Buckler_Lab2_graded_95-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab2 Python for Data Science 1\n",
    "----------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HOMEWORK ASSIGNMENT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer the following questions in the jupyter notebook file. (You can also use other IDEs to test your code) **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Finish the tutorial (40 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the tutorial \"10 Minutes to pandas\" at http://pandas.pydata.org/pandas-docs/stable/10min.html.  \n",
    "Practice the examples in your python IDE.\n",
    "\n",
    "Write 5 new things you learnt from the tutorial that has not been covered in the lecture slides.\n",
    "\n",
    "e.g. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#We can sort a dataframe by a certain column values\n",
    "#df.sort(columns='B')\n",
    "#                   A         B         C         D\n",
    "#2013-01-03 -0.861849 -2.104569 -0.494929  1.071804\n",
    "#2013-01-04  0.721555 -0.706771 -1.039575  0.271860\n",
    "#2013-01-01  0.469112 -0.282863 -1.509059 -1.135632\n",
    "#2013-01-02  1.212112 -0.173215  0.119209 -1.044236\n",
    "#2013-01-06 -0.673690  0.113648 -1.478427  0.524988\n",
    "#2013-01-05 -0.424972  0.567020  0.276232 -1.087401\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#1 Using the isin() method for filtering:\n",
    "\n",
    "In [41]: df2 = df.copy()\n",
    "\n",
    "In [42]: df2['E']=['one', 'one','two','three','four','three']\n",
    "\n",
    "In [43]: df2\n",
    "Out[43]: \n",
    "                   A         B         C         D      E\n",
    "2013-01-01  0.469112 -0.282863 -1.509059 -1.135632    one\n",
    "2013-01-02  1.212112 -0.173215  0.119209 -1.044236    one\n",
    "2013-01-03 -0.861849 -2.104569 -0.494929  1.071804    two\n",
    "2013-01-04  0.721555 -0.706771 -1.039575  0.271860  three\n",
    "2013-01-05 -0.424972  0.567020  0.276232 -1.087401   four\n",
    "2013-01-06 -0.673690  0.113648 -1.478427  0.524988  three\n",
    "\n",
    "In [44]: df2[df2['E'].isin(['two','four'])]\n",
    "Out[44]: \n",
    "                   A         B         C         D     E\n",
    "2013-01-03 -0.861849 -2.104569 -0.494929  1.071804   two\n",
    "2013-01-05 -0.424972  0.567020  0.276232 -1.087401  four"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#2\n",
    "\n",
    "Histogramming\n",
    "See more at Histogramming and Discretization\n",
    "\n",
    "In [68]: s = pd.Series(np.random.randint(0,7,size=10))\n",
    "\n",
    "In [69]: s\n",
    "Out[69]: \n",
    "0    4\n",
    "1    2\n",
    "2    1\n",
    "3    2\n",
    "4    6\n",
    "5    4\n",
    "6    4\n",
    "7    6\n",
    "8    4\n",
    "9    4\n",
    "dtype: int32\n",
    "\n",
    "In [70]: s.value_counts()\n",
    "Out[70]: \n",
    "4    5\n",
    "6    2\n",
    "2    2\n",
    "1    1\n",
    "dtype: int64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#3\n",
    "\n",
    "Stack\n",
    "In [90]: tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',\n",
    "   ....:                      'foo', 'foo', 'qux', 'qux'],\n",
    "   ....:                     ['one', 'two', 'one', 'two',\n",
    "   ....:                      'one', 'two', 'one', 'two']]))\n",
    "   ....: \n",
    "\n",
    "In [91]: index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])\n",
    "\n",
    "In [92]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])\n",
    "\n",
    "In [93]: df2 = df[:4]\n",
    "\n",
    "In [94]: df2\n",
    "Out[94]: \n",
    "                     A         B\n",
    "first second                    \n",
    "bar   one     0.029399 -0.542108\n",
    "      two     0.282696 -0.087302\n",
    "baz   one    -1.575170  1.771208\n",
    "      two     0.816482  1.100230"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#4\n",
    "\n",
    "Time Series\n",
    "pandas has simple, powerful, and efficient functionality for performing resampling operations during frequency conversion (e.g., converting secondly data into 5-minutely data). This is extremely common in, but not limited to, financial applications. See the Time Series section\n",
    "\n",
    "In [103]: rng = pd.date_range('1/1/2012', periods=100, freq='S')\n",
    "\n",
    "In [104]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)\n",
    "\n",
    "In [105]: ts.resample('5Min', how='sum')\n",
    "Out[105]: \n",
    "2012-01-01    25083\n",
    "Freq: 5T, dtype: int32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#5\n",
    "\n",
    "Categoricals\n",
    "Since version 0.15, pandas can include categorical data in a DataFrame. For full docs, see the categorical introduction and the API documentation.\n",
    "\n",
    "In [122]: df = pd.DataFrame({\"id\":[1,2,3,4,5,6], \"raw_grade\":['a', 'b', 'b', 'a', 'a', 'e']})\n",
    "Convert the raw grades to a categorical data type.\n",
    "\n",
    "In [123]: df[\"grade\"] = df[\"raw_grade\"].astype(\"category\")\n",
    "\n",
    "In [124]: df[\"grade\"]\n",
    "Out[124]: \n",
    "0    a\n",
    "1    b\n",
    "2    b\n",
    "3    a\n",
    "4    a\n",
    "5    e\n",
    "Name: grade, dtype: category\n",
    "Categories (3, object): [a < b < e]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Getting and Knowing your Data (25 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull data directly from the internet.\n",
    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "### Step 1. Import the necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user). \n",
    "\n",
    "### Step 3. Assign it to a variable called users and use the 'user_id' as index\n",
    "\n",
    "users = pd.read_table('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user', \n",
    "                      sep='|', index_col='user_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
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       "      <td>06405</td>\n",
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       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>29206</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>45</td>\n",
       "      <td>M</td>\n",
       "      <td>scientist</td>\n",
       "      <td>55106</td>\n",
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       "      <td>entertainment</td>\n",
       "      <td>10309</td>\n",
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       "      <td>06355</td>\n",
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       "      <td>37212</td>\n",
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       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>librarian</td>\n",
       "      <td>02138</td>\n",
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       "      <th>20</th>\n",
       "      <td>42</td>\n",
       "      <td>F</td>\n",
       "      <td>homemaker</td>\n",
       "      <td>95660</td>\n",
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       "      <th>21</th>\n",
       "      <td>26</td>\n",
       "      <td>M</td>\n",
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       "      <td>30068</td>\n",
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       "      <th>22</th>\n",
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       "      <td>40206</td>\n",
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       "      <td>39</td>\n",
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       "      <td>55107</td>\n",
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       "         age gender     occupation zip_code\n",
       "user_id                                    \n",
       "1         24      M     technician    85711\n",
       "2         53      F          other    94043\n",
       "3         23      M         writer    32067\n",
       "4         24      M     technician    43537\n",
       "5         33      F          other    15213\n",
       "6         42      M      executive    98101\n",
       "7         57      M  administrator    91344\n",
       "8         36      M  administrator    05201\n",
       "9         29      M        student    01002\n",
       "10        53      M         lawyer    90703\n",
       "11        39      F          other    30329\n",
       "12        28      F          other    06405\n",
       "13        47      M       educator    29206\n",
       "14        45      M      scientist    55106\n",
