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Plotting Litecoin Prices
Raw
gistfile1.txt
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<center><h3>Plotting Litecoin Closing Price in US Dollars</h3></center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Litecoin data was obtained [here](http://www.ltc-charts.com/period-charts.php?period=ytd&resolution=day&pair=ltc-usd&market=bitfinex). We will use the BeautifulSoup library to parse the HTML page. Open that web page and view its source to understand why the following code is doing what it is doing."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from bs4 import BeautifulSoup\n",
"import urllib.request as request\n",
"\n",
"html = request.urlopen('http://www.ltc-charts.com/period-charts.php?period=ytd&resolution=day&pair=ltc-usd&market=bitfinex')\n",
"\n",
"soup = BeautifulSoup(html)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Since the data we want is enclosed in the &lt;tbody&gt; tag, we will tell Beautifulsoup to grab data in the tbody tag:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"table_body = soup.tbody\n",
"table_body"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"<tbody>\n",
"<tr>\n",
"<td>2013-11-18</td>\n",
"<td class=\"num\">7.00000 USD</td>\n",
"<td class=\"num\">9.99000 USD</td>\n",
"<td class=\"num\">8.31000 USD</td>\n",
"<td class=\"num\">9.30000 USD</td>\n",
"<td class=\"num\">10,687.61 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-11-19</td>\n",
"<td class=\"num\">6.55000 USD</td>\n",
"<td class=\"num\">10.00000 USD</td>\n",
"<td class=\"num\">9.30000 USD</td>\n",
"<td class=\"num\">7.74000 USD</td>\n",
"<td class=\"num\">22,885.89 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-11-20</td>\n",
"<td class=\"num\">6.00000 USD</td>\n",
"<td class=\"num\">9.40000 USD</td>\n",
"<td class=\"num\">7.74000 USD</td>\n",
"<td class=\"num\">8.35000 USD</td>\n",
"<td class=\"num\">15,508.26 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-11-21</td>\n",
"<td class=\"num\">7.31000 USD</td>\n",
"<td class=\"num\">9.99000 USD</td>\n",
"<td class=\"num\">7.31000 USD</td>\n",
"<td class=\"num\">9.88900 USD</td>\n",
"<td class=\"num\">7,409.77 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-11-22</td>\n",
"<td class=\"num\">8.71499 USD</td>\n",
"<td class=\"num\">10.45000 USD</td>\n",
"<td class=\"num\">9.61000 USD</td>\n",
"<td class=\"num\">10.20000 USD</td>\n",
"<td class=\"num\">7,852.93 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-11-23</td>\n",
"<td class=\"num\">10.10000 USD</td>\n",
"<td class=\"num\">12.28000 USD</td>\n",
"<td class=\"num\">10.10000 USD</td>\n",
"<td class=\"num\">11.49000 USD</td>\n",
"<td class=\"num\">9,675.14 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-11-24</td>\n",
"<td class=\"num\">10.11000 USD</td>\n",
"<td class=\"num\">12.00000 USD</td>\n",
"<td class=\"num\">11.99000 USD</td>\n",
"<td class=\"num\">11.00000 USD</td>\n",
"<td class=\"num\">21,053.20 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-11-25</td>\n",
"<td class=\"num\">10.78000 USD</td>\n",
"<td class=\"num\">13.00000 USD</td>\n",
"<td class=\"num\">11.00000 USD</td>\n",
"<td class=\"num\">12.00500 USD</td>\n",
"<td class=\"num\">11,876.89 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-11-26</td>\n",
"<td class=\"num\">12.00000 USD</td>\n",
"<td class=\"num\">20.66000 USD</td>\n",
"<td class=\"num\">12.25000 USD</td>\n",
"<td class=\"num\">19.30000 USD</td>\n",
