3HHSycX5G/CspNckfSvK5ko6PabPArrF9L4ksXmJ6S4Z\n5GVRTtwvBTCzCqBcUqealHnnndDxO3x4WLNFKqzIVtlILZfQuXPjPM/XdWkYOnfOPWZxKv+BBzac\nPo5TV3Jx/p81syOAocB3JA0GLgBGSXoN2JUYtL0xOPjg5J/usMO2DSlX6LRv39QaOI7jkDWGL2a2\nIu5XS3qY0G7/a+BkAEkHAV+M2ctIvgIAuhJq/GUxXV2euqY7sFxSK6CDma3NpMuYMWM+SZeUlFBS\nUsJ774VFxvr2zfYmhcGAAXVbLGx7adkyLPjVVMNcHcdpfEpLSyktLc0pb61hHCXtArQ0sw8ltQOe\nAcYCs2Nh0IIQlP15M5ssqTdwHzCQ0JzzLHCgmZmkV4HLCR2/TwK3mNm0GAj+cDO7RNIIYJiZbRPE\nvaYwjql+gLFjw3K+n/88jCi4EPCO4ziNz3bH8JXUE0itet8KuNfMrpd0BWGUDsBDZnZV2jVXEZqF\nKoArzOzpKB9AKCjaAk+Z2eVR3gaYAhwBrAFGxM7i6rrU6vxrm/DkOI5TjDTrAO633grnntuw66g7\njuPsiDRr5+84juNkpjbnv0Mt7+A4juPkB3f+juM4RYg7f8dxnCLEnb/jOE4R4s7fcRynCHHn7ziO\nU4S483ccxylC3Pk7juMUIe78HcdxihB3/o7jOEWIO3/HcZwixJ2/4zhOEeLO33EcpwjJ6vwlLZH0\nlqTZkmZE2UBJM6JspqQjo3w/SRujfLakCWn3GSBpjqSFksanydtIeiDKX5HUoyFetD7kGhmnoSkE\nPQpBBygMPQpBB3A9Ck0HKAw9sumQawD3EjM7wswGRtmNwE9jbN9r4nGKRTHvEWY2Kk1+OzDSzHoB\nvSQNifKRwJooHwfckINOjUoh/JBQGHoUgg5QGHoUgg7gehSaDlAYeuTD+QNUXw96BdAhpjsS4vDW\nfLG0D9DezGZE0T3AsJg+Dbg7ph8CTshRJ8dxHGc7ybXm/6yk1yR9K8quBH4t6T3gJmB0Wv6escmn\nVNLnoqwLScB2CIVFl7RzSwHMrAIol9Rp+17HcRzHyQkzq3UD9on7zsAbwGBCYPYzovwsYHpMtwZ2\ni+n+wHtAe+AzqTzx3GDg8ZieA+ybdm4R0CmDHuabb7755lvdtpp8eyuyYGYr4n61pIeBgcBAMzsx\nZpkK3BnzbAY2x/QsSYuBXoSafte023Yl+RIoA7oDyyW1AjqY2doMemQMReY4juPUnVqbfSTtIql9\nTLcDTgLeBhZJOi5mOx5YEPPsIallTO9PcPzvxgJkvaRBkgScCzwar38MOC+mhwPP5evlHMdxnMxk\nq/nvBTwc/DWtgHvN7GlJa4DfSmoDbAQuivmPBX4maQtQCVxsZuviuVHAZKAt8JSZTYvyicAUSQuB\nNcCIvLyZ4ziOUyOK7elORFILM6tsaj0KAbdFgtsiwW2RsCPbwmf4ApJ2lXSZpAOAnaOs0fsYJLVu\n7Gdm0MFtkejgtkh0cFskOjQLWxR9zV/S8YQJaG8D7wObzeyyJtDju8A3CX0hM83sCUmyRvyB3BZV\ndHBbJDq4LRIdmo8tsg31bO4b8HVgTEzvCcwizEQGaNFIOpwAvAocAZwDvA4Makwd3BZuC7dFcdmi\n6Jp9JHWX1D9NdAiwAcDMVgE/Bv5fPG6wtjxJO6Ud7kHoBJ9tZvcRZkD/rhF0cFskOrgtEh3cFokO\nzdcWjVViFsIGXEeYTTydMDO5I/BZwnDU9HyPEdYuaggddgJ+A9wMnBBlZwJ/q5bvbeD8mJbbwm3h\ntnBb5NMWRVPzl7QHcBBwIPAVoAK41sxeAuZL+kVa9knAXtVK23zo0AL4LaHkngWMlnSxmT0E7Cnp\na2nZf0KY94DFXzSPergtEj3cFokebotEj2Zvi6Jx/sAW4Cigs5l9ADwIIOlcwjyFr0k6NuY9GCgz\nsy151qED0Jcw/+Ee4NfAp+OEue8Av4hzJwCWE/7IWsY/gnzitkhwWyS4LRKavS2arfNPDb2KxpCZ\nlRN+wFRpOQd4GTga+C8wFjhH0gsxz8x8PD/tuEX8I/oPcH4UvwS8Bowws1LgGeAWSWcR2hJ3NbOt\nVs+2RLfFtrq4LdwWmXQpKls0RDtVU27At4FPA5/KcO50woziw+PxZ4BHgI7xuC3wxTzp0TIt3SK1\nJ/TOTwT2jLIjgVuBnsBuwKnAX4CfuS3cFm4Lt0VD2aLeShfKBvQhrDr6BKHne3LauSnRaF2Aq4CJ\naedeBA7Jox6poVc3A2enyU8ljBToDvwK+HHauZeBo9KOd3JbuC3cFm6LhrRFXl6gEDbg88DtMb1r\n/FFvisf7pOXbC3gB+ANhrOyDhJVE86HDofGHPC7+eH8Hzonnzo3nWwElhE+4MwgdSs8Bn3FbuC3c\nFm6LxrJFXgzZFBth2NVAYqlH+Hy7Je18T2Ad0CUet0g71xn4AvCNPOiRft+SajoMJXQEZbruNOAu\n4F/AJW4Lt4Xbwm3RGLb45J75uEljb4Te9lXAk4QQkF3jthLYPS3fOODutOMLga551ONaYAJwVjwe\nAMyulmca8MtMfwBAG+o5K9Bt4bZwW7gttmfb4Ub7SGpL6HEfbGZfJEQLGw18CNxH+DxLMQVoKalj\nPN5EGMKVDz1+AhxD+LEuk/RDM3udEJTmurSs/wccK6lDvO6XxGWrzWyT1WOUgtuiig5ui0QHt0Wi\ng9uiJvJVijTmBrxD+DEhTMT4GSGucCtCGMhUyXoWcGsDPL8V8DTQNx4fR5iF9zVCB80aYo2B0Gn0\nO2I7IRlGFbgt3BZuC7dFY9gifSvomn/aBAYktUqbvDCJMAwLM1sA/BPYD9gduAw4QdKzhDU3Xq2n\nDtXH37ayEGh+LvDVKH4p6nAC8AGh5/5Xks4Brib8oB9Hfddvpx5ui+S5bovkuW6L5Llui7rQUKVK\nHkrLbxE+0bbpZCF8Pt0JnBiP9yeMdT0kHu8EnAy0y4MebdPS6Z01JxEWVOoTjw8mDMk6BmgJDCFE\nLrutvnq4LdwWbgu3Rb63gqv5Szpe0nPAlwnjWbdEuSRdH2ezzQVmAxfGkvVdQlCFfQHMbIuZPW1m\nG+qhxxdibeAmSak2t0pJR0sqibq9C3wjnvsXIWbxfhZm2U0DvmVml26vHm4Lt4Xbwm3RUBSM81eY\nVr0roYf9VjMbCiwGPgefLFZ0o5n92cLU6/sAA/4k6a+EsboL8qTLgYQV/W4lfDKeKumqeLoDYdW8\nDwljgw+T9H1JuxFqEOWp+9h2rvXhtqjyfLdF8ny3RfJ8t0V9aYzPi9o2wifPrwifO5+tdm4w8Aph\nzYpM17YmBI2/KA96tCAZUvV1YELauZGEH2mvDNd9mvC59hbw/9wWbgu3hduiMWxR73do0ocHA94O\n3BsN+Cxhtbp28fxAQknao9p1w4iRa/KkxwXACuAX8bgvoSOmZzy+mDAT755q130q7Y+qbT11cFu4\nLdwWbotG25q62ac9oST8tpn9kVCiH0QIVgBhRbvjSIIkp+ubl17w+Ol4OnADMFTSIWb2FmEyyPWS\nXiLUFr4J7CFp73jdKMIfHma22cw21lMVt0WC2yLBbZHgtsgnTV36APcDl8f0roTlSycA3aJsIvCT\nBtahe9z/EnggplsShoKlxgd3J3yutYnHeS+53RZuC7eF26Kxtqau+UMYcvVpSfuY2UeEtrBNQOdY\ncq8Btkhq3VAKmNl7MXkzsL+kk81sK7DOzF6M5y4GNgJb4zUNUXK7LRLcFgluiwS3Rb5o6tIH2Ae4\nERidJnuJpAQtATo1oj4XAy+kHQ8kxOh8irTV/twWbgu3hduiKW1R301R4SZF0jGENrRbCRFx7iR8\nur3cyHrIzEzSQ4SwaJsJnUoLzWxRI+ngtkh0cFskOrgtEh3cFvmgqUuftBLzFMKSpe8AlzahHrsQ\nAja8D1zhtnBbuC3cFoVsi+3dCqLmnyK202210H7WVDr8gNBZ8yMz29SEergtEj3cFokebotED7dF\nPSgo518IKAROzt+yqTswbosEt0WC2yJhR7aFO3/HcZwipBCGejqO4ziNjDt/x3GcIsSdv+M4ThHi\nzt9xHKcIcefvOI5ThLjzd4oWSWPiOO2azp8u6dAc7lMln6Sxkk7Il56O0xC483eKmWzjnM8Aeudw\nnyr5zOxaM3uuPoo5TkPjzt8pKiRdLelfkl4kBNFG0oWSZkh6Q9JUSW3j+jGnEmKyzpbUU9IBkv4q\n6TVJL0g6uFq+WZL2lzRZ0pnx3ksk/SLe4zVJ/SU9I2mRpIvT9Pq/qMObksY0vmWcYqNVUyvgOI2F\npAHA2UA/QvzUWcBrwF/M7M6Y5/8BI83sNkmPAY+b2V/iueeAi81skaRBhNB9J2TIZyRfFQb8x8yO\nkPQbwhrvRwNtgbeB30s6CTjQzAbGZYkflTTYkuWBHSfvuPN3ionBBEf/P+B/0WkLOFzSdYRg27sC\n09KuEXwSwelo4M+SUudaV89XA4/F/RxCyMENwAZJmyR1AE4CTpI0O+ZrBxxIWDDMcRoEd/5OMWFk\ndtJ3Aaeb2RxJ5xHWhE+/BkIT6TozO6KWe9dEasGvSsKyv6Qdp/4HrzezP9RyD8fJK97m7xQTLwDD\nJO0sqT2hrR5CbNiVknYiBAZPOfIPgU8BmNl64N+ShkNYy11S3+r5spCp4DHgaeACSe3ivbtI6lzn\nt3OcOuDO3ykazGw28ADwJiHS0gyC8/0p8CrwD2B+2iV/Av5P0uuSegJfA0ZKeoPQXn9ahnz716YC\nVb8QLOo1HbgPeFnSW8CDhOYnx2kwfFVPx3GcIsRr/o7jOEWIO3/HcZwixJ2/4zhOEeLO33Ecpwhx\n5+84jlOEuPN3HMcpQtz5O47jFCHu/B3HcYqQ/w888qES+d20VwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112804080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[\"price\"].plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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