       "15        49      F       educator    97301\n",
       "16        21      M  entertainment    10309\n",
       "17        30      M     programmer    06355\n",
       "18        35      F          other    37212\n",
       "19        40      M      librarian    02138\n",
       "20        42      F      homemaker    95660\n",
       "21        26      M         writer    30068\n",
       "22        25      M         writer    40206\n",
       "23        30      F         artist    48197\n",
       "24        21      F         artist    94533\n",
       "25        39      M       engineer    55107"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 4. See the first 25 entries\n",
    "users = pd.read_table('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user', \n",
    "                      sep='|', index_col='user_id')\n",
    "users.head(25)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
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       "      <td>07310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>60</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>33556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>58</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>06437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>28</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>48105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>61</td>\n",
       "      <td>M</td>\n",
       "      <td>engineer</td>\n",
       "      <td>22902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>42</td>\n",
       "      <td>M</td>\n",
       "      <td>doctor</td>\n",
       "      <td>66221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>other</td>\n",
       "      <td>32789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>48</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>98072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>38</td>\n",
       "      <td>F</td>\n",
       "      <td>technician</td>\n",
       "      <td>55038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>26</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>33319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>administrator</td>\n",
       "      <td>02215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>20</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>97229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>librarian</td>\n",
       "      <td>78209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>22</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>77841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age gender     occupation zip_code\n",
       "user_id                                    \n",
       "919       25      M          other    14216\n",
       "920       30      F         artist    90008\n",
       "921       20      F        student    98801\n",
       "922       29      F  administrator    21114\n",
       "923       21      M        student    E2E3R\n",
       "924       29      M          other    11753\n",
       "925       18      F       salesman    49036\n",
       "926       49      M  entertainment    01701\n",
       "927       23      M     programmer    55428\n",
       "928       21      M        student    55408\n",
       "929       44      M      scientist    53711\n",
       "930       28      F      scientist    07310\n",
       "931       60      M       educator    33556\n",
       "932       58      M       educator    06437\n",
       "933       28      M        student    48105\n",
       "934       61      M       engineer    22902\n",
       "935       42      M         doctor    66221\n",
       "936       24      M          other    32789\n",
       "937       48      M       educator    98072\n",
       "938       38      F     technician    55038\n",
       "939       26      F        student    33319\n",
       "940       32      M  administrator    02215\n",
       "941       20      M        student    97229\n",
       "942       48      F      librarian    78209\n",
       "943       22      M        student    77841"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 5. See the last 10 entries\n",
    "users = pd.read_table('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user', \n",
    "                      sep='|', index_col='user_id')\n",
    "users.tail(25)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(943, 4)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 6. What is the number of observations in the dataset? (Hint: shape())\n",
    "users = pd.read_table('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user', \n",
    "                      sep='|', index_col='user_id')\n",
    "users.shape\n",
    "\n",
    "# There are 943 observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Step 7. What is the number of columns in the dataset? (Hint: shape())\n",
    "\n",
    "# There are 4 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'age', u'gender', u'occupation', u'zip_code'], dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 8. Print the name of all the columns.\n",
    "\n",
    "users.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
       "            ...\n",
       "            934, 935, 936, 937, 938, 939, 940, 941, 942, 943],\n",
       "           dtype='int64', name=u'user_id', length=943)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 9.What are the dataset index?\n",
    "users.index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age            int64\n",
       "gender        object\n",
       "occupation    object\n",
       "zip_code      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 10. What is the data type of each column?\n",
    "users.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "1         technician\n",
       "2              other\n",
       "3             writer\n",
       "4         technician\n",
       "5              other\n",
       "6          executive\n",
       "7      administrator\n",
       "8      administrator\n",
       "9            student\n",
       "10            lawyer\n",
       "11             other\n",
       "12             other\n",
       "13          educator\n",
       "14         scientist\n",
       "15          educator\n",
       "16     entertainment\n",
       "17        programmer\n",
       "18             other\n",
       "19         librarian\n",
       "20         homemaker\n",
       "21            writer\n",
       "22            writer\n",
       "23            artist\n",
       "24            artist\n",
       "25          engineer\n",
       "26          engineer\n",
       "27         librarian\n",
       "28            writer\n",
       "29        programmer\n",
       "30           student\n",
       "           ...      \n",
       "914            other\n",
       "915    entertainment\n",
       "916         engineer\n",
       "917          student\n",
       "918        scientist\n",
       "919            other\n",
       "920           artist\n",
       "921          student\n",
       "922    administrator\n",
       "923          student\n",
       "924            other\n",
       "925         salesman\n",
       "926    entertainment\n",
       "927       programmer\n",
       "928          student\n",
       "929        scientist\n",
       "930        scientist\n",
       "931         educator\n",
       "932         educator\n",
       "933          student\n",
       "934         engineer\n",
       "935           doctor\n",
       "936            other\n",
       "937         educator\n",
       "938       technician\n",
       "939          student\n",
       "940    administrator\n",
       "941          student\n",
       "942        librarian\n",
       "943          student\n",
       "Name: occupation, Length: 943, dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 11. Print only the occupation column\n",
    "users.occupation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 12. How many different occupations there are in this dataset?\n",
    "len(users.occupation.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "student          196\n",
       "other            105\n",
       "educator          95\n",
       "administrator     79\n",
       "engineer          67\n",
       "programmer        66\n",
       "librarian         51\n",
       "writer            45\n",
       "executive         32\n",
       "scientist         31\n",