"<td class=\"num\">33,083.90 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-11-27</td>\n",
"<td class=\"num\">17.51000 USD</td>\n",
"<td class=\"num\">47.00000 USD</td>\n",
"<td class=\"num\">19.00000 USD</td>\n",
"<td class=\"num\">38.15000 USD</td>\n",
"<td class=\"num\">57,353.21 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-11-28</td>\n",
"<td class=\"num\">25.00000 USD</td>\n",
"<td class=\"num\">55.00000 USD</td>\n",
"<td class=\"num\">36.03000 USD</td>\n",
"<td class=\"num\">40.50000 USD</td>\n",
"<td class=\"num\">66,632.58 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-11-29</td>\n",
"<td class=\"num\">25.50000 USD</td>\n",
"<td class=\"num\">46.00000 USD</td>\n",
"<td class=\"num\">39.50000 USD</td>\n",
"<td class=\"num\">36.69000 USD</td>\n",
"<td class=\"num\">25,133.05 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-11-30</td>\n",
"<td class=\"num\">34.02010 USD</td>\n",
"<td class=\"num\">44.94000 USD</td>\n",
"<td class=\"num\">36.40000 USD</td>\n",
"<td class=\"num\">41.25000 USD</td>\n",
"<td class=\"num\">12,248.14 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-12-07</td>\n",
"<td class=\"num\">13.00000 USD</td>\n",
"<td class=\"num\">28.00000 USD</td>\n",
"<td class=\"num\">16.15100 USD</td>\n",
"<td class=\"num\">21.02300 USD</td>\n",
"<td class=\"num\">13,629.40 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-12-08</td>\n",
"<td class=\"num\">20.52000 USD</td>\n",
"<td class=\"num\">27.98000 USD</td>\n",
"<td class=\"num\">22.99000 USD</td>\n",
"<td class=\"num\">26.50000 USD</td>\n",
"<td class=\"num\">12,219.27 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-12-09</td>\n",
"<td class=\"num\">26.20100 USD</td>\n",
"<td class=\"num\">35.33000 USD</td>\n",
"<td class=\"num\">27.10000 USD</td>\n",
"<td class=\"num\">31.14000 USD</td>\n",
"<td class=\"num\">12,458.78 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-12-10</td>\n",
"<td class=\"num\">30.50000 USD</td>\n",
"<td class=\"num\">37.60900 USD</td>\n",
"<td class=\"num\">31.45100 USD</td>\n",
"<td class=\"num\">35.13000 USD</td>\n",
"<td class=\"num\">8,148.49 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-12-11</td>\n",
"<td class=\"num\">27.00000 USD</td>\n",
"<td class=\"num\">37.46000 USD</td>\n",
"<td class=\"num\">35.13000 USD</td>\n",
"<td class=\"num\">31.40000 USD</td>\n",
"<td class=\"num\">16,705.02 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-12-12</td>\n",
"<td class=\"num\">29.55000 USD</td>\n",
"<td class=\"num\">32.19000 USD</td>\n",
"<td class=\"num\">30.78920 USD</td>\n",
"<td class=\"num\">30.99000 USD</td>\n",
"<td class=\"num\">5,022.61 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-12-13</td>\n",
"<td class=\"num\">29.16000 USD</td>\n",
"<td class=\"num\">33.00000 USD</td>\n",
"<td class=\"num\">31.40000 USD</td>\n",
"<td class=\"num\">30.65000 USD</td>\n",
"<td class=\"num\">6,703.60 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-12-14</td>\n",
"<td class=\"num\">29.21000 USD</td>\n",
"<td class=\"num\">30.99000 USD</td>\n",
"<td class=\"num\">30.65000 USD</td>\n",
"<td class=\"num\">29.22000 USD</td>\n",
"<td class=\"num\">3,191.54 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-12-15</td>\n",
"<td class=\"num\">27.23000 USD</td>\n",
"<td class=\"num\">32.50000 USD</td>\n",
"<td class=\"num\">29.23000 USD</td>\n",
"<td class=\"num\">30.89000 USD</td>\n",
"<td class=\"num\">11,378.15 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-12-16</td>\n",
"<td class=\"num\">22.50000 USD</td>\n",
