diff --git a/codigo/.gitignore b/codigo/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..87620ac7e74efee566c6ee9d2ed7281ebafb4788
--- /dev/null
+++ b/codigo/.gitignore
@@ -0,0 +1 @@
+.ipynb_checkpoints/
diff --git a/codigo/Proyecto_clase08_LH.ipynb b/codigo/Proyecto_clase08_LH.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..16ab872ca936fe275caba73df04e1ef4f6068062
--- /dev/null
+++ b/codigo/Proyecto_clase08_LH.ipynb
@@ -0,0 +1,1929 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import leastsq\n",
+    "import math\n",
+    "import statistics as stats\n",
+    "import csv\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "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>Position</th>\n",
+       "      <th>team</th>\n",
+       "      <th>C1</th>\n",
+       "      <th>C2</th>\n",
+       "      <th>C3</th>\n",
+       "      <th>C4</th>\n",
+       "      <th>C5</th>\n",
+       "      <th>C6</th>\n",
+       "      <th>C7</th>\n",
+       "      <th>C8</th>\n",
+       "      <th>...</th>\n",
+       "      <th>C13</th>\n",
+       "      <th>C14</th>\n",
+       "      <th>C15</th>\n",
+       "      <th>C16</th>\n",
+       "      <th>C17</th>\n",
+       "      <th>C18</th>\n",
+       "      <th>C19</th>\n",
+       "      <th>C20</th>\n",
+       "      <th>Avg</th>\n",
+       "      <th>Points</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Leicester</td>\n",
+       "      <td>―</td>\n",
+       "      <td>2:5</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2:1</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>4:2</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>3:2</td>\n",
+       "      <td>32</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Arsenal</td>\n",
+       "      <td>2:1</td>\n",
+       "      <td>―</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>2:1</td>\n",
+       "      <td>3:0</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>0:2</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4:0</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>3:1</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>4:0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Tottenham</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>―</td>\n",
+       "      <td>4:1</td>\n",
+       "      <td>3:0</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>4:1</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>3:0</td>\n",
+       "      <td>4:1</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>3:0</td>\n",
+       "      <td>3:1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Man. City</td>\n",
+       "      <td>1:3</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>―</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>3:1</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>1:4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>2:1</td>\n",
+       "      <td>4:0</td>\n",
+       "      <td>5:1</td>\n",
+       "      <td>4:1</td>\n",
+       "      <td>6:1</td>\n",
+       "      <td>2:1</td>\n",
+       "      <td>4:0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>Man. United</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>3:2</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>―</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>3:1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>3:1</td>\n",
+       "      <td>3:0</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>4:0</td>\n",
+       "      <td>0:2</td>\n",
+       "      <td>4:2</td>\n",
+       "      <td>2:3</td>\n",
+       "      <td>―</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>3:2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>3:0</td>\n",
+       "      <td>4:1</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>3:1</td>\n",
+       "      <td>3:0</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>West Ham</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>3:3</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>3:2</td>\n",
+       "      <td>2:1</td>\n",
+       "      <td>―</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3:1</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>3:4</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>Liverpool</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>3:3</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>3:0</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>0:3</td>\n",
+       "      <td>―</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>3:2</td>\n",
+       "      <td>13</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>Stoke</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>0:4</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>2:1</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0:2</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>2:1</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>3:1</td>\n",
+       "      <td>2:1</td>\n",
+       "      <td>-14</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Chelsea</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>0:3</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>1:3</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>1:3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>3:1</td>\n",
+       "      <td>5:1</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>Everton</td>\n",
+       "      <td>2:3</td>\n",
+       "      <td>0:2</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>0:2</td>\n",