       "artist            28\n",
       "technician        27\n",
       "marketing         26\n",
       "entertainment     18\n",
       "healthcare        16\n",
       "retired           14\n",
       "salesman          12\n",
       "lawyer            12\n",
       "none               9\n",
       "homemaker          7\n",
       "doctor             7\n",
       "Name: occupation, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 13. What is the most frequent occupation?\n",
    "users.occupation.value_counts()\n",
    "# Student is the most frequent occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>943.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.051962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.192740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age\n",
       "count  943.000000\n",
       "mean    34.051962\n",
       "std     12.192740\n",
       "min      7.000000\n",
       "25%     25.000000\n",
       "50%     31.000000\n",
       "75%     43.000000\n",
       "max     73.000000"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 14. Summarize the DataFrame using describe().\n",
    "users.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>943.000000</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "      <td>943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>55414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>670</td>\n",
       "      <td>196</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>34.051962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.192740</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age gender occupation zip_code\n",
       "count   943.000000    943        943      943\n",
       "unique         NaN      2         21      795\n",
       "top            NaN      M    student    55414\n",
       "freq           NaN    670        196        9\n",
       "mean     34.051962    NaN        NaN      NaN\n",
       "std      12.192740    NaN        NaN      NaN\n",
       "min       7.000000    NaN        NaN      NaN\n",
       "25%      25.000000    NaN        NaN      NaN\n",
       "50%      31.000000    NaN        NaN      NaN\n",
       "75%      43.000000    NaN        NaN      NaN\n",
       "max      73.000000    NaN        NaN      NaN"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 15. Summarize all the columns\n",
    "users.describe(include = \"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         943\n",
       "unique         21\n",
       "top       student\n",
       "freq          196\n",
       "Name: occupation, dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 16. Summarize only the occupation column\n",
    "users.occupation.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.05196182396607"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 17. What is the mean age of users?\n",
    "users.age.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30    39\n",
       "25    38\n",
       "22    37\n",
       "28    36\n",
       "27    35\n",
       "26    34\n",
       "24    33\n",
       "29    32\n",
       "20    32\n",
       "32    28\n",
       "23    28\n",
       "35    27\n",
       "21    27\n",
       "33    26\n",
       "31    25\n",
       "19    23\n",
       "44    23\n",
       "39    22\n",
       "40    21\n",
       "36    21\n",
       "42    21\n",
       "51    20\n",
       "50    20\n",
       "48    20\n",
       "49    19\n",
       "37    19\n",
       "18    18\n",
       "34    17\n",
       "38    17\n",
       "45    15\n",
       "      ..\n",
       "47    14\n",
       "43    13\n",
       "46    12\n",
       "53    12\n",
       "55    11\n",
       "41    10\n",
       "57     9\n",
       "60     9\n",
       "52     6\n",
       "56     6\n",
       "15     6\n",
       "13     5\n",
       "16     5\n",
       "54     4\n",
       "63     3\n",
       "14     3\n",
       "65     3\n",
       "70     3\n",
       "61     3\n",
       "59     3\n",
       "58     3\n",
       "64     2\n",
       "68     2\n",
       "69     2\n",
       "62     2\n",
       "11     1\n",
       "10     1\n",
       "73     1\n",
       "66     1\n",
       "7      1\n",
       "Name: age, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 18. What is the age with least occurrence?\n",
    "users.age.value_counts()\n",
    "# The age 7 occurs the least"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Filtering and Sorting Data (20 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we are going to pull the data of 2012 UEFA European Championship (Euro 2012) for men's national football teams directly from the internet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Step 1. Import the necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Shots on target</th>\n",
       "      <th>Shots off target</th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "      <th>% Goals-to-shots</th>\n",
       "      <th>Total shots (inc. Blocked)</th>\n",
       "      <th>Hit Woodwork</th>\n",
       "      <th>Penalty goals</th>\n",
       "      <th>Penalties not scored</th>\n",
       "      <th>...</th>\n",
       "      <th>Saves made</th>\n",
       "      <th>Saves-to-shots ratio</th>\n",
       "      <th>Fouls Won</th>\n",
       "      <th>Fouls Conceded</th>\n",
       "      <th>Offsides</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "      <th>Subs on</th>\n",
       "      <th>Subs off</th>\n",
       "      <th>Players Used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>51.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>81.3%</td>\n",
       "      <td>41</td>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>41.9%</td>\n",
       "      <td>12.9%</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>60.1%</td>\n",
       "      <td>53</td>\n",
       "      <td>73</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>20.0%</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>66.7%</td>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>17.2%</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>88.1%</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>37.9%</td>\n",
       "      <td>6.5%</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>54.6%</td>\n",
       "      <td>36</td>\n",
       "      <td>51</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>47.8%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>62.6%</td>\n",
       "      <td>63</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>30.7%</td>\n",
       "      <td>19.2%</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>65.1%</td>\n",
       "      <td>67</td>\n",
       "      <td>48</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Italy</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>45</td>\n",
       "      <td>43.0%</td>\n",
       "      <td>7.5%</td>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>74.1%</td>\n",
       "      <td>101</td>\n",
       "      <td>89</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>36</td>\n",
       "      <td>25.0%</td>\n",
       "      <td>4.1%</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>70.6%</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Poland</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>39.4%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>66.7%</td>\n",
       "      <td>48</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>42</td>\n",
       "      <td>34.3%</td>\n",
       "      <td>9.3%</td>\n",
       "      <td>82</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>71.5%</td>\n",
       "      <td>73</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>36.8%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>65.4%</td>\n",
       "      <td>43</td>\n",
       "      <td>51</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Russia</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>22.5%</td>\n",
       "      <td>12.5%</td>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>77.0%</td>\n",
       "      <td>34</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>55.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>93.8%</td>\n",
       "      <td>102</td>\n",
       "      <td>83</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>47.2%</td>\n",
       "      <td>13.8%</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>61.6%</td>\n",
       "      <td>35</td>\n",
       "      <td>51</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>21.2%</td>\n",