"<td class=\"num\">31.00000 USD</td>\n",
"<td class=\"num\">30.89000 USD</td>\n",
"<td class=\"num\">24.45000 USD</td>\n",
"<td class=\"num\">30,372.23 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-12-17</td>\n",
"<td class=\"num\">15.50000 USD</td>\n",
"<td class=\"num\">26.00000 USD</td>\n",
"<td class=\"num\">23.60300 USD</td>\n",
"<td class=\"num\">22.15000 USD</td>\n",
"<td class=\"num\">26,209.58 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-12-18</td>\n",
"<td class=\"num\">0.00100 USD</td>\n",
"<td class=\"num\">22.60000 USD</td>\n",
"<td class=\"num\">22.10000 USD</td>\n",
"<td class=\"num\">13.25000 USD</td>\n",
"<td class=\"num\">158,101.91 LTC</td>\n",
"</tr>\n",
"<tr>\n",
"<td>2013-12-19</td>\n",
"<td class=\"num\">12.55000 USD</td>\n",
"<td class=\"num\">19.34000 USD</td>\n",
"<td class=\"num\">13.50000 USD</td>\n",
"<td class=\"num\">18.67000 USD</td>\n",
"<td class=\"num\">24,529.78 LTC</td>\n",
"</tr>\n",
"</tbody>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"But within the &lt;tbody&gt; tag, we really just need the data in the &lt;td&gt; tag:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"table_data = table_body.find_all('td')\n",
"table_data"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"[<td>2013-11-18</td>,\n",
" <td class=\"num\">7.00000 USD</td>,\n",
" <td class=\"num\">9.99000 USD</td>,\n",
" <td class=\"num\">8.31000 USD</td>,\n",
" <td class=\"num\">9.30000 USD</td>,\n",
" <td class=\"num\">10,687.61 LTC</td>,\n",
" <td>2013-11-19</td>,\n",
" <td class=\"num\">6.55000 USD</td>,\n",
" <td class=\"num\">10.00000 USD</td>,\n",
" <td class=\"num\">9.30000 USD</td>,\n",
" <td class=\"num\">7.74000 USD</td>,\n",
" <td class=\"num\">22,885.89 LTC</td>,\n",
" <td>2013-11-20</td>,\n",
" <td class=\"num\">6.00000 USD</td>,\n",
" <td class=\"num\">9.40000 USD</td>,\n",
" <td class=\"num\">7.74000 USD</td>,\n",
" <td class=\"num\">8.35000 USD</td>,\n",
" <td class=\"num\">15,508.26 LTC</td>,\n",
" <td>2013-11-21</td>,\n",
" <td class=\"num\">7.31000 USD</td>,\n",
" <td class=\"num\">9.99000 USD</td>,\n",
" <td class=\"num\">7.31000 USD</td>,\n",
" <td class=\"num\">9.88900 USD</td>,\n",
" <td class=\"num\">7,409.77 LTC</td>,\n",
" <td>2013-11-22</td>,\n",
" <td class=\"num\">8.71499 USD</td>,\n",
" <td class=\"num\">10.45000 USD</td>,\n",
" <td class=\"num\">9.61000 USD</td>,\n",
" <td class=\"num\">10.20000 USD</td>,\n",
" <td class=\"num\">7,852.93 LTC</td>,\n",
" <td>2013-11-23</td>,\n",
" <td class=\"num\">10.10000 USD</td>,\n",
" <td class=\"num\">12.28000 USD</td>,\n",
" <td class=\"num\">10.10000 USD</td>,\n",
" <td class=\"num\">11.49000 USD</td>,\n",
" <td class=\"num\">9,675.14 LTC</td>,\n",
" <td>2013-11-24</td>,\n",
" <td class=\"num\">10.11000 USD</td>,\n",
" <td class=\"num\">12.00000 USD</td>,\n",
" <td class=\"num\">11.99000 USD</td>,\n",
" <td class=\"num\">11.00000 USD</td>,\n",
" <td class=\"num\">21,053.20 LTC</td>,\n",
" <td>2013-11-25</td>,\n",
" <td class=\"num\">10.78000 USD</td>,\n",
" <td class=\"num\">13.00000 USD</td>,\n",
" <td class=\"num\">11.00000 USD</td>,\n",
" <td class=\"num\">12.00500 USD</td>,\n",
" <td class=\"num\">11,876.89 LTC</td>,\n",
" <td>2013-11-26</td>,\n",
" <td class=\"num\">12.00000 USD</td>,\n",
" <td class=\"num\">20.66000 USD</td>,\n",
" <td class=\"num\">12.25000 USD</td>,\n",
" <td class=\"num\">19.30000 USD</td>,\n",
" <td class=\"num\">33,083.90 LTC</td>,\n",
" <td>2013-11-27</td>,\n",
" <td class=\"num\">17.51000 USD</td>,\n",