+       "      <td>0:3</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>2:3</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>2:1</td>\n",
+       "      <td>6:2</td>\n",
+       "      <td>3:0</td>\n",
+       "      <td>3:0</td>\n",
+       "      <td>4:0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>Swansea</td>\n",
+       "      <td>0:3</td>\n",
+       "      <td>0:3</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>2:1</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>3:1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>2:4</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>-10</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>Watford</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>0:3</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>3:0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>―</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>2:1</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>3:2</td>\n",
+       "      <td>-10</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>Bromwich</td>\n",
+       "      <td>2:3</td>\n",
+       "      <td>2:1</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>0:3</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>0:3</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>―</td>\n",
+       "      <td>3:2</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>-14</td>\n",
+       "      <td>43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>Palace</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>1:3</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>1:3</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>―</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>5:1</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>2:1</td>\n",
+       "      <td>-12</td>\n",
+       "      <td>42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>Bournemouth</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>0:2</td>\n",
+       "      <td>1:5</td>\n",
+       "      <td>0:4</td>\n",
+       "      <td>2:1</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>1:3</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>―</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>3:0</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>-22</td>\n",
+       "      <td>42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>Sunderland</td>\n",
+       "      <td>0:2</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>2:1</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>―</td>\n",
+       "      <td>3:0</td>\n",
+       "      <td>1:3</td>\n",
+       "      <td>3:1</td>\n",
+       "      <td>-14</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>Newcastle</td>\n",
+       "      <td>0:3</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>5:1</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>3:3</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>2:1</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>1:3</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>―</td>\n",
+       "      <td>6:2</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>-21</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>Norwich</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>0:3</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>4:5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4:2</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>1:3</td>\n",
+       "      <td>3:1</td>\n",
+       "      <td>0:3</td>\n",
+       "      <td>3:2</td>\n",
+       "      <td>―</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>-28</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>Villa</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>0:2</td>\n",
+       "      <td>0:2</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>2:4</td>\n",
+       "      <td>1:1</td>\n",
+       "      <td>0:6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2:3</td>\n",
+       "      <td>0:1</td>\n",
+       "      <td>1:0</td>\n",
+       "      <td>1:2</td>\n",
+       "      <td>2:2</td>\n",
+       "      <td>0:0</td>\n",
+       "      <td>2:0</td>\n",
+       "      <td>―</td>\n",
+       "      <td>-49</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Position         team   C1   C2   C3   C4   C5   C6   C7   C8  ...  C13  \\\n",
+       "0        1.0    Leicester    ―  2:5  1:1  0:0  1:1  1:0  2:2  2:0  ...  2:1   \n",
+       "1        2.0      Arsenal  2:1    ―  1:1  2:1  3:0  0:0  0:2  0:0  ...  4:0   \n",
+       "2        3.0    Tottenham  0:1  2:2    ―  4:1  3:0  1:2  4:1  0:0  ...  1:0   \n",
+       "3        4.0    Man. City  1:3  2:2  1:2    ―  0:1  3:1  1:2  1:4  ...  2:0   \n",
+       "4        5.0  Man. United  1:1  3:2  1:0  0:0    ―  0:1  0:0  3:1  ...  1:0   \n",
+       "5        6.0  Southampton  2:2  4:0  0:2  4:2  2:3    ―  1:0  3:2  ...  2:0   \n",
+       "6        7.0     West Ham  1:2  3:3  1:0  2:2  3:2  2:1    ―  2:0  ...  3:1   \n",
+       "7        8.0    Liverpool  1:0  3:3  1:1  3:0  0:1  1:1  0:3    ―  ...  2:0   \n",
+       "8        9.0        Stoke  2:2  0:0  0:4  2:0  2:0  1:2  2:1  0:1  ...  0:2   \n",
+       "9       10.0      Chelsea  1:1  2:0  2:2  0:3  1:1  1:3  2:2  1:3  ...  2:2   \n",
+       "10      11.0      Everton  2:3  0:2  1:1  0:2  0:3  1:1  2:3  1:1  ...  2:2   \n",
+       "11      12.0      Swansea  0:3  0:3  2:2  1:1  2:1  0:1  0:0  3:1  ...  1:0   \n",
+       "12      13.0      Watford  0:1  0:3  1:2  1:2  1:2  0:0  2:0  3:0  ...    ―   \n",