       "      <td>6.0%</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>76.5%</td>\n",
       "      <td>48</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team  Goals  Shots on target  Shots off target  \\\n",
       "0               Croatia      4               13                12   \n",
       "1        Czech Republic      4               13                18   \n",
       "2               Denmark      4               10                10   \n",
       "3               England      5               11                18   \n",
       "4                France      3               22                24   \n",
       "5               Germany     10               32                32   \n",
       "6                Greece      5                8                18   \n",
       "7                 Italy      6               34                45   \n",
       "8           Netherlands      2               12                36   \n",
       "9                Poland      2               15                23   \n",
       "10             Portugal      6               22                42   \n",
       "11  Republic of Ireland      1                7                12   \n",
       "12               Russia      5                9                31   \n",
       "13                Spain     12               42                33   \n",
       "14               Sweden      5               17                19   \n",
       "15              Ukraine      2                7                26   \n",
       "\n",
       "   Shooting Accuracy % Goals-to-shots  Total shots (inc. Blocked)  \\\n",
       "0              51.9%            16.0%                          32   \n",
       "1              41.9%            12.9%                          39   \n",
       "2              50.0%            20.0%                          27   \n",
       "3              50.0%            17.2%                          40   \n",
       "4              37.9%             6.5%                          65   \n",
       "5              47.8%            15.6%                          80   \n",
       "6              30.7%            19.2%                          32   \n",
       "7              43.0%             7.5%                         110   \n",
       "8              25.0%             4.1%                          60   \n",
       "9              39.4%             5.2%                          48   \n",
       "10             34.3%             9.3%                          82   \n",
       "11             36.8%             5.2%                          28   \n",
       "12             22.5%            12.5%                          59   \n",
       "13             55.9%            16.0%                         100   \n",
       "14             47.2%            13.8%                          39   \n",
       "15             21.2%             6.0%                          38   \n",
       "\n",
       "    Hit Woodwork  Penalty goals  Penalties not scored      ...       \\\n",
       "0              0              0                     0      ...        \n",
       "1              0              0                     0      ...        \n",
       "2              1              0                     0      ...        \n",
       "3              0              0                     0      ...        \n",
       "4              1              0                     0      ...        \n",
       "5              2              1                     0      ...        \n",
       "6              1              1                     1      ...        \n",
       "7              2              0                     0      ...        \n",
       "8              2              0                     0      ...        \n",
       "9              0              0                     0      ...        \n",
       "10             6              0                     0      ...        \n",
       "11             0              0                     0      ...        \n",
       "12             2              0                     0      ...        \n",
       "13             0              1                     0      ...        \n",
       "14             3              0                     0      ...        \n",
       "15             0              0                     0      ...        \n",
       "\n",
       "    Saves made  Saves-to-shots ratio  Fouls Won Fouls Conceded  Offsides  \\\n",
       "0           13                 81.3%         41             62         2   \n",
       "1            9                 60.1%         53             73         8   \n",
       "2           10                 66.7%         25             38         8   \n",
       "3           22                 88.1%         43             45         6   \n",
       "4            6                 54.6%         36             51         5   \n",
       "5           10                 62.6%         63             49        12   \n",
       "6           13                 65.1%         67             48        12   \n",
       "7           20                 74.1%        101             89        16   \n",
       "8           12                 70.6%         35             30         3   \n",
       "9            6                 66.7%         48             56         3   \n",
       "10          10                 71.5%         73             90        10   \n",
       "11          17                 65.4%         43             51        11   \n",
       "12          10                 77.0%         34             43         4   \n",
       "13          15                 93.8%        102             83        19   \n",
       "14           8                 61.6%         35             51         7   \n",
       "15          13                 76.5%         48             31         4   \n",
       "\n",
       "    Yellow Cards  Red Cards  Subs on  Subs off  Players Used  \n",
       "0              9          0        9         9            16  \n",
       "1              7          0       11        11            19  \n",
       "2              4          0        7         7            15  \n",
       "3              5          0       11        11            16  \n",
       "4              6          0       11        11            19  \n",
       "5              4          0       15        15            17  \n",
       "6              9          1       12        12            20  \n",
       "7             16          0       18        18            19  \n",
       "8              5          0        7         7            15  \n",
       "9              7          1        7         7            17  \n",
       "10            12          0       14        14            16  \n",
       "11             6          1       10        10            17  \n",
       "12             6          0        7         7            16  \n",
       "13            11          0       17        17            18  \n",
       "14             7          0        9         9            18  \n",
       "15             5          0        9         9            18  \n",
       "\n",
       "[16 rows x 35 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/jokecamp/FootballData/master/Euro%202012/Euro%202012%20stats%20TEAM.csv). \n",
    "### Step 3. Assign it to a variable called euro12.\n",
    "euro12 = pd.read_csv('C:/Users/casca/Downloads/Job & Edu/Spring 2019/AdvProgGIS/Lab2/Lab2/Euro2012.csv')\n",
    "###euro12 = pd.read_csv(r'C:/Users/casca/Downloads/Job&Edu/Spring2019/AdvProgGIS/Lab2/Euro12.csv')\n",
    "euro12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      4\n",
       "1      4\n",
       "2      4\n",
       "3      5\n",
       "4      3\n",
       "5     10\n",
       "6      5\n",
       "7      6\n",
       "8      2\n",
       "9      2\n",
       "10     6\n",
       "11     1\n",
       "12     5\n",
       "13    12\n",
       "14     5\n",
       "15     2\n",
       "Name: Goals, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 4. Select only the Goal column.\n",
    "euro12['Goals']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 35)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 5. How many team participated in the Euro2012? (Hint: shape[])\n",
    "euro12.shape\n",
    "# 16 teams participated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Step 6. What is the number of columns in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16 entries, 0 to 15\n",