" <td class=\"num\">47.00000 USD</td>,\n",
" <td class=\"num\">19.00000 USD</td>,\n",
" <td class=\"num\">38.15000 USD</td>,\n",
" <td class=\"num\">57,353.21 LTC</td>,\n",
" <td>2013-11-28</td>,\n",
" <td class=\"num\">25.00000 USD</td>,\n",
" <td class=\"num\">55.00000 USD</td>,\n",
" <td class=\"num\">36.03000 USD</td>,\n",
" <td class=\"num\">40.50000 USD</td>,\n",
" <td class=\"num\">66,632.58 LTC</td>,\n",
" <td>2013-11-29</td>,\n",
" <td class=\"num\">25.50000 USD</td>,\n",
" <td class=\"num\">46.00000 USD</td>,\n",
" <td class=\"num\">39.50000 USD</td>,\n",
" <td class=\"num\">36.69000 USD</td>,\n",
" <td class=\"num\">25,133.05 LTC</td>,\n",
" <td>2013-11-30</td>,\n",
" <td class=\"num\">34.02010 USD</td>,\n",
" <td class=\"num\">44.94000 USD</td>,\n",
" <td class=\"num\">36.40000 USD</td>,\n",
" <td class=\"num\">41.25000 USD</td>,\n",
" <td class=\"num\">12,248.14 LTC</td>,\n",
" <td>2013-12-07</td>,\n",
" <td class=\"num\">13.00000 USD</td>,\n",
" <td class=\"num\">28.00000 USD</td>,\n",
" <td class=\"num\">16.15100 USD</td>,\n",
" <td class=\"num\">21.02300 USD</td>,\n",
" <td class=\"num\">13,629.40 LTC</td>,\n",
" <td>2013-12-08</td>,\n",
" <td class=\"num\">20.52000 USD</td>,\n",
" <td class=\"num\">27.98000 USD</td>,\n",
" <td class=\"num\">22.99000 USD</td>,\n",
" <td class=\"num\">26.50000 USD</td>,\n",
" <td class=\"num\">12,219.27 LTC</td>,\n",
" <td>2013-12-09</td>,\n",
" <td class=\"num\">26.20100 USD</td>,\n",
" <td class=\"num\">35.33000 USD</td>,\n",
" <td class=\"num\">27.10000 USD</td>,\n",
" <td class=\"num\">31.14000 USD</td>,\n",
" <td class=\"num\">12,458.78 LTC</td>,\n",
" <td>2013-12-10</td>,\n",
" <td class=\"num\">30.50000 USD</td>,\n",
" <td class=\"num\">37.60900 USD</td>,\n",
" <td class=\"num\">31.45100 USD</td>,\n",
" <td class=\"num\">35.13000 USD</td>,\n",
" <td class=\"num\">8,148.49 LTC</td>,\n",
" <td>2013-12-11</td>,\n",
" <td class=\"num\">27.00000 USD</td>,\n",
" <td class=\"num\">37.46000 USD</td>,\n",
" <td class=\"num\">35.13000 USD</td>,\n",
" <td class=\"num\">31.40000 USD</td>,\n",
" <td class=\"num\">16,705.02 LTC</td>,\n",
" <td>2013-12-12</td>,\n",
" <td class=\"num\">29.55000 USD</td>,\n",
" <td class=\"num\">32.19000 USD</td>,\n",
" <td class=\"num\">30.78920 USD</td>,\n",
" <td class=\"num\">30.99000 USD</td>,\n",
" <td class=\"num\">5,022.61 LTC</td>,\n",
" <td>2013-12-13</td>,\n",
" <td class=\"num\">29.16000 USD</td>,\n",
" <td class=\"num\">33.00000 USD</td>,\n",
" <td class=\"num\">31.40000 USD</td>,\n",
" <td class=\"num\">30.65000 USD</td>,\n",
" <td class=\"num\">6,703.60 LTC</td>,\n",
" <td>2013-12-14</td>,\n",
" <td class=\"num\">29.21000 USD</td>,\n",
" <td class=\"num\">30.99000 USD</td>,\n",
" <td class=\"num\">30.65000 USD</td>,\n",
" <td class=\"num\">29.22000 USD</td>,\n",
" <td class=\"num\">3,191.54 LTC</td>,\n",
" <td>2013-12-15</td>,\n",
" <td class=\"num\">27.23000 USD</td>,\n",
" <td class=\"num\">32.50000 USD</td>,\n",
" <td class=\"num\">29.23000 USD</td>,\n",
" <td class=\"num\">30.89000 USD</td>,\n",
" <td class=\"num\">11,378.15 LTC</td>,\n",
" <td>2013-12-16</td>,\n",
" <td class=\"num\">22.50000 USD</td>,\n",
" <td class=\"num\">31.00000 USD</td>,\n",
" <td class=\"num\">30.89000 USD</td>,\n",
" <td class=\"num\">24.45000 USD</td>,\n",
" <td class=\"num\">30,372.23 LTC</td>,\n",
" <td>2013-12-17</td>,\n",
" <td class=\"num\">15.50000 USD</td>,\n",
" <td class=\"num\">26.00000 USD</td>,\n",
" <td class=\"num\">23.60300 USD</td>,\n",