+       "13      14.0     Bromwich  2:3  2:1  1:1  0:3  1:0  0:0  0:3  1:1  ...  0:1   \n",
+       "14      15.0       Palace  0:1  1:2  1:3  0:1  0:0  1:0  1:3  1:2  ...  1:2   \n",
+       "15      16.0  Bournemouth  1:1  0:2  1:5  0:4  2:1  2:0  1:3  1:2  ...  1:1   \n",
+       "16      17.0   Sunderland  0:2  0:0  0:1  0:1  2:1  0:1  2:2  0:1  ...  0:1   \n",
+       "17      18.0    Newcastle  0:3  0:1  5:1  1:1  3:3  2:2  2:1  2:0  ...  1:2   \n",
+       "18      19.0      Norwich  1:2  1:1  0:3  0:0  0:1  1:0  2:2  4:5  ...  4:2   \n",
+       "19      20.0        Villa  1:1  0:2  0:2  0:0  0:1  2:4  1:1  0:6  ...  2:3   \n",
+       "\n",
+       "    C14  C15  C16  C17  C18  C19  C20 Avg Points  \n",
+       "0   2:2  1:0  0:0  4:2  1:0  1:0  3:2  32     81  \n",
+       "1   2:0  1:1  2:0  3:1  1:0  1:0  4:0  29     71  \n",
+       "2   1:1  1:0  3:0  4:1  1:2  3:0  3:1  34     70  \n",
+       "3   2:1  4:0  5:1  4:1  6:1  2:1  4:0  30     66  \n",
+       "4   2:0  2:0  3:1  3:0  0:0  1:2  1:0  14     66  \n",
+       "5   3:0  4:1  2:0  1:1  3:1  3:0  1:1  18     63  \n",
+       "6   1:1  2:2  3:4  1:0  2:0  2:2  2:0  14     62  \n",
+       "7   2:2  1:2  1:0  2:2  2:2  1:1  3:2  13     60  \n",
+       "8   0:1  1:2  2:1  1:1  1:0  3:1  2:1 -14     51  \n",
+       "9   2:2  1:2  0:1  3:1  5:1  1:0  2:0   6     50  \n",
+       "10  0:1  1:1  2:1  6:2  3:0  3:0  4:0   4     47  \n",
+       "11  1:0  1:1  2:2  2:4  2:0  1:0  1:0 -10     47  \n",
+       "12  0:0  0:1  0:0  2:2  2:1  2:0  3:2 -10     45  \n",
+       "13    ―  3:2  1:2  1:0  1:0  0:1  0:0 -14     43  \n",
+       "14  2:0    ―  1:2  0:1  5:1  1:0  2:1 -12     42  \n",
+       "15  1:1  0:0    ―  2:0  0:1  3:0  0:1 -22     42  \n",
+       "16  0:0  2:2  1:1    ―  3:0  1:3  3:1 -14     39  \n",
+       "17  1:0  1:0  1:3  1:1    ―  6:2  1:1 -21     37  \n",
+       "18  0:1  1:3  3:1  0:3  3:2    ―  2:0 -28     34  \n",
+       "19  0:1  1:0  1:2  2:2  0:0  2:0    ― -49     17  \n",
+       "\n",
+       "[20 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Year2015_2016 = pd.read_csv('../data-used/2015-2016PL', sep='\\t', names=['Position','team','C1','C2','C3','C4','C5'\n",
+    "                                                                        ,'C6','C7','C8','C9','C10','C11','C12','C13'\n",
+    "                                                                        ,'C14','C15','C16','C17','C18','C19','C20'\n",
+    "                                                                        ,'Avg','Points'])\n",
+    "Year2015_2016"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataframes = pd.concat([Year2015_2016])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#asignamos los datos de cada columna\n",
+    "clubes = dataframes.team\n",
+    "AVGgoles = dataframes.Avg\n",
+    "puntos = dataframes.Points\n",
+    "resultados1 = dataframes.C1\n",
+    "resultados2 = dataframes.C2\n",
+    "resultados3 = dataframes.C3\n",
+    "resultados4 = dataframes.C4\n",
+    "resultados5 = dataframes.C5\n",
+    "resultados6 = dataframes.C6\n",
+    "resultados7 = dataframes.C7\n",
+    "resultados8 = dataframes.C8\n",
+    "resultados9 = dataframes.C9\n",
+    "resultados10 = dataframes.C10\n",
+    "resultados11 = dataframes.C11\n",
+    "resultados12 = dataframes.C12\n",
+    "resultados13 = dataframes.C13\n",
+    "resultados14 = dataframes.C14\n",
+    "resultados15 = dataframes.C15\n",
+    "resultados16 = dataframes.C16\n",
+    "resultados17 = dataframes.C17\n",
+    "resultados18 = dataframes.C18\n",
+    "resultados19 = dataframes.C19\n",
+    "resultados20 = dataframes.C20\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (25,8)) #tamano del grafico\n",
+    "plt.title('Average de goles Premier League 2015-2016', fontsize = 16, fontweight = 'bold') #titulo del grafico\n",
+    "#titulo de los ejes\n",
+    "plt.xlabel('Clubes', fontweight = 'bold') \n",
+    "plt.ylabel('Goles',fontweight = 'bold')\n",
+    "plt.plot(clubes,AVGgoles,color='b')\n",
+    "plt.scatter(clubes,AVGgoles,color='r')\n",
+    "plt.grid()  \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,8)) #tamano del grafico\n",
+    "plt.title('Average de goles vs Puntos Obtenidos Premier League 2015-2016', fontsize = 16, fontweight = 'bold') #titulo del grafico\n",
+    "#titulo de los ejes\n",
+    "plt.xlabel('Puntos', fontweight = 'bold') \n",
+    "plt.ylabel('Goles',fontweight = 'bold')\n",
+    "\n",
+    "plt.scatter(puntos,AVGgoles,color='b')\n",
+    "plt.grid()  \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       ―\n",
+       "1     2:1\n",
+       "2     0:1\n",
+       "3     1:3\n",
+       "4     1:1\n",
+       "5     2:2\n",
+       "6     1:2\n",
+       "7     1:0\n",
+       "8     2:2\n",
+       "9     1:1\n",
+       "10    2:3\n",
+       "11    0:3\n",
+       "12    0:1\n",
+       "13    2:3\n",
+       "14    0:1\n",
+       "15    1:1\n",
+       "16    0:2\n",
+       "17    0:3\n",
+       "18    1:2\n",
+       "19    1:1\n",
+       "Name: C1, dtype: object"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resultados1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_of_single_column = dataframes['C1'].tolist()\n",
+    "list_of_single_column.remove(\"―\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['2:1',\n",
+       " '0:1',\n",
+       " '1:3',\n",
+       " '1:1',\n",
+       " '2:2',\n",
+       " '1:2',\n",
+       " '1:0',\n",
+       " '2:2',\n",
+       " '1:1',\n",