      "Data columns (total 35 columns):\n",
      "Team                          16 non-null object\n",
      "Goals                         16 non-null int64\n",
      "Shots on target               16 non-null int64\n",
      "Shots off target              16 non-null int64\n",
      "Shooting Accuracy             16 non-null object\n",
      "% Goals-to-shots              16 non-null object\n",
      "Total shots (inc. Blocked)    16 non-null int64\n",
      "Hit Woodwork                  16 non-null int64\n",
      "Penalty goals                 16 non-null int64\n",
      "Penalties not scored          16 non-null int64\n",
      "Headed goals                  16 non-null int64\n",
      "Passes                        16 non-null int64\n",
      "Passes completed              16 non-null int64\n",
      "Passing Accuracy              16 non-null object\n",
      "Touches                       16 non-null int64\n",
      "Crosses                       16 non-null int64\n",
      "Dribbles                      16 non-null int64\n",
      "Corners Taken                 16 non-null int64\n",
      "Tackles                       16 non-null int64\n",
      "Clearances                    16 non-null int64\n",
      "Interceptions                 16 non-null int64\n",
      "Clearances off line           15 non-null float64\n",
      "Clean Sheets                  16 non-null int64\n",
      "Blocks                        16 non-null int64\n",
      "Goals conceded                16 non-null int64\n",
      "Saves made                    16 non-null int64\n",
      "Saves-to-shots ratio          16 non-null object\n",
      "Fouls Won                     16 non-null int64\n",
      "Fouls Conceded                16 non-null int64\n",
      "Offsides                      16 non-null int64\n",
      "Yellow Cards                  16 non-null int64\n",
      "Red Cards                     16 non-null int64\n",
      "Subs on                       16 non-null int64\n",
      "Subs off                      16 non-null int64\n",
      "Players Used                  16 non-null int64\n",
      "dtypes: float64(1), int64(29), object(5)\n",
      "memory usage: 4.1+ KB\n"
     ]
    }
   ],
   "source": [
    "euro12.info()\n",
    "# There are 35 columns (third row from the top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Italy</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Poland</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Russia</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team  Yellow Cards  Red Cards\n",
       "0               Croatia             9          0\n",
       "1        Czech Republic             7          0\n",
       "2               Denmark             4          0\n",
       "3               England             5          0\n",
       "4                France             6          0\n",
       "5               Germany             4          0\n",
       "6                Greece             9          1\n",
       "7                 Italy            16          0\n",
       "8           Netherlands             5          0\n",
       "9                Poland             7          1\n",
       "10             Portugal            12          0\n",
       "11  Republic of Ireland             6          1\n",
       "12               Russia             6          0\n",
       "13                Spain            11          0\n",
       "14               Sweden             7          0\n",
       "15              Ukraine             5          0"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 7. View only the columns Team, Yellow Cards and Red Cards and assign them to a dataframe called discipline\n",
    "\n",
    "select_T_YC_RC =euro12.loc[:,['Team','Yellow Cards','Red Cards']]\n",
    "discipline = pd.DataFrame(select_T_YC_RC)\n",
    "discipline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Russia</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Poland</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Italy</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team  Yellow Cards  Red Cards\n",
       "2               Denmark             4          0\n",
       "5               Germany             4          0\n",
       "3               England             5          0\n",
       "8           Netherlands             5          0\n",
       "15              Ukraine             5          0\n",
       "4                France             6          0\n",
       "11  Republic of Ireland             6          1\n",
       "12               Russia             6          0\n",
       "1        Czech Republic             7          0\n",
       "9                Poland             7          1\n",
       "14               Sweden             7          0\n",
       "0               Croatia             9          0\n",
       "6                Greece             9          1\n",
       "13                Spain            11          0\n",
       "10             Portugal            12          0\n",
       "7                 Italy            16          0"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 8. Sort the teams by Red Cards, then to Yellow Cards (Hint: sort_values())\n",
    "\n",
    "discipline.sort_values('Red Cards')\n",
    "discipline.sort_values('Yellow Cards')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4.5\n",
       "1     3.5\n",
       "2     2.0\n",
       "3     2.5\n",
       "4     3.0\n",
       "5     2.0\n",
       "6     5.0\n",
       "7     8.0\n",
       "8     2.5\n",
       "9     4.0\n",
       "10    6.0\n",
       "11    3.5\n",
       "12    3.0\n",
       "13    5.5\n",
       "14    3.5\n",
       "15    2.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 9. Calculate the mean Yellow Cards given per Team\n",
    "discipline.mean(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Shots on target</th>\n",
       "      <th>Shots off target</th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "      <th>% Goals-to-shots</th>\n",
       "      <th>Total shots (inc. Blocked)</th>\n",
       "      <th>Hit Woodwork</th>\n",
       "      <th>Penalty goals</th>\n",
       "      <th>Penalties not scored</th>\n",
       "      <th>...</th>\n",
       "      <th>Saves made</th>\n",
       "      <th>Saves-to-shots ratio</th>\n",
       "      <th>Fouls Won</th>\n",
       "      <th>Fouls Conceded</th>\n",
       "      <th>Offsides</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "      <th>Subs on</th>\n",
       "      <th>Subs off</th>\n",
       "      <th>Players Used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>47.8%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>62.6%</td>\n",
       "      <td>63</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>55.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>93.8%</td>\n",
       "      <td>102</td>\n",
       "      <td>83</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Team  Goals  Shots on target  Shots off target Shooting Accuracy  \\\n",
       "5   Germany     10               32                32             47.8%   \n",
       "13    Spain     12               42                33             55.9%   \n",
       "\n",
       "   % Goals-to-shots  Total shots (inc. Blocked)  Hit Woodwork  Penalty goals  \\\n",
       "5             15.6%                          80             2              1   \n",
       "13            16.0%                         100             0              1   \n",
       "\n",
       "    Penalties not scored      ...       Saves made  Saves-to-shots ratio  \\\n",
       "5                      0      ...               10                 62.6%   \n",
       "13                     0      ...               15                 93.8%   \n",
       "\n",
       "    Fouls Won Fouls Conceded  Offsides  Yellow Cards  Red Cards  Subs on  \\\n",
       "5          63             49        12             4          0       15   \n",
       "13        102             83        19            11          0       17   \n",
       "\n",
       "    Subs off  Players Used  \n",
       "5         15            17  \n",
       "13        17            18  \n",
       "\n",
       "[2 rows x 35 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 10. Filter teams that scored more than 6 goals\n",
    "euro12[euro12['Goals'] > 6] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Shots on target</th>\n",