" <td class=\"num\">22.15000 USD</td>,\n",
" <td class=\"num\">26,209.58 LTC</td>,\n",
" <td>2013-12-18</td>,\n",
" <td class=\"num\">0.00100 USD</td>,\n",
" <td class=\"num\">22.60000 USD</td>,\n",
" <td class=\"num\">22.10000 USD</td>,\n",
" <td class=\"num\">13.25000 USD</td>,\n",
" <td class=\"num\">158,101.91 LTC</td>,\n",
" <td>2013-12-19</td>,\n",
" <td class=\"num\">12.55000 USD</td>,\n",
" <td class=\"num\">19.34000 USD</td>,\n",
" <td class=\"num\">13.50000 USD</td>,\n",
" <td class=\"num\">18.67000 USD</td>,\n",
" <td class=\"num\">24,529.78 LTC</td>]"
]
}
],
"prompt_number": 18
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Let's just grab the raw data without the HTML tags. But first, let's print the data out to see if the get_text() method does what we want it to do:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for row in table_data:\n",
" print(row.get_text())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2013-11-18\n",
"7.00000 USD\n",
"9.99000 USD\n",
"8.31000 USD\n",
"9.30000 USD\n",
"10,687.61 LTC\n",
"2013-11-19\n",
"6.55000 USD\n",
"10.00000 USD\n",
"9.30000 USD\n",
"7.74000 USD\n",
"22,885.89 LTC\n",
"2013-11-20\n",
"6.00000 USD\n",
"9.40000 USD\n",
"7.74000 USD\n",
"8.35000 USD\n",
"15,508.26 LTC\n",
"2013-11-21\n",
"7.31000 USD\n",
"9.99000 USD\n",
"7.31000 USD\n",
"9.88900 USD\n",
"7,409.77 LTC\n",
"2013-11-22\n",
"8.71499 USD\n",
"10.45000 USD\n",
"9.61000 USD\n",
"10.20000 USD\n",
"7,852.93 LTC\n",
"2013-11-23\n",
"10.10000 USD\n",
"12.28000 USD\n",
"10.10000 USD\n",
"11.49000 USD\n",
"9,675.14 LTC\n",
"2013-11-24\n",
"10.11000 USD\n",
"12.00000 USD\n",
"11.99000 USD\n",
"11.00000 USD\n",
"21,053.20 LTC\n",
"2013-11-25\n",
"10.78000 USD\n",
"13.00000 USD\n",
"11.00000 USD\n",
"12.00500 USD\n",
"11,876.89 LTC\n",
"2013-11-26\n",
"12.00000 USD\n",
"20.66000 USD\n",
"12.25000 USD\n",
"19.30000 USD\n",
"33,083.90 LTC\n",
"2013-11-27\n",
"17.51000 USD\n",
"47.00000 USD\n",
"19.00000 USD\n",
"38.15000 USD\n",
"57,353.21 LTC\n",
"2013-11-28\n",
"25.00000 USD\n",
"55.00000 USD\n",
"36.03000 USD\n",
"40.50000 USD\n",
"66,632.58 LTC\n",
"2013-11-29\n",
"25.50000 USD\n",
"46.00000 USD\n",
"39.50000 USD\n",
"36.69000 USD\n",
"25,133.05 LTC\n",
"2013-11-30\n",
"34.02010 USD\n",
"44.94000 USD\n",
"36.40000 USD\n",
"41.25000 USD\n",
"12,248.14 LTC\n",
"2013-12-07\n",
"13.00000 USD\n",
"28.00000 USD\n",
"16.15100 USD\n",
"21.02300 USD\n",
"13,629.40 LTC\n",
"2013-12-08\n",
"20.52000 USD\n",
"27.98000 USD\n",
"22.99000 USD\n",
"26.50000 USD\n",
"12,219.27 LTC\n",
"2013-12-09\n",
"26.20100 USD\n",
"35.33000 USD\n",
"27.10000 USD\n",
"31.14000 USD\n",
"12,458.78 LTC\n",
"2013-12-10\n",
"30.50000 USD\n",
"37.60900 USD\n",
"31.45100 USD\n",
"35.13000 USD\n",
"8,148.49 LTC\n",
"2013-12-11\n",
"27.00000 USD\n",
"37.46000 USD\n",
"35.13000 USD\n",
"31.40000 USD\n",
"16,705.02 LTC\n",
"2013-12-12\n",
"29.55000 USD\n",
"32.19000 USD\n",
"30.78920 USD\n",
"30.99000 USD\n",
"5,022.61 LTC\n",
"2013-12-13\n",
"29.16000 USD\n",
"33.00000 USD\n",
"31.40000 USD\n",
"30.65000 USD\n",
"6,703.60 LTC\n",
"2013-12-14\n",
"29.21000 USD\n",
"30.99000 USD\n",
"30.65000 USD\n",
"29.22000 USD\n",
"3,191.54 LTC\n",
"2013-12-15\n",
"27.23000 USD\n",
"32.50000 USD\n",
"29.23000 USD\n",
"30.89000 USD\n",
"11,378.15 LTC\n",
"2013-12-16\n",
"22.50000 USD\n",
"31.00000 USD\n",
"30.89000 USD\n",
"24.45000 USD\n",
"30,372.23 LTC\n",
"2013-12-17\n",
"15.50000 USD\n",
"26.00000 USD\n",
"23.60300 USD\n",
"22.15000 USD\n",
"26,209.58 LTC\n",