+       " '2:3',\n",
+       " '0:3',\n",
+       " '0:1',\n",
+       " '2:3',\n",
+       " '0:1',\n",
+       " '1:1',\n",
+       " '0:2',\n",
+       " '0:3',\n",
+       " '1:2',\n",
+       " '1:1']"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_of_single_column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 - 1\n",
+      "0 - 1\n",
+      "1 - 3\n",
+      "1 - 1\n",
+      "2 - 2\n",
+      "1 - 2\n",
+      "1 - 0\n",
+      "2 - 2\n",
+      "1 - 1\n",
+      "2 - 3\n",
+      "0 - 3\n",
+      "0 - 1\n",
+      "2 - 3\n",
+      "0 - 1\n",
+      "1 - 1\n",
+      "0 - 2\n",
+      "0 - 3\n",
+      "1 - 2\n",
+      "1 - 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "home = []\n",
+    "away = []\n",
+    "for results in list_of_single_column:\n",
+    "    h  = results.split(':')[0]\n",
+    "    a  = results.split(':')[1]\n",
+    "    home.append(h)\n",
+    "    away.append(a)\n",
+    "    print(h, \"-\" ,a) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['2', '0', '1', '1', '2', '1', '1', '2', '1', '2', '0', '0', '2', '0', '1', '0', '0', '1', '1'] -- ['1', '1', '3', '1', '2', '2', '0', '2', '1', '3', '3', '1', '3', '1', '1', '2', '3', '2', '1']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(home,(\"--\"), away)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplot(121)\n",
+    "plt.title('Frecuencia de goles de local Premier League 2015-2016', fontsize = 12, fontweight = 'bold') \n",
+    "#titulo de los ejes\n",
+    "plt.xlabel('Goles Home', fontweight = 'bold') \n",
+    "plt.ylabel('frecuencia',fontweight = 'bold')\n",
+    "plt.hist(home, bins=10)\n",
+    "plt.show()\n",
+    "plt.clf()\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.subplot(122)\n",
+    "plt.title('Frecuencia de goles de Visita Premier League 2015-2016', fontsize = 12, fontweight = 'bold') \n",
+    "#titulo de los ejes\n",
+    "plt.xlabel('Goles Away', fontweight = 'bold') \n",
+    "plt.ylabel('frecuencia',fontweight = 'bold')\n",
+    "plt.hist(away, bins=10)\n",
+    "plt.show()\n",
+    "plt.clf()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#realizamos el proceso para otras columnas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_of_single_column2 = dataframes['C2'].tolist()\n",
+    "list_of_single_column2.remove(\"―\") \n",
+    "#list_of_single_column2\n",
+    "list_of_single_column3 = dataframes['C3'].tolist()\n",
+    "list_of_single_column3.remove(\"―\") \n",
+    "#list_of_single_column3\n",
+    "list_of_single_column4 = dataframes['C4'].tolist()\n",
+    "list_of_single_column4.remove(\"―\") \n",
+    "#list_of_single_column4\n",
+    "list_of_single_column5 = dataframes['C5'].tolist()\n",
+    "list_of_single_column5.remove(\"―\") \n",
+    "#list_of_single_column5\n",
+    "list_of_single_column6 = dataframes['C6'].tolist()\n",
+    "list_of_single_column6.remove(\"―\") \n",
+    "#list_of_single_column6\n",
+    "list_of_single_column7 = dataframes['C7'].tolist()\n",
+    "list_of_single_column7.remove(\"―\") \n",
+    "#list_of_single_column7\n",
+    "list_of_single_column8 = dataframes['C8'].tolist()\n",
+    "list_of_single_column8.remove(\"―\") \n",
+    "#list_of_single_column8\n",
+    "list_of_single_column9 = dataframes['C9'].tolist()\n",
+    "list_of_single_column9.remove(\"―\") \n",
+    "#list_of_single_column9\n",
+    "list_of_single_column10 = dataframes['C10'].tolist()\n",
+    "list_of_single_column10.remove(\"―\") \n",
+    "#list_of_single_column10\n",
+    "list_of_single_column11 = dataframes['C11'].tolist()\n",
+    "list_of_single_column11.remove(\"―\") \n",
+    "#list_of_single_column11\n",
+    "list_of_single_column12 = dataframes['C12'].tolist()\n",
+    "list_of_single_column12.remove(\"―\") \n",
+    "#list_of_single_column12\n",
+    "list_of_single_column13 = dataframes['C13'].tolist()\n",
+    "list_of_single_column13.remove(\"―\") \n",
+    "#list_of_single_column13\n",
+    "list_of_single_column14 = dataframes['C14'].tolist()\n",
+    "list_of_single_column14.remove(\"―\") \n",
+    "#list_of_single_column14\n",
+    "list_of_single_column15 = dataframes['C15'].tolist()\n",
+    "list_of_single_column15.remove(\"―\") \n",
+    "#list_of_single_column15\n",
+    "list_of_single_column16 = dataframes['C16'].tolist()\n",
+    "list_of_single_column16.remove(\"―\") \n",
+    "#list_of_single_column16\n",
+    "list_of_single_column17 = dataframes['C17'].tolist()\n",
+    "list_of_single_column17.remove(\"―\") \n",
+    "#list_of_single_column17\n",
+    "list_of_single_column18 = dataframes['C18'].tolist()\n",
+    "list_of_single_column18.remove(\"―\") \n",
+    "#list_of_single_column18\n",
+    "list_of_single_column19 = dataframes['C19'].tolist()\n",
+    "list_of_single_column19.remove(\"―\") \n",
+    "#list_of_single_column19\n",
+    "list_of_single_column20 = dataframes['C20'].tolist()\n",
+    "list_of_single_column20.remove(\"―\") \n",
+    "#list_of_single_column20\n",
+    "\n",
+    "resultados = (list_of_single_column + list_of_single_column2 + list_of_single_column3 + list_of_single_column4 \n",
+    "+ list_of_single_column5 + list_of_single_column6 + list_of_single_column7 + list_of_single_column8 \n",
+    "+ list_of_single_column9 + list_of_single_column10 + list_of_single_column11 + list_of_single_column12\n",
+    "+ list_of_single_column13 + list_of_single_column14 + list_of_single_column15 + list_of_single_column16\n",
