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va2577/jupyter-notebook-pandas-highcharts.ipynb

Created May 21, 2018 01:10
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Jupyter Notebook pandas-highcharts candlestick
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jupyter-notebook-pandas-highcharts.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# pandas-highcharts\n",
"\n",
"* [gtnx/pandas-highcharts: Beautiful charting of pandas.DataFrame with Highcharts](https://github.com/gtnx/pandas-highcharts)\n",
"* [Jupyter Notebook Viewer](http://nbviewer.jupyter.org/github/gtnx/pandas-highcharts/blob/master/example.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<script src=\"//code.highcharts.com/stock/highstock.js\"></script>\n",
"<script src=\"//code.highcharts.com/highcharts-more.js\"></script>\n",
"<script src=\"//code.highcharts.com/modules/exporting.js\"></script>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import requests\n",
"#from pandas_highcharts.core import serialize\n",
"from pandas_highcharts.display import display_charts"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[1497474000000, '109.554', '110.979', '109.258', '110.911'],\n",
" [1497560400000, '110.925', '111.415', '110.637', '110.89'],\n",
" [1497819600000, '110.841', '111.601', '110.704', '111.526'],\n",
" [1497906000000, '111.514', '111.782', '111.306', '111.454'],\n",
" [1497992400000, '111.434', '111.738', '111.035', '111.375'],\n",
" [1498078800000, '111.337', '111.446', '110.93', '111.319'],\n",
" [1498165200000, '111.32', '111.427', '111.142', '111.226'],\n",
" [1498424400000, '111.157', '111.939', '111.101', '111.86'],\n",
" [1498510800000, '111.82', '112.464', '111.459', '112.337'],\n",
" [1498597200000, '112.337', '112.416', '111.829', '112.28']]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/64.0.3282.140 Safari/537.36 Edge/17.17134' }\n",
"r = requests.get('https://fx.minkabu.jp/api/v2/bar/USDJPY/daily.json?count=240', headers=headers)\n",
"r.raise_for_status()\n",
"r.json()[:10]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1497474000000</td>\n",
" <td>109.554</td>\n",
" <td>110.979</td>\n",
" <td>109.258</td>\n",
" <td>110.911</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1497560400000</td>\n",
" <td>110.925</td>\n",
" <td>111.415</td>\n",
" <td>110.637</td>\n",
" <td>110.89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1497819600000</td>\n",
" <td>110.841</td>\n",
" <td>111.601</td>\n",
" <td>110.704</td>\n",
" <td>111.526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1497906000000</td>\n",
" <td>111.514</td>\n",
" <td>111.782</td>\n",
" <td>111.306</td>\n",
" <td>111.454</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1497992400000</td>\n",
" <td>111.434</td>\n",
" <td>111.738</td>\n",
" <td>111.035</td>\n",
" <td>111.375</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" time open high low close\n",
"0 1497474000000 109.554 110.979 109.258 110.911\n",
"1 1497560400000 110.925 111.415 110.637 110.89\n",
"2 1497819600000 110.841 111.601 110.704 111.526\n",
"3 1497906000000 111.514 111.782 111.306 111.454\n",
"4 1497992400000 111.434 111.738 111.035 111.375"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(data=r.json(), columns=['time', 'open', 'high', 'low', 'close'])\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" <tr>\n",
" <th>time</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-06-15</th>\n",
" <td>109.554</td>\n",
" <td>110.979</td>\n",
" <td>109.258</td>\n",
" <td>110.911</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-16</th>\n",
" <td>110.925</td>\n",
" <td>111.415</td>\n",
" <td>110.637</td>\n",
" <td>110.890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-19</th>\n",
" <td>110.841</td>\n",
" <td>111.601</td>\n",
" <td>110.704</td>\n",
" <td>111.526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-20</th>\n",
" <td>111.514</td>\n",
" <td>111.782</td>\n",