       "      <th>Shots off target</th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "      <th>% Goals-to-shots</th>\n",
       "      <th>Total shots (inc. Blocked)</th>\n",
       "      <th>Hit Woodwork</th>\n",
       "      <th>Penalty goals</th>\n",
       "      <th>Penalties not scored</th>\n",
       "      <th>...</th>\n",
       "      <th>Saves made</th>\n",
       "      <th>Saves-to-shots ratio</th>\n",
       "      <th>Fouls Won</th>\n",
       "      <th>Fouls Conceded</th>\n",
       "      <th>Offsides</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "      <th>Subs on</th>\n",
       "      <th>Subs off</th>\n",
       "      <th>Players Used</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>47.8%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>62.6%</td>\n",
       "      <td>63</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>30.7%</td>\n",
       "      <td>19.2%</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>65.1%</td>\n",
       "      <td>67</td>\n",
       "      <td>48</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Team  Goals  Shots on target  Shots off target Shooting Accuracy  \\\n",
       "5  Germany     10               32                32             47.8%   \n",
       "6   Greece      5                8                18             30.7%   \n",
       "\n",
       "  % Goals-to-shots  Total shots (inc. Blocked)  Hit Woodwork  Penalty goals  \\\n",
       "5            15.6%                          80             2              1   \n",
       "6            19.2%                          32             1              1   \n",
       "\n",
       "   Penalties not scored      ...       Saves made  Saves-to-shots ratio  \\\n",
       "5                     0      ...               10                 62.6%   \n",
       "6                     1      ...               13                 65.1%   \n",
       "\n",
       "   Fouls Won Fouls Conceded  Offsides  Yellow Cards  Red Cards  Subs on  \\\n",
       "5         63             49        12             4          0       15   \n",
       "6         67             48        12             9          1       12   \n",
       "\n",
       "   Subs off  Players Used  \n",
       "5        15            17  \n",
       "6        12            20  \n",
       "\n",
       "[2 rows x 35 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 11. Select the teams that start with G\n",
    "euro12[euro12.Team.str.startswith('G')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Shots on target</th>\n",
       "      <th>Shots off target</th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "      <th>% Goals-to-shots</th>\n",
       "      <th>Total shots (inc. Blocked)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>51.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>41.9%</td>\n",
       "      <td>12.9%</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>20.0%</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>17.2%</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>37.9%</td>\n",
       "      <td>6.5%</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>47.8%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>30.7%</td>\n",
       "      <td>19.2%</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Italy</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>45</td>\n",
       "      <td>43.0%</td>\n",
       "      <td>7.5%</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>36</td>\n",
       "      <td>25.0%</td>\n",
       "      <td>4.1%</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Poland</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>39.4%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>42</td>\n",
       "      <td>34.3%</td>\n",
       "      <td>9.3%</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>36.8%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Russia</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>22.5%</td>\n",
       "      <td>12.5%</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>55.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>47.2%</td>\n",
       "      <td>13.8%</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>21.2%</td>\n",
       "      <td>6.0%</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team  Goals  Shots on target  Shots off target  \\\n",
       "0               Croatia      4               13                12   \n",
       "1        Czech Republic      4               13                18   \n",
       "2               Denmark      4               10                10   \n",
       "3               England      5               11                18   \n",
       "4                France      3               22                24   \n",
       "5               Germany     10               32                32   \n",
       "6                Greece      5                8                18   \n",
       "7                 Italy      6               34                45   \n",
       "8           Netherlands      2               12                36   \n",
       "9                Poland      2               15                23   \n",
       "10             Portugal      6               22                42   \n",
       "11  Republic of Ireland      1                7                12   \n",
       "12               Russia      5                9                31   \n",
       "13                Spain     12               42                33   \n",
       "14               Sweden      5               17                19   \n",
       "15              Ukraine      2                7                26   \n",
       "\n",
       "   Shooting Accuracy % Goals-to-shots  Total shots (inc. Blocked)  \n",
       "0              51.9%            16.0%                          32  \n",
       "1              41.9%            12.9%                          39  \n",
       "2              50.0%            20.0%                          27  \n",
       "3              50.0%            17.2%                          40  \n",
       "4              37.9%             6.5%                          65  \n",
       "5              47.8%            15.6%                          80  \n",
       "6              30.7%            19.2%                          32  \n",
       "7              43.0%             7.5%                         110  \n",
       "8              25.0%             4.1%                          60  \n",
       "9              39.4%             5.2%                          48  \n",
       "10             34.3%             9.3%                          82  \n",
       "11             36.8%             5.2%                          28  \n",
       "12             22.5%            12.5%                          59  \n",
       "13             55.9%            16.0%                         100  \n",
       "14             47.2%            13.8%                          39  \n",
       "15             21.2%             6.0%                          38  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 12. Select the first 7 columns\n",
    "euro12.iloc[:,:7]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Shots on target</th>\n",
       "      <th>Shots off target</th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "      <th>% Goals-to-shots</th>\n",
       "      <th>Total shots (inc. Blocked)</th>\n",
       "      <th>Hit Woodwork</th>\n",
       "      <th>Penalty goals</th>\n",
       "      <th>Penalties not scored</th>\n",
       "      <th>...</th>\n",
       "      <th>Clean Sheets</th>\n",
       "      <th>Blocks</th>\n",
       "      <th>Goals conceded</th>\n",
       "      <th>Saves made</th>\n",
       "      <th>Saves-to-shots ratio</th>\n",
       "      <th>Fouls Won</th>\n",
       "      <th>Fouls Conceded</th>\n",
       "      <th>Offsides</th>\n",
       "      <th>Yellow Cards</th>\n",
       "      <th>Red Cards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>51.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>81.3%</td>\n",
       "      <td>41</td>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>41.9%</td>\n",
       "      <td>12.9%</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>60.1%</td>\n",
       "      <td>53</td>\n",
       "      <td>73</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>20.0%</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>66.7%</td>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>England</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>50.0%</td>\n",