+    "+ list_of_single_column17 + list_of_single_column18 + list_of_single_column19 + list_of_single_column20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['2:1',\n",
+       " '0:1',\n",
+       " '1:3',\n",
+       " '1:1',\n",
+       " '2:2',\n",
+       " '1:2',\n",
+       " '1:0',\n",
+       " '2:2',\n",
+       " '1:1',\n",
+       " '2:3',\n",
+       " '0:3',\n",
+       " '0:1',\n",
+       " '2:3',\n",
+       " '0:1',\n",
+       " '1:1',\n",
+       " '0:2',\n",
+       " '0:3',\n",
+       " '1:2',\n",
+       " '1:1',\n",
+       " '2:5',\n",
+       " '2:2',\n",
+       " '2:2',\n",
+       " '3:2',\n",
+       " '4:0',\n",
+       " '3:3',\n",
+       " '3:3',\n",
+       " '0:0',\n",
+       " '2:0',\n",
+       " '0:2',\n",
+       " '0:3',\n",
+       " '0:3',\n",
+       " '2:1',\n",
+       " '1:2',\n",
+       " '0:2',\n",
+       " '0:0',\n",
+       " '0:1',\n",
+       " '1:1',\n",
+       " '0:2',\n",
+       " '1:1',\n",
+       " '1:1',\n",
+       " '1:2',\n",
+       " '1:0',\n",
+       " '0:2',\n",
+       " '1:0',\n",
+       " '1:1',\n",
+       " '0:4',\n",
+       " '2:2',\n",
+       " '1:1',\n",
+       " '2:2',\n",
+       " '1:2',\n",
+       " '1:1',\n",
+       " '1:3',\n",
+       " '1:5',\n",
+       " '0:1',\n",
+       " '5:1',\n",
+       " '0:3',\n",
+       " '0:2',\n",
+       " '0:0',\n",
+       " '2:1',\n",
+       " '4:1',\n",
+       " '0:0',\n",
+       " '4:2',\n",
+       " '2:2',\n",
+       " '3:0',\n",
+       " '2:0',\n",
+       " '0:3',\n",
+       " '0:2',\n",
+       " '1:1',\n",
+       " '1:2',\n",
+       " '0:3',\n",
+       " '0:1',\n",
+       " '0:4',\n",
+       " '0:1',\n",
+       " '1:1',\n",
+       " '0:0',\n",
+       " '0:0',\n",
+       " '1:1',\n",
+       " '3:0',\n",
+       " '3:0',\n",
+       " '0:1',\n",
+       " '2:3',\n",
+       " '3:2',\n",
+       " '0:1',\n",
+       " '2:0',\n",
+       " '1:1',\n",
+       " '0:3',\n",
+       " '2:1',\n",
+       " '1:2',\n",
+       " '1:0',\n",
+       " '0:0',\n",
+       " '2:1',\n",
+       " '2:1',\n",
+       " '3:3',\n",
+       " '0:1',\n",
+       " '0:1',\n",
+       " '1:0',\n",
+       " '0:0',\n",
+       " '1:2',\n",
+       " '3:1',\n",
+       " '0:1',\n",
+       " '2:1',\n",
+       " '1:1',\n",
+       " '1:2',\n",
+       " '1:3',\n",
+       " '1:1',\n",
+       " '0:1',\n",
+       " '0:0',\n",
+       " '0:0',\n",
+       " '1:0',\n",
+       " '2:0',\n",
+       " '0:1',\n",
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+       " '1:3',\n",
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+       " '4:5',\n",
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+       " '3:0',\n",
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+       " '1:1',\n",
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+       " '0:1',\n",
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+       " '1:2',\n",
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+       " '3:1',\n",
+       " '1:0',\n",
+       " '0:0',\n",
+       " '2:3',\n",
+       " '0:3',\n",
+       " '1:4',\n",
+       " '3:2',\n",
+       " '2:2',\n",
+       " '1:2',\n",
+       " '0:4',\n",
+       " '3:1',\n",
+       " '2:1',\n",
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+       " '0:0',\n",
+       " '1:0',\n",
+       " '0:3',\n",
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+       " '1:3',\n",
+       " '4:0',\n",
+       " '1:2',\n",
+       " '2:1',\n",
+       " '2:1',\n",
+       " '2:1',\n",
+       " '3:1',\n",
+       " '1:4',\n",
+       " '1:0',\n",
+       " '2:2',\n",
+       " '2:2',\n",
+       " '1:2',\n",
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+       " '1:1',\n",
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+       " '3:2',\n",
+       " '1:1',\n",
+       " '3:0',\n",
+       " '1:0',\n",
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+       " '1:2',\n",
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+       " '1:2',\n",
+       " '4:2',\n",
+       " '2:3',\n",
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+       " '0:1',\n",
+       " '1:0',\n",
+       " '1:1',\n",
+       " '1:0',\n",
+       " '4:0',\n",
+       " '2:0',\n",
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+       " '2:2',\n",
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+       " '1:2',\n",
+       " '1:2',\n",
+       " '1:1',\n",
+       " '1:1',\n",
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+       " '3:2',\n",
+       " '0:0',\n",
+       " '2:2',\n",
+       " '1:0',\n",
+       " '1:3',\n",
+       " '1:0',\n",
+       " '0:0',\n",
+       " '2:0',\n",
+       " '3:0',\n",
+       " '5:1',\n",
+       " '3:1',\n",
+       " '2:0',\n",
+       " '3:4',\n",
+       " '1:0',\n",
+       " '2:1',\n",
+       " '0:1',\n",
+       " '2:1',\n",
+       " '2:2',\n",
+       " '0:0',\n",
+       " '1:2',\n",
+       " '1:2',\n",
+       " '1:1',\n",
+       " '1:3',\n",
+       " '3:1',\n",
+       " '1:2',\n",
+       " '4:2',\n",
+       " '3:1',\n",
+       " '4:1',\n",
+       " '4:1',\n",
+       " '3:0',\n",
+       " '1:1',\n",
+       " '1:0',\n",
+       " '2:2',\n",
+       " '1:1',\n",
+       " '3:1',\n",
+       " '6:2',\n",
+       " '2:4',\n",
+       " '2:2',\n",
+       " '1:0',\n",
+       " '0:1',\n",
+       " '2:0',\n",
+       " '1:1',\n",
+       " '0:3',\n",
+       " '2:2',\n",
+       " '1:0',\n",
+       " '1:0',\n",
+       " '1:2',\n",
+       " '6:1',\n",
+       " '0:0',\n",
+       " '3:1',\n",
+       " '2:0',\n",
+       " '2:2',\n",