" <td>111.306</td>\n",
" <td>111.454</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-06-21</th>\n",
" <td>111.434</td>\n",
" <td>111.738</td>\n",
" <td>111.035</td>\n",
" <td>111.375</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" open high low close\n",
"time \n",
"2017-06-15 109.554 110.979 109.258 110.911\n",
"2017-06-16 110.925 111.415 110.637 110.890\n",
"2017-06-19 110.841 111.601 110.704 111.526\n",
"2017-06-20 111.514 111.782 111.306 111.454\n",
"2017-06-21 111.434 111.738 111.035 111.375"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# [From Timestamps to Epoch](https://pandas.pydata.org/pandas-docs/stable/timeseries.html#from-timestamps-to-epoch)\n",
"utc = pd.to_datetime(df['time'], unit='ms')\n",
"utc3 = utc + pd.DateOffset(hours=3)\n",
"data1 = { 'open': df['open'].astype('float64').values, 'high': df['high'].astype('float64').values, 'low': df['low'].astype('float64').values, 'close': df['close'].astype('float64').values}\n",
"columns1 = ['open', 'high', 'low', 'close']\n",
"index1 = utc3\n",
"df2 = pd.DataFrame(data=data1, columns=columns1, index=index1)\n",
"df2.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div id=\"usdjpy\"</div>\n",
" <script type=\"text/javascript\">new Highcharts.Chart({\"title\":{\"text\":\"\\u7c73\\u30c9\\u30eb\\/\\u5186\"},\"legend\":{\"enabled\":true},\"yAxis\":[{}],\"chart\":{\"renderTo\":\"usdjpy\",\"type\":\"line\"},\"xAxis\":{\"title\":{\"text\":\"time\"},\"type\":\"datetime\"},\"series\":[{\"yAxis\":0,\"name\":\"close\",\"data\":[[1497484800000,110.911],[1497571200000,110.89],[1497830400000,111.526],[1497916800000,111.454],[1498003200000,111.375],[1498089600000,111.319],[1498176000000,111.226],[1498435200000,111.86],[1498521600000,112.337],[1498608000000,112.28],[1498694400000,112.164],[1498780800000,112.362],[1499040000000,113.376],[1499126400000,113.261],[1499212800000,113.252],[1499299200000,113.208],[1499385600000,113.91],[1499644800000,114.024],[1499731200000,113.93],[1499817600000,113.149],[1499904000000,113.28],[1499990400000,112.528],[1500249600000,112.617],[1500336000000,112.056],[1500422400000,111.93],[1500508800000,111.893],[1500595200000,111.122],[1500854400000,111.081],[1500940800000,111.872],[1501027200000,111.169],[1501113600000,111.266],[1501200000000,110.671],[1501459200000,110.254],[1501545600000,110.387],[1501632000000,110.741],[1501718400000,110.045],[1501804800000,110.655],[1502064000000,110.729],[1502150400000,110.338],[1502236800000,110.056],[1502323200000,109.209],[1502409600000,109.173],[1502668800000,109.652],[1502755200000,110.662],[1502841600000,110.191],[1502928000000,109.553],[1503014400000,109.185],[1503273600000,108.958],[1503360000000,109.552],[1503446400000,109.032],[1503532800000,109.552],[1503619200000,109.322],[1503878400000,109.23],[1503964800000,109.738],[1504051200000,110.228],[1504137600000,109.96],[1504224000000,110.228],[1504483200000,109.696],[1504569600000,108.8],[1504656000000,109.178],[1504742400000,108.426],[1504828800000,107.836],[1505088000000,109.392],[1505174400000,110.15],[1505260800000,110.486],[1505347200000,110.214],[1505433600000,110.83],[1505692800000,111.57],[1505779200000,111.588],[1505865600000,112.21],[1505952000000,112.456],[1506038400000,111.99],[1506297600000,111.722],[1506384000000,112.21],[1506470400000,112.808],[1506556800000,112.322],[1506643200000,112.5],[1506902400000,112.738],[1506988800000,112.844],[1507075200000,112.75],[1507161600000,112.804],[1507248000000,112.6],[1507507200000,112.672],[1507593600000,112.436],[1507680000000,112.472],[1507766400000,112.272],[1507852800000,111.826],[1508112000000,112.182],[1508198400000,112.185],[1508284800000,112.92],[1508371200000,112.536],[1508457600000,113.508],[1508716800000,113.43],[1508803200000,113.892],[1508889600000,113.736],[1508976000000,113.97],[1509062400000,113.635],[1509321600000,113.166],[1509408000000,113.622],[1509494400000,114.178],[1509580800000,114.084],[1509667200000,114