       "      <td>17.2%</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>88.1%</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>37.9%</td>\n",
       "      <td>6.5%</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>54.6%</td>\n",
       "      <td>36</td>\n",
       "      <td>51</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>47.8%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>62.6%</td>\n",
       "      <td>63</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Greece</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>30.7%</td>\n",
       "      <td>19.2%</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>65.1%</td>\n",
       "      <td>67</td>\n",
       "      <td>48</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Italy</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>45</td>\n",
       "      <td>43.0%</td>\n",
       "      <td>7.5%</td>\n",
       "      <td>110</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>74.1%</td>\n",
       "      <td>101</td>\n",
       "      <td>89</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>36</td>\n",
       "      <td>25.0%</td>\n",
       "      <td>4.1%</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>70.6%</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Poland</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "      <td>39.4%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>66.7%</td>\n",
       "      <td>48</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>42</td>\n",
       "      <td>34.3%</td>\n",
       "      <td>9.3%</td>\n",
       "      <td>82</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>71.5%</td>\n",
       "      <td>73</td>\n",
       "      <td>90</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>36.8%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>65.4%</td>\n",
       "      <td>43</td>\n",
       "      <td>51</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Russia</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>22.5%</td>\n",
       "      <td>12.5%</td>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>77.0%</td>\n",
       "      <td>34</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Spain</td>\n",
       "      <td>12</td>\n",
       "      <td>42</td>\n",
       "      <td>33</td>\n",
       "      <td>55.9%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>93.8%</td>\n",
       "      <td>102</td>\n",
       "      <td>83</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>47.2%</td>\n",
       "      <td>13.8%</td>\n",
       "      <td>39</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>61.6%</td>\n",
       "      <td>35</td>\n",
       "      <td>51</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>21.2%</td>\n",
       "      <td>6.0%</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>76.5%</td>\n",
       "      <td>48</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Team  Goals  Shots on target  Shots off target  \\\n",
       "0               Croatia      4               13                12   \n",
       "1        Czech Republic      4               13                18   \n",
       "2               Denmark      4               10                10   \n",
       "3               England      5               11                18   \n",
       "4                France      3               22                24   \n",
       "5               Germany     10               32                32   \n",
       "6                Greece      5                8                18   \n",
       "7                 Italy      6               34                45   \n",
       "8           Netherlands      2               12                36   \n",
       "9                Poland      2               15                23   \n",
       "10             Portugal      6               22                42   \n",
       "11  Republic of Ireland      1                7                12   \n",
       "12               Russia      5                9                31   \n",
       "13                Spain     12               42                33   \n",
       "14               Sweden      5               17                19   \n",
       "15              Ukraine      2                7                26   \n",
       "\n",
       "   Shooting Accuracy % Goals-to-shots  Total shots (inc. Blocked)  \\\n",
       "0              51.9%            16.0%                          32   \n",
       "1              41.9%            12.9%                          39   \n",
       "2              50.0%            20.0%                          27   \n",
       "3              50.0%            17.2%                          40   \n",
       "4              37.9%             6.5%                          65   \n",
       "5              47.8%            15.6%                          80   \n",
       "6              30.7%            19.2%                          32   \n",
       "7              43.0%             7.5%                         110   \n",
       "8              25.0%             4.1%                          60   \n",
       "9              39.4%             5.2%                          48   \n",
       "10             34.3%             9.3%                          82   \n",
       "11             36.8%             5.2%                          28   \n",
       "12             22.5%            12.5%                          59   \n",
       "13             55.9%            16.0%                         100   \n",
       "14             47.2%            13.8%                          39   \n",
       "15             21.2%             6.0%                          38   \n",
       "\n",
       "    Hit Woodwork  Penalty goals  Penalties not scored    ...      \\\n",
       "0              0              0                     0    ...       \n",
       "1              0              0                     0    ...       \n",
       "2              1              0                     0    ...       \n",
       "3              0              0                     0    ...       \n",
       "4              1              0                     0    ...       \n",
       "5              2              1                     0    ...       \n",
       "6              1              1                     1    ...       \n",
       "7              2              0                     0    ...       \n",
       "8              2              0                     0    ...       \n",
       "9              0              0                     0    ...       \n",
       "10             6              0                     0    ...       \n",
       "11             0              0                     0    ...       \n",
       "12             2              0                     0    ...       \n",
       "13             0              1                     0    ...       \n",
       "14             3              0                     0    ...       \n",
       "15             0              0                     0    ...       \n",
       "\n",
       "    Clean Sheets  Blocks  Goals conceded Saves made  Saves-to-shots ratio  \\\n",
       "0              0      10               3         13                 81.3%   \n",
       "1              1      10               6          9                 60.1%   \n",
       "2              1      10               5         10                 66.7%   \n",
       "3              2      29               3         22                 88.1%   \n",
       "4              1       7               5          6                 54.6%   \n",
       "5              1      11               6         10                 62.6%   \n",
       "6              1      23               7         13                 65.1%   \n",
       "7              2      18               7         20                 74.1%   \n",
       "8              0       9               5         12                 70.6%   \n",
       "9              0       8               3          6                 66.7%   \n",
       "10             2      11               4         10                 71.5%   \n",
       "11             0      23               9         17                 65.4%   \n",
       "12             0       8               3         10                 77.0%   \n",