+       " '1:0',\n",
+       " '5:1',\n",
+       " '3:0',\n",
+       " '2:0',\n",
+       " '2:1',\n",
+       " '1:0',\n",
+       " '5:1',\n",
+       " '0:1',\n",
+       " '3:0',\n",
+       " '3:2',\n",
+       " '0:0',\n",
+       " '1:0',\n",
+       " '1:0',\n",
+       " '3:0',\n",
+       " '2:1',\n",
+       " '1:2',\n",
+       " '3:0',\n",
+       " '2:2',\n",
+       " '1:1',\n",
+       " '3:1',\n",
+       " '1:0',\n",
+       " '3:0',\n",
+       " '1:0',\n",
+       " '2:0',\n",
+       " '0:1',\n",
+       " '1:0',\n",
+       " '3:0',\n",
+       " '1:3',\n",
+       " '6:2',\n",
+       " '2:0',\n",
+       " '3:2',\n",
+       " '4:0',\n",
+       " '3:1',\n",
+       " '4:0',\n",
+       " '1:0',\n",
+       " '1:1',\n",
+       " '2:0',\n",
+       " '3:2',\n",
+       " '2:1',\n",
+       " '2:0',\n",
+       " '4:0',\n",
+       " '1:0',\n",
+       " '3:2',\n",
+       " '0:0',\n",
+       " '2:1',\n",
+       " '0:1',\n",
+       " '3:1',\n",
+       " '1:1',\n",
+       " '2:0']"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resultados\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 - 1\n",
+      "0 - 1\n",
+      "1 - 3\n",
+      "1 - 1\n",
+      "2 - 2\n",
+      "1 - 2\n",
+      "1 - 0\n",
+      "2 - 2\n",
+      "1 - 1\n",
+      "2 - 3\n",
+      "0 - 3\n",
+      "0 - 1\n",
+      "2 - 3\n",
+      "0 - 1\n",
+      "1 - 1\n",
+      "0 - 2\n",
+      "0 - 3\n",
+      "1 - 2\n",
+      "1 - 1\n",
+      "2 - 5\n",
+      "2 - 2\n",
+      "2 - 2\n",
+      "3 - 2\n",
+      "4 - 0\n",
+      "3 - 3\n",
+      "3 - 3\n",
+      "0 - 0\n",
+      "2 - 0\n",
+      "0 - 2\n",
+      "0 - 3\n",
+      "0 - 3\n",
+      "2 - 1\n",
+      "1 - 2\n",
+      "0 - 2\n",
+      "0 - 0\n",
+      "0 - 1\n",
+      "1 - 1\n",
+      "0 - 2\n",
+      "1 - 1\n",
+      "1 - 1\n",
+      "1 - 2\n",
+      "1 - 0\n",
+      "0 - 2\n",
+      "1 - 0\n",
+      "1 - 1\n",
+      "0 - 4\n",
+      "2 - 2\n",
+      "1 - 1\n",
+      "2 - 2\n",
+      "1 - 2\n",
+      "1 - 1\n",
+      "1 - 3\n",
+      "1 - 5\n",
+      "0 - 1\n",
+      "5 - 1\n",
+      "0 - 3\n",
+      "0 - 2\n",
+      "0 - 0\n",
+      "2 - 1\n",
+      "4 - 1\n",
+      "0 - 0\n",
+      "4 - 2\n",
+      "2 - 2\n",
+      "3 - 0\n",
+      "2 - 0\n",
+      "0 - 3\n",
+      "0 - 2\n",
+      "1 - 1\n",
+      "1 - 2\n",
+      "0 - 3\n",
+      "0 - 1\n",
+      "0 - 4\n",
+      "0 - 1\n",
+      "1 - 1\n",
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+      "1 - 1\n",
+      "3 - 0\n",
+      "3 - 0\n",
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+      "2 - 3\n",
+      "3 - 2\n",
+      "0 - 1\n",
+      "2 - 0\n",
+      "1 - 1\n",
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+      "2 - 1\n",
+      "1 - 2\n",
+      "1 - 0\n",
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+      "2 - 1\n",
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+      "3 - 1\n",
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+      "1 - 1\n",
+      "1 - 2\n",
+      "1 - 3\n",
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+      "2 - 3\n",
+      "0 - 3\n",
+      "1 - 4\n",
+      "3 - 2\n",
+      "2 - 2\n",
+      "1 - 2\n",
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+      "3 - 1\n",
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+      "1 - 1\n",
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+      "3 - 3\n",
+      "0 - 0\n",
+      "1 - 1\n",
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+      "0 - 0\n",
+      "3 - 3\n",
+      "3 - 0\n",
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+      "1 - 1\n",
+      "1 - 3\n",
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+      "3 - 1\n",
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+      "2 - 2\n",
+      "1 - 2\n",
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+      "3 - 2\n",
+      "1 - 1\n",
+      "3 - 0\n",
+      "1 - 0\n",
+      "1 - 2\n",
+      "2 - 1\n",
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+      "1 - 0\n",
+      "2 - 0\n",
+      "1 - 0\n",
+      "2 - 0\n",
+      "3 - 1\n",
+      "2 - 0\n",
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+      "2 - 2\n",
+      "1 - 0\n",
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+      "1 - 2\n",
+      "1 - 1\n",
+      "0 - 1\n",
+      "1 - 2\n",
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+      "2 - 3\n",
+      "2 - 2\n",
+      "2 - 0\n",
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+      "2 - 1\n",
+      "2 - 0\n",
+      "3 - 0\n",
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+      "2 - 2\n",
+      "1 - 2\n",
+      "1 - 2\n",
+      "1 - 2\n",
+      "1 - 1\n",
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+      "2 - 0\n",
+      "3 - 0\n",
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+      "2 - 1\n",
+      "2 - 2\n",
+      "0 - 0\n",
+      "1 - 2\n",
+      "1 - 2\n",
+      "1 - 1\n",
+      "1 - 3\n",
+      "3 - 1\n",
+      "1 - 2\n",
+      "4 - 2\n",
+      "3 - 1\n",
+      "4 - 1\n",
+      "4 - 1\n",
+      "3 - 0\n",
+      "1 - 1\n",
+      "1 - 0\n",
+      "2 - 2\n",
+      "1 - 1\n",
+      "3 - 1\n",
+      "6 - 2\n",
+      "2 - 4\n",
+      "2 - 2\n",
+      "1 - 0\n",
+      "0 - 1\n",
+      "2 - 0\n",
+      "1 - 1\n",
+      "0 - 3\n",
+      "2 - 2\n",
+      "1 - 0\n",
+      "1 - 0\n",
+      "1 - 2\n",
+      "6 - 1\n",
+      "0 - 0\n",
+      "3 - 1\n",
+      "2 - 0\n",