"2013-12-18\n",
"0.00100 USD\n",
"22.60000 USD\n",
"22.10000 USD\n",
"13.25000 USD\n",
"158,101.91 LTC\n",
"2013-12-19\n",
"12.55000 USD\n",
"19.34000 USD\n",
"13.50000 USD\n",
"18.67000 USD\n",
"24,529.78 LTC\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"So from above, it looks like it does. So, let's store the data in a list"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_list = []\n",
"for row in table_data:\n",
" data_list.append(row.get_text())"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_list"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"['2013-11-18',\n",
" '7.00000 USD',\n",
" '9.99000 USD',\n",
" '8.31000 USD',\n",
" '9.30000 USD',\n",
" '10,687.61 LTC',\n",
" '2013-11-19',\n",
" '6.55000 USD',\n",
" '10.00000 USD',\n",
" '9.30000 USD',\n",
" '7.74000 USD',\n",
" '22,885.89 LTC',\n",
" '2013-11-20',\n",
" '6.00000 USD',\n",
" '9.40000 USD',\n",
" '7.74000 USD',\n",
" '8.35000 USD',\n",
" '15,508.26 LTC',\n",
" '2013-11-21',\n",
" '7.31000 USD',\n",
" '9.99000 USD',\n",
" '7.31000 USD',\n",
" '9.88900 USD',\n",
" '7,409.77 LTC',\n",
" '2013-11-22',\n",
" '8.71499 USD',\n",
" '10.45000 USD',\n",
" '9.61000 USD',\n",
" '10.20000 USD',\n",
" '7,852.93 LTC',\n",
" '2013-11-23',\n",
" '10.10000 USD',\n",
" '12.28000 USD',\n",
" '10.10000 USD',\n",
" '11.49000 USD',\n",
" '9,675.14 LTC',\n",
" '2013-11-24',\n",
" '10.11000 USD',\n",
" '12.00000 USD',\n",
" '11.99000 USD',\n",
" '11.00000 USD',\n",
" '21,053.20 LTC',\n",
" '2013-11-25',\n",
" '10.78000 USD',\n",
" '13.00000 USD',\n",
" '11.00000 USD',\n",
" '12.00500 USD',\n",
" '11,876.89 LTC',\n",
" '2013-11-26',\n",
" '12.00000 USD',\n",
" '20.66000 USD',\n",
" '12.25000 USD',\n",
" '19.30000 USD',\n",
" '33,083.90 LTC',\n",
" '2013-11-27',\n",
" '17.51000 USD',\n",
" '47.00000 USD',\n",
" '19.00000 USD',\n",
" '38.15000 USD',\n",
" '57,353.21 LTC',\n",
" '2013-11-28',\n",
" '25.00000 USD',\n",
" '55.00000 USD',\n",
" '36.03000 USD',\n",
" '40.50000 USD',\n",
" '66,632.58 LTC',\n",
" '2013-11-29',\n",
" '25.50000 USD',\n",
" '46.00000 USD',\n",
" '39.50000 USD',\n",
" '36.69000 USD',\n",
" '25,133.05 LTC',\n",
" '2013-11-30',\n",
" '34.02010 USD',\n",
" '44.94000 USD',\n",
" '36.40000 USD',\n",
" '41.25000 USD',\n",
" '12,248.14 LTC',\n",
" '2013-12-07',\n",
" '13.00000 USD',\n",
" '28.00000 USD',\n",
" '16.15100 USD',\n",
" '21.02300 USD',\n",
" '13,629.40 LTC',\n",
" '2013-12-08',\n",
" '20.52000 USD',\n",
" '27.98000 USD',\n",
" '22.99000 USD',\n",
" '26.50000 USD',\n",
" '12,219.27 LTC',\n",
" '2013-12-09',\n",
" '26.20100 USD',\n",
" '35.33000 USD',\n",
" '27.10000 USD',\n",
" '31.14000 USD',\n",
" '12,458.78 LTC',\n",
" '2013-12-10',\n",
" '30.50000 USD',\n",
" '37.60900 USD',\n",
" '31.45100 USD',\n",
" '35.13000 USD',\n",
" '8,148.49 LTC',\n",
" '2013-12-11',\n",
" '27.00000 USD',\n",
" '37.46000 USD',\n",
" '35.13000 USD',\n",
" '31.40000 USD',\n",
" '16,705.02 LTC',\n",
" '2013-12-12',\n",
" '29.55000 USD',\n",
" '32.19000 USD',\n",
" '30.78920 USD',\n",
" '30.99000 USD',\n",
" '5,022.61 LTC',\n",
" '2013-12-13',\n",
" '29.16000 USD',\n",
" '33.00000 USD',\n",
" '31.40000 USD',\n",
" '30.65000 USD',\n",
" '6,703.60 LTC',\n",
" '2013-12-14',\n",
" '29.21000 USD',\n",
" '30.99000 USD',\n",
" '30.65000 USD',\n",
" '29.22000 USD',\n",
" '3,191.54 LTC',\n",
" '2013-12-15',\n",
" '27.23000 USD',\n",
" '32.50000 USD',\n",
" '29.23000 USD',\n",
" '30.89000 USD',\n",
" '11,378.15 LTC',\n",
" '2013-12-16',\n",
" '22.50000 USD',\n",