.042],[1509930000000,113.698],[1510016400000,114.002],[1510102800000,113.866],[1510189200000,113.454],[1510275600000,113.505],[1510534800000,113.62],[1510621200000,113.45],[1510707600000,112.876],[1510794000000,113.042],[1510880400000,112.085],[1511139600000,112.614],[1511226000000,112.435],[1511312400000,111.208],[1511398800000,111.199],[1511485200000,111.501],[1511744400000,111.082],[1511830800000,111.465],[1511917200000,111.906],[1512003600000,112.536],[1512090000000,112.164],[1512349200000,112.4],[1512435600000,112.579],[1512522000000,112.284],[1512608400000,113.082],[1512694800000,113.448],[1512954000000,113.545],[1513040400000,113.548],[1513126800000,112.526],[1513213200000,112.38],[1513299600000,112.581],[1513558800000,112.538],[1513645200000,112.884],[1513731600000,113.385],[1513818000000,113.315],[1513904400000,113.265],[1514250000000,113.226],[1514336400000,113.34],[1514422800000,112.856],[1514509200000,112.651],[1514854800000,112.273],[1514941200000,112.502],[1515027600000,112.732],[1515114000000,113.018],[1515373200000,113.082],[1515459600000,112.635],[1515546000000,111.424],[1515632400000,111.244],[1515718800000,110.996],[1515978000000,110.525],[1516064400000,110.438],[1516150800000,111.29],[1516237200000,111.092],[1516323600000,110.814],[1516582800000,110.922],[1516669200000,110.3],[1516755600000,109.218],[1516842000000,109.402],[1516928400000,108.589],[1517187600000,108.952],[1517274000000,108.774],[1517360400000,109.188],[1517446800000,109.382],[1517533200000,110.152],[1517792400000,109.064],[1517878800000,109.554],[1517965200000,109.302],[1518051600000,108.736],[1518138000000,108.77],[1518397200000,108.641],[1518483600000,107.81],[1518570000000,106.998],[1518656400000,106.118],[1518742800000,106.272],[1519002000000,106.58],[1519088400000,107.322],[1519174800000,107.77],[1519261200000,106.742],[1519347600000,106.858],[1519606800000,106.907],[1519693200000,107.32],[1519779600000,106.661],[1519866000000,106.222],[1519952400000,105.712],[1520211600000,106.204],[1520298000000,106.128],[1520384400000,106.059],[1520470800000,106.193],[1520557200000,106.814],[1520812800000,106.41],[1520899200000,106.564],[1520985600000,106.316],[1521072000000,106.327],[1521158400000,105.953],[1521417600000,106.084],[1521504000000,106.526],[1521590400000,106.046],[1521676800000,105.27],[1521763200000,104.712],[1522022400000,105.396],[1522108800000,105.328],[1522195200000,106.842],[1522281600000,106.418],[1522368000000,106.26],[1522627200000,105.877],[1522713600000,106.6],[1522800000000,106.774],[1522886400000,107.376],[1522972800000,106.915],[1523232000000,106.747],[1523318400000,107.188],[1523404800000,106.782],[1523491200000,107.304],[1523577600000,107.338],[1523836800000,107.106],[1523923200000,106.991],[1524009600000,107.225],[1524096000000,107.358],[1524182400000,107.616],[1524441600000,108.7],[1524528000000,108.808],[1524614400000,109.418],[1524700800000,109.294],[1524787200000,109.022],[1525046400000,109.328],[1525132800000,109.862],[1525219200000,109.826],[1525305600000,109.182],[1525392000000,109.056],[1525651200000,109.09],[1525737600000,109.126],[1525824000000,109.742],[1525910400000,109.388],[1525996800000,109.36],[1526256000000,109.652],[1526342400000,110.342],[1526428800000,110.394],[1526515200000,110.75],[1526601600000,110.736]]}]});</script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Basic line plot\n",
"#chart = serialize(df2, render_to=\"my-chart\", title=\"My Chart\")\n",
"# Basic column plot\n",
"#chart = serialize(df, render_to=\"my-chart\", title=\"Test\", kind=\"bar\")\n",
"\n",
"#chart = display_charts(df2, render_to=\"usdjpy\", title=\"米ドル/円\", kind=\"candlestick\")\n",
"# ValueError: candlestick plots are not yet supported\n",
"\n",
"chart = display_charts(df2, render_to=\"usdjpy\", title=\"米ドル/円\", kind=\"line\", y=['close'])"
]
}
],
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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