+      "2 - 2\n",
+      "1 - 0\n",
+      "5 - 1\n",
+      "3 - 0\n",
+      "2 - 0\n",
+      "2 - 1\n",
+      "1 - 0\n",
+      "5 - 1\n",
+      "0 - 1\n",
+      "3 - 0\n",
+      "3 - 2\n",
+      "0 - 0\n",
+      "1 - 0\n",
+      "1 - 0\n",
+      "3 - 0\n",
+      "2 - 1\n",
+      "1 - 2\n",
+      "3 - 0\n",
+      "2 - 2\n",
+      "1 - 1\n",
+      "3 - 1\n",
+      "1 - 0\n",
+      "3 - 0\n",
+      "1 - 0\n",
+      "2 - 0\n",
+      "0 - 1\n",
+      "1 - 0\n",
+      "3 - 0\n",
+      "1 - 3\n",
+      "6 - 2\n",
+      "2 - 0\n",
+      "3 - 2\n",
+      "4 - 0\n",
+      "3 - 1\n",
+      "4 - 0\n",
+      "1 - 0\n",
+      "1 - 1\n",
+      "2 - 0\n",
+      "3 - 2\n",
+      "2 - 1\n",
+      "2 - 0\n",
+      "4 - 0\n",
+      "1 - 0\n",
+      "3 - 2\n",
+      "0 - 0\n",
+      "2 - 1\n",
+      "0 - 1\n",
+      "3 - 1\n",
+      "1 - 1\n",
+      "2 - 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "home = []\n",
+    "away = []\n",
+    "for results in resultados:\n",
+    "    h  = results.split(':')[0]\n",
+    "    a  = results.split(':')[1]\n",
+    "    home.append(h)\n",
+    "    away.append(a)\n",
+    "    print(h, \"-\" ,a) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['2', '0', '1', '1', '2', '1', '1', '2', '1', '2', '0', '0', '2', '0', '1', '0', '0', '1', '1', '2', '2', '2', '3', '4', '3', '3', '0', '2', '0', '0', '0', '2', '1', '0', '0', '0', '1', '0', '1', '1', '1', '1', '0', '1', '1', '0', '2', '1', '2', '1', '1', '1', '1', '0', '5', '0', '0', '0', '2', '4', '0', '4', '2', '3', '2', '0', '0', '1', '1', '0', '0', '0', '0', '1', '0', '0', '1', '3', '3', '0', '2', '3', '0', '2', '1', '0', '2', '1', '1', '0', '2', '2', '3', '0', '0', '1', '0', '1', '3', '0', '2', '1', '1', '1', '1', '0', '0', '0', '1', '2', '0', '2', '1', '2', '2', '0', '4', '1', '0', '1', '0', '2', '2', '2', '0', '2', '0', '1', '1', '2', '2', '2', '1', '2', '0', '0', '1', '3', '3', '2', '0', '1', '1', '3', '3', '1', '1', '1', '0', '2', '4', '0', '3', '2', '2', '4', '3', '0', '0', '4', '1', '3', '0', '1', '2', '2', '1', '2', '0', '1', '0', '2', '0', '0', '3', '0', '1', '2', '1', '1', '3', '1', '0', '2', '0', '1', '3', '2', '1', '0', '3', '2', '0', '0', '1', '0', '1', '4', '0', '3', '0', '1', '2', '0', '3', '3', '0', '1', '1', '4', '1', '2', '2', '2', '3', '1', '1', '2', '2', '1', '1', '1', '0', '3', '1', '3', '1', '1', '2', '4', '1', '2', '1', '2', '3', '2', '0', '2', '2', '1', '0', '1', '1', '0', '1', '4', '2', '2', '2', '1', '2', '2', '3', '1', '2', '0', '2', '0', '1', '0', '2', '1', '0', '1', '0', '0', '1', '1', '1', '4', '2', '4', '2', '1', '1', '1', '1', '1', '0', '3', '0', '2', '1', '1', '1', '0', '2', '3', '5', '3', '2', '3', '1', '2', '0', '2', '2', '0', '1', '1', '1', '1', '3', '1', '4', '3', '4', '4', '3', '1', '1', '2', '1', '3', '6', '2', '2', '1', '0', '2', '1', '0', '2', '1', '1', '1', '6', '0', '3', '2', '2', '1', '5', '3', '2', '2', '1', '5', '0', '3', '3', '0', '1', '1', '3', '2', '1', '3', '2', '1', '3', '1', '3', '1', '2', '0', '1', '3', '1', '6', '2', '3', '4', '3', '4', '1', '1', '2', '3', '2', '2', '4', '1', '3', '0', '2', '0', '3', '1', '2'] -- ['1', '1', '3', '1', '2', '2', '0', '2', '1', '3', '3', '1', '3', '1', '1', '2', '3', '2', '1', '5', '2', '2', '2', '0', '3', '3', '0', '0', '2', '3', '3', '1', '2', '2', '0', '1', '1', '2', '1', '1', '2', '0', '2', '0', '1', '4', '2', '1', '2', '2', '1', '3', '5', '1', '1', '3', '2', '0', '1', '1', '0', '2', '2', '0', '0', '3', '2', '1', '2', '3', '1', '4', '1', '1', '0', '0', '1', '0', '0', '1', '3', '2', '1', '0', '1', '3', '1', '2', '0', '0', '1', '1', '3', '1', '1', '0', '0', '2', '1', '1', '1', '1', '2', '3', '1', '1', '0', '0', '0', '0', '1', '2', '0', '4', '2', '2', '1', '2', '0', '0', '3', '1', '2', '3', '0', '0', '3', '3', '3', '2', '1', '2', '1', '0', '0', '0', '4', '1', '2', '0', '1', '3', '1', '1', '0', '1', '2', '2', '1', '0', '5', '6', '0', '0', '2', '0', '0', '1', '0', '1', '1', '4', '1', '2', '1', '1', '3', '0', '0', '1', '1', '1', '1', '0', '0', '0', '2', '1', '1', '0', '1', '0', '0', '3', '3', '4', '2', '2', '2', '4', '1', '1', '0', '0', '0', '3', '1', '0', '3', '3', '0', '1', '3', '0', '3', '0', '1', '1', '3', '0', '2', '1', '1', '1', '1', '4', '0', '2', '2', '2', '0', '1', '0', '2', '1', '0', '0', '2', '1', '0', '0', '0', '0', '0', '1', '0', '2', '2', '2', '0', '1', '2', '1', '1', '2', '2', '3', '2', '0', '1', '1', '0', '0', '1', '2', '1', '2', '1', '0', '0', '0', '1', '0', '0', '1', '1', '0', '1', '0', '0', '0', '1', '2', '2', '2', '2', '1', '1', '1', '2', '0', '2', '0', '3', '0', '0', '0', '0', '1', '1', '0', '4', '0', '1', '1', '1', '2', '0', '2', '2', '1', '3', '1', '2', '2', '1', '1', '1', '0', '1', '0', '2', '1', '1', '2', '4', '2', '0', '1', '0', '1', '3', '2', '0', '0', '2', '1', '0', '1', '0', '2', '0', '1', '0', '0', '1', '0', '1', '1', '0', '2', '0', '0', '0', '0', '1', '2', '0', '2', '1', '1', '0', '0', '0', '0', '1', '0', '0', '3', '2', '0', '2', '0', '1', '0', '0', '1', '0', '2', '1', '0', '0', '0', '2', '0', '1', '1', '1', '1', '0']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(home,(\"--\"), away)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 432x288 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#HISTOGRAMA DE FRECUENCIA DE GOLES DE LOS LOCALES Y VISITANTES EN EL 2015-2016 DE LA LIGA INGLESA\n",
+    "plt.subplot(121)\n",
+    "plt.title('Frecuencia de goles de local Premier League 2015-2016', fontsize = 12, fontweight = 'bold') \n",
+    "#titulo de los ejes\n",
+    "plt.xlabel('Goles Home', fontweight = 'bold') \n",
+    "plt.ylabel('frecuencia',fontweight = 'bold')\n",
+    "plt.hist(home, bins=10)\n",
+    "plt.show()\n",
+    "plt.clf()\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.subplot(122)\n",
+    "plt.title('Frecuencia de goles de Visita Premier League 2015-2016', fontsize = 12, fontweight = 'bold') \n",
+    "#titulo de los ejes\n",
+    "plt.xlabel('Goles Away', fontweight = 'bold') \n",
+    "plt.ylabel('frecuencia',fontweight = 'bold')\n",
+    "plt.hist(away, bins=10)\n",
+    "plt.show()\n",
+    "plt.clf()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2, 0, 1, 1, 2, 1, 1, 2, 1, 2, 0, 0, 2, 0, 1, 0, 0, 1, 1, 2, 2, 2, 3, 4, 3, 3, 0, 2, 0, 0, 0, 2, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 2, 1, 2, 1, 1, 1, 1, 0, 5, 0, 0, 0, 2, 4, 0, 4, 2, 3, 2, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 3, 3, 0, 2, 3, 0, 2, 1, 0, 2, 1, 1, 0, 2, 2, 3, 0, 0, 1, 0, 1, 3, 0, 2, 1, 1, 1, 1, 0, 0, 0, 1, 2, 0, 2, 1, 2, 2, 0, 4, 1, 0, 1, 0, 2, 2, 2, 0, 2, 0, 1, 1, 2, 2, 2, 1, 2, 0, 0, 1, 3, 3, 2, 0, 1, 1, 3, 3, 1, 1, 1, 0, 2, 4, 0, 3, 2, 2, 4, 3, 0, 0, 4, 1, 3, 0, 1, 2, 2, 1, 2, 0, 1, 0, 2, 0, 0, 3, 0, 1, 2, 1, 1, 3, 1, 0, 2, 0, 1, 3, 2, 1, 0, 3, 2, 0, 0, 1, 0, 1, 4, 0, 3, 0, 1, 2, 0, 3, 3, 0, 1, 1, 4, 1, 2, 2, 2, 3, 1, 1, 2, 2, 1, 1, 1, 0, 3, 1, 3, 1, 1, 2, 4, 1, 2, 1, 2, 3, 2, 0, 2, 2, 1, 0, 1, 1, 0, 1, 4, 2, 2, 2, 1, 2, 2, 3, 1, 2, 0, 2, 0, 1, 0, 2, 1, 0, 1, 0, 0, 1, 1, 1, 4, 2, 4, 2, 1, 1, 1, 1, 1, 0, 3, 0, 2, 1, 1, 1, 0, 2, 3, 5, 3, 2, 3, 1, 2, 0, 2, 2, 0, 1, 1, 1, 1, 3, 1, 4, 3, 4, 4, 3, 1, 1, 2, 1, 3, 6, 2, 2, 1, 0, 2, 1, 0, 2, 1, 1, 1, 6, 0, 3, 2, 2, 1, 5, 3, 2, 2, 1, 5, 0, 3, 3, 0, 1, 1, 3, 2, 1, 3, 2, 1, 3, 1, 3, 1, 2, 0, 1, 3, 1, 6, 2, 3, 4, 3, 4, 1, 1, 2, 3, 2, 2, 4, 1, 3, 0, 2, 0, 3, 1, 2] ////////*******//////// [1, 1, 3, 1, 2, 2, 0, 2, 1, 3, 3, 1, 3, 1, 1, 2, 3, 2, 1, 5, 2, 2, 2, 0, 3, 3, 0, 0, 2, 3, 3, 1, 2, 2, 0, 1, 1, 2, 1, 1, 2, 0, 2, 0, 1, 4, 2, 1, 2, 2, 1, 3, 5, 1, 1, 3, 2, 0, 1, 1, 0, 2, 2, 0, 0, 3, 2, 1, 2, 3, 1, 4, 1, 1, 0, 0, 1, 0, 0, 1, 3, 2, 1, 0, 1, 3, 1, 2, 0, 0, 1, 1, 3, 1, 1, 0, 0, 2, 1, 1, 1, 1, 2, 3, 1, 1, 0, 0, 0, 0, 1, 2, 0, 4, 2, 2, 1, 2, 0, 0, 3, 1, 2, 3, 0, 0, 3, 3, 3, 2, 1, 2, 1, 0, 0, 0, 4, 1, 2, 0, 1, 3, 1, 1, 0, 1, 2, 2, 1, 0, 5, 6, 0, 0, 2, 0, 0, 1, 0, 1, 1, 4, 1, 2, 1, 1, 3, 0, 0, 1, 1, 1, 1, 0, 0, 0, 2, 1, 1, 0, 1, 0, 0, 3, 3, 4, 2, 2, 2, 4, 1, 1, 0, 0, 0, 3, 1, 0, 3, 3, 0, 1, 3, 0, 3, 0, 1, 1, 3, 0, 2, 1, 1, 1, 1, 4, 0, 2, 2, 2, 0, 1, 0, 2, 1, 0, 0, 2, 1, 0, 0, 0, 0, 0, 1, 0, 2, 2, 2, 0, 1, 2, 1, 1, 2, 2, 3, 2, 0, 1, 1, 0, 0, 1, 2, 1, 2, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 2, 2, 2, 2, 1, 1, 1, 2, 0, 2, 0, 3, 0, 0, 0, 0, 1, 1, 0, 4, 0, 1, 1, 1, 2, 0, 2, 2, 1, 3, 1, 2, 2, 1, 1, 1, 0, 1, 0, 2, 1, 1, 2, 4, 2, 0, 1, 0, 1, 3, 2, 0, 0, 2, 1, 0, 1, 0, 2, 0, 1, 0, 0, 1, 0, 1, 1, 0, 2, 0, 0, 0, 0, 1, 2, 0, 2, 1, 1, 0, 0, 0, 0, 1, 0, 0, 3, 2, 0, 2, 0, 1, 0, 0, 1, 0, 2, 1, 0, 0, 0, 2, 0, 1, 1, 1, 1, 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#paso de string a enteros la lista\n",
+    "\n",
+    "enthome = [int(x) for x in home]\n",
+    "entaway = [int(y) for y in away]\n",
+    "\n",
+    "\n",
+    "print(enthome, '////////*******////////' , entaway)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/data-used/2015-2016PL b/data-used/2015-2016PL
new file mode 100644
index 0000000000000000000000000000000000000000..6b5c6daa8a9ee59db2adb1d177bd1cb5dacab4e7
--- /dev/null
+++ b/data-used/2015-2016PL
@@ -0,0 +1,21 @@
+1.	Leicester	―	2:5	1:1	0:0	1:1	1:0	2:2	2:0	3:0	2:1	3:1	4:0	2:1	2:2	1:0	0:0	4:2	1:0	1:0	3:2	+32	81
+2.	Arsenal	2:1	―	1:1	2:1	3:0	0:0	0:2	0:0	2:0	0:1	2:1	1:2	4:0	2:0	1:1	2:0	3:1	1:0	1:0	4:0	+29	71
+3.	Tottenham	0:1	2:2	―	4:1	3:0	1:2	4:1	0:0	2:2	0:0	0:0	2:1	1:0	1:1	1:0	3:0	4:1	1:2	3:0	3:1	+34	70
+4.	Man. City	1:3	2:2	1:2	―	0:1	3:1	1:2	1:4	4:0	3:0	0:0	2:1	2:0	2:1	4:0	5:1	4:1	6:1	2:1	4:0	+30	66
+5.	Man. United	1:1	3:2	1:0	0:0	―	0:1	0:0	3:1	3:0	0:0	1:0	2:1	1:0	2:0	2:0	3:1	3:0	0:0	1:2	1:0	+14	66
+6.	Southampton	2:2	4:0	0:2	4:2	2:3	―	1:0	3:2	0:1	1:2	0:3	3:1	2:0	3:0	4:1	2:0	1:1	3:1	3:0	1:1	+18	63
+7.	West Ham	1:2	3:3	1:0	2:2	3:2	2:1	―	2:0	0:0	2:1	1:1	1:4	3:1	1:1	2:2	3:4	1:0	2:0	2:2	2:0	+14	62
+8.	Liverpool	1:0	3:3	1:1	3:0	0:1	1:1	0:3	―	4:1	1:1	4:0	1:0	2:0	2:2	1:2	1:0	2:2	2:2	1:1	3:2	+13	60
+9.	Stoke	2:2	0:0	0:4	2:0	2:0	1:2	2:1	0:1	―	1:0	0:3	2:2	0:2	0:1	1:2	2:1	1:1	1:0	3:1	2:1	-14	51
+10.	Chelsea	1:1	2:0	2:2	0:3	1:1	1:3	2:2	1:3	1:1	―	3:3	2:2	2:2	2:2	1:2	0:1	3:1	5:1	1:0	2:0	+6	50
+11.	Everton	2:3	0:2	1:1	0:2	0:3	1:1	2:3	1:1	3:4	3:1	―	1:2	2:2	0:1	1:1	2:1	6:2	3:0	3:0	4:0	+4	47
+12.	Swansea	0:3	0:3	2:2	1:1	2:1	0:1	0:0	3:1	0:1	1:0	0:0	―	1:0	1:0	1:1	2:2	2:4	2:0	1:0	1:0	-10	47
+13.	Watford	0:1	0:3	1:2	1:2	1:2	0:0	2:0	3:0	1:2	0:0	1:1	1:0	―	0:0	0:1	0:0	2:2	2:1	2:0	3:2	-10	45
+14.	Bromwich	2:3	2:1	1:1	0:3	1:0	0:0	0:3	1:1	2:1	2:3	2:3	1:1	0:1	―	3:2	1:2	1:0	1:0	0:1	0:0	-14	43
+15.	Palace	0:1	1:2	1:3	0:1	0:0	1:0	1:3	1:2	2:1	0:3	0:0	0:0	1:2	2:0	―	1:2	0:1	5:1	1:0	2:1	-12	42
+16.	Bournemouth	1:1	0:2	1:5	0:4	2:1	2:0	1:3	1:2	1:3	1:4	3:3	3:2	1:1	1:1	0:0	―	2:0	0:1	3:0	0:1	-22	42
+17.	Sunderland	0:2	0:0	0:1	0:1	2:1	0:1	2:2	0:1	2:0	3:2	3:0	1:1	0:1	0:0	2:2	1:1	―	3:0	1:3	3:1	-14	39
+18.	Newcastle	0:3	0:1	5:1	1:1	3:3	2:2	2:1	2:0	0:0	2:2	0:1	3:0	1:2	1:0	1:0	1:3	1:1	―	6:2	1:1	-21	37
+19.	Norwich	1:2	1:1	0:3	0:0	0:1	1:0	2:2	4:5	1:1	1:2	1:1	1:0	4:2	0:1	1:3	3:1	0:3	3:2	―	2:0	-28	34
+20.	Villa	1:1	0:2	0:2	0:0	0:1	2:4	1:1	0:6	0:1	0:4	1:3	1:2	2:3	0:1	1:0	1:2	2:2	0:0	2:0	―	-49	17
+
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