" '31.00000 USD',\n",
" '30.89000 USD',\n",
" '24.45000 USD',\n",
" '30,372.23 LTC',\n",
" '2013-12-17',\n",
" '15.50000 USD',\n",
" '26.00000 USD',\n",
" '23.60300 USD',\n",
" '22.15000 USD',\n",
" '26,209.58 LTC',\n",
" '2013-12-18',\n",
" '0.00100 USD',\n",
" '22.60000 USD',\n",
" '22.10000 USD',\n",
" '13.25000 USD',\n",
" '158,101.91 LTC',\n",
" '2013-12-19',\n",
" '12.55000 USD',\n",
" '19.34000 USD',\n",
" '13.50000 USD',\n",
" '18.67000 USD',\n",
" '24,529.78 LTC']"
]
}
],
"prompt_number": 23
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Let's use Python's handy list slicing to grab the data we want"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dates = data_list[0::6] # grab the 1st item and every 6th item afterwards"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dates"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"['2013-11-18',\n",
" '2013-11-19',\n",
" '2013-11-20',\n",
" '2013-11-21',\n",
" '2013-11-22',\n",
" '2013-11-23',\n",
" '2013-11-24',\n",
" '2013-11-25',\n",
" '2013-11-26',\n",
" '2013-11-27',\n",
" '2013-11-28',\n",
" '2013-11-29',\n",
" '2013-11-30',\n",
" '2013-12-07',\n",
" '2013-12-08',\n",
" '2013-12-09',\n",
" '2013-12-10',\n",
" '2013-12-11',\n",
" '2013-12-12',\n",
" '2013-12-13',\n",
" '2013-12-14',\n",
" '2013-12-15',\n",
" '2013-12-16',\n",
" '2013-12-17',\n",
" '2013-12-18',\n",
" '2013-12-19']"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"close_price = data_list[4::6]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"close_price"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": [
"['9.30000 USD',\n",
" '7.74000 USD',\n",
" '8.35000 USD',\n",
" '9.88900 USD',\n",
" '10.20000 USD',\n",
" '11.49000 USD',\n",
" '11.00000 USD',\n",
" '12.00500 USD',\n",
" '19.30000 USD',\n",
" '38.15000 USD',\n",
" '40.50000 USD',\n",
" '36.69000 USD',\n",
" '41.25000 USD',\n",
" '21.02300 USD',\n",
" '26.50000 USD',\n",
" '31.14000 USD',\n",
" '35.13000 USD',\n",
" '31.40000 USD',\n",
" '30.99000 USD',\n",
" '30.65000 USD',\n",
" '29.22000 USD',\n",
" '30.89000 USD',\n",
" '24.45000 USD',\n",
" '22.15000 USD',\n",
" '13.25000 USD',\n",
" '18.67000 USD']"
]
}
],
"prompt_number": 29
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Since we want to plot the closing price, we need to get rid of the \" USD\". We can replace it with an emply space \"\""
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"price = []\n",
"for row in close_price:\n",
" price.append(float(row.replace(\" USD\",\"\")))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 31
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Looks like that worked:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"price"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 32,
"text": [
"[9.3,\n",
" 7.74,\n",
" 8.35,\n",
" 9.889,\n",
" 10.2,\n",
" 11.49,\n",
" 11.0,\n",
" 12.005,\n",
" 19.3,\n",
" 38.15,\n",
" 40.5,\n",
" 36.69,\n",
" 41.25,\n",
" 21.023,\n",
" 26.5,\n",
" 31.14,\n",
" 35.13,\n",
" 31.4,\n",
" 30.99,\n",
" 30.65,\n",
" 29.22,\n",
" 30.89,\n",
" 24.45,\n",
" 22.15,\n",
" 13.25,\n",
" 18.67]"
]
}
],
"prompt_number": 32
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"One more problem, the dates list we made stored dates in String format, we want them to be actual Python date objects so that MATPLOTLIB will plot them. So we'll convert str to Python dates:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from datetime import datetime\n",
"dates_final = []\n",
"for date in dates:\n",
" dates_final.append(datetime.strptime(date, \"%Y-%m-%d\"))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 34
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Looks like that worked:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dates_final"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 35,
"text": [
"[datetime.datetime(2013, 11, 18, 0, 0),\n",
" datetime.datetime(2013, 11, 19, 0, 0),\n",
" datetime.datetime(2013, 11, 20, 0, 0),\n",
" datetime.datetime(2013, 11, 21, 0, 0),\n",
" datetime.datetime(2013, 11, 22, 0, 0),\n",
" datetime.datetime(2013, 11, 23, 0, 0),\n",
" datetime.datetime(2013, 11, 24, 0, 0),\n",
" datetime.datetime(2013, 11, 25, 0, 0),\n",
" datetime.datetime(2013, 11, 26, 0, 0),\n",
" datetime.datetime(2013, 11, 27, 0, 0),\n",
" datetime.datetime(2013, 11, 28, 0, 0),\n",
" datetime.datetime(2013, 11, 29, 0, 0),\n",
" datetime.datetime(2013, 11, 30, 0, 0),\n",
" datetime.datetime(2013, 12, 7, 0, 0),\n",
" datetime.datetime(2013, 12, 8, 0, 0),\n",
" datetime.datetime(2013, 12, 9, 0, 0),\n",
" datetime.datetime(2013, 12, 10, 0, 0),\n",
" datetime.datetime(2013, 12, 11, 0, 0),\n",
" datetime.datetime(2013, 12, 12, 0, 0),\n",
" datetime.datetime(2013, 12, 13, 0, 0),\n",
" datetime.datetime(2013, 12, 14, 0, 0),\n",
" datetime.datetime(2013, 12, 15, 0, 0),\n",
" datetime.datetime(2013, 12, 16, 0, 0),\n",
" datetime.datetime(2013, 12, 17, 0, 0),\n",
" datetime.datetime(2013, 12, 18, 0, 0),\n",
" datetime.datetime(2013, 12, 19, 0, 0)]"
]
}
],
"prompt_number": 35
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Now that we have the data we need in their proper format, we are ready to plot:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt\n",
"from matplotlib.dates import YearLocator, MonthLocator, WeekdayLocator, DateFormatter\n",
"from matplotlib.ticker import MultipleLocator\n",
"\n",
"# These are just time intervals we can use to format our X axis tick intervals\n",
"year = YearLocator()\n",
"month = MonthLocator(bymonth=range(1,13), bymonthday=1, interval=1)\n",
"week = WeekdayLocator(byweekday=MO) # Every MOnday\n",
"day = DayLocator(bymonthday=range(1,32), interval=1)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_axes([0, 0, 2, 1]) # left, bottom, width, height\n",
"\n",
"# Format the x-axis, set tick labels as Month-dd format, turn on grid for major ticks\n",
"#dayFmt = DateFormatter(\"%b-%d\")\n",
"dayFmt = DateFormatter(\"%b-%d\")\n",
"ax.xaxis.set_major_formatter(dayFmt) # Make the x-axis tick interval to be monthly interval size\n",
"ax.xaxis.set_major_locator(day)\n",
"ax.xaxis.grid(which='major')\n",
"\n",
"# Format the y-axis\n",
"y_major_ticks = MultipleLocator(5)\n",
"ax.yaxis.set_major_locator(y_major_ticks)\n",
"ax.yaxis.set_ticks_position('right') # Not sure why 'both' don't work\n",
"ax.yaxis.set_label_position('right')\n",
"ax.yaxis.grid(which='major')\n",
"\n",
"# Plot the data...\n",
"plt.plot_date(dates_final, price, 'r')\n",
"ax.set_title(\"Litecoin Value in US$\", weight=\"bold\")\n",
"ax.set_xlabel(\"Month-Day\")\n",
"ax.set_ylabel(\"US$\")\n",
"plt.setp(ax.get_xticklabels(), rotation=-90)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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aSpUqsWHDBrxeL5s3b6ZPnz4AREdHs3nzZjZt2sScOXMIDQ21rAbHN5EiYqED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\n4t7+MLQAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x6c10110>"
]
}
],
"prompt_number": 70
}
],
"metadata": {}
}
]
}
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