       "13             5       8               1         15                 93.8%   \n",
       "14             1      12               5          8                 61.6%   \n",
       "15             0       4               4         13                 76.5%   \n",
       "\n",
       "    Fouls Won  Fouls Conceded  Offsides  Yellow Cards  Red Cards  \n",
       "0          41              62         2             9          0  \n",
       "1          53              73         8             7          0  \n",
       "2          25              38         8             4          0  \n",
       "3          43              45         6             5          0  \n",
       "4          36              51         5             6          0  \n",
       "5          63              49        12             4          0  \n",
       "6          67              48        12             9          1  \n",
       "7         101              89        16            16          0  \n",
       "8          35              30         3             5          0  \n",
       "9          48              56         3             7          1  \n",
       "10         73              90        10            12          0  \n",
       "11         43              51        11             6          1  \n",
       "12         34              43         4             6          0  \n",
       "13        102              83        19            11          0  \n",
       "14         35              51         7             7          0  \n",
       "15         48              31         4             5          0  \n",
       "\n",
       "[16 rows x 32 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 13. Select all columns except the last 3.\n",
    "\n",
    "euro12.iloc[:,:32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Shooting Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>43.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>22.5%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Shooting Accuracy\n",
       "3              50.0%\n",
       "7              43.0%\n",
       "12             22.5%"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 14. Present only the Shooting Accuracy from England, Italy and Russia\n",
    "euro12.loc[[3,7,12],['Shooting Accuracy']]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Merge, Join tables (15 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time you will create a data again by merging and joining tables.\n",
    "\n",
    "Special thanks to [Chris Albon](http://chrisalbon.com/) for sharing the dataset and materials.\n",
    "All the credits to this exercise belongs to him.  \n",
    "\n",
    "In order to understand about it go [here](https://blog.codinghorror.com/a-visual-explanation-of-sql-joins/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Step 1. Import the necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Step 2. Create the 3 DataFrames based on the followin raw data\n",
    "raw_data_1 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5'],\n",
    "        'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], \n",
    "        'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}\n",
    "\n",
    "raw_data_2 = {\n",
    "        'subject_id': ['4', '5', '6', '7', '8'],\n",
    "        'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], \n",
    "        'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}\n",
    "\n",
    "raw_data_3 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],\n",
    "        'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raw_data_1 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5'],\n",
    "        'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], \n",
    "        'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}\n",
    "\n",
    "raw_data_2 = {\n",
    "        'subject_id': ['4', '5', '6', '7', '8'],\n",
    "        'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], \n",
    "        'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}\n",
    "\n",
    "raw_data_3 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],\n",
    "        'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Step 3. Create 3 dataframes (data1, data2, data3) based on the 3 datasets provided\n",
    "### e.g. data1 = pd.DataFrame(raw_data_1, columns = ['subject_id', 'first_name', 'last_name'])\n",
    "data1=pd.DataFrame(raw_data_1, columns = ['subject_id','first_name','last_name'])\n",
    "data2=pd.DataFrame(raw_data_2, columns = ['subject_id','first_name','last_name'])\n",
    "data3=pd.DataFrame(raw_data_3, columns = ['subject_id','test_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>Bran</td>\n",
       "      <td>Balwner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name\n",
       "0          1       Alex  Anderson\n",
       "1          2        Amy  Ackerman\n",
       "2          3      Allen       Ali\n",
       "3          4      Alice      Aoni\n",
       "4          5     Ayoung   Atiches\n",
       "0          4      Billy    Bonder\n",
       "1          5      Brian     Black\n",
       "2          6       Bran   Balwner\n",
       "3          7      Bryce     Brice\n",
       "4          8      Betty    Btisan"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 4. Combine data1 and data2 using concat() to make a new dataframe called all_data\n",
    "raw_data_1 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5'],\n",
    "        'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], \n",
    "        'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}\n",
    "\n",
    "raw_data_2 = {\n",
    "        'subject_id': ['4', '5', '6', '7', '8'],\n",
    "        'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], \n",
    "        'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}\n",
    "\n",
    "raw_data_3 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],\n",
    "        'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}\n",
    "\n",
    "data1=pd.DataFrame(raw_data_1, columns = ['subject_id','first_name','last_name'])\n",
    "data2=pd.DataFrame(raw_data_2, columns = ['subject_id','first_name','last_name'])\n",
    "data3=pd.DataFrame(raw_data_3, columns = ['subject_id','test_id'])\n",
    "\n",
    "data_1_2 = [data1, data2]\n",
    "all_data = pd.concat(data_1_2)\n",
    "all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>4</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "      <td>5</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "      <td>6</td>\n",
       "      <td>Bran</td>\n",
       "      <td>Balwner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>7</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>8</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name subject_id first_name last_name\n",
       "0          1       Alex  Anderson          4      Billy    Bonder\n",
       "1          2        Amy  Ackerman          5      Brian     Black\n",
       "2          3      Allen       Ali          6       Bran   Balwner\n",
       "3          4      Alice      Aoni          7      Bryce     Brice\n",
       "4          5     Ayoung   Atiches          8      Betty    Btisan"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 5. Join the two dataframes along columns and assing to all_data_col\n",
    "all_data_col = pd.concat([data1, data2], axis = 1)\n",
    "all_data_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  subject_id  test_id\n",
      "0          1       51\n",
      "1          2       15\n",
      "2          3       15\n",
      "3          4       61\n",
      "4          5       16\n",
      "5          7       14\n",
      "6          8       15\n",
      "7          9        1\n",
      "8         10       61\n",
      "9         11       16\n"
     ]
    }
   ],
   "source": [
    "### Step 6. Print data3\n",
    "print(data3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>test_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Billy</td>\n",
       "      <td>Bonder</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Black</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name  test_id\n",
       "0          1       Alex  Anderson       51\n",
       "1          2        Amy  Ackerman       15\n",
       "2          3      Allen       Ali       15\n",
       "3          4      Alice      Aoni       61\n",
       "4          4      Billy    Bonder       61\n",
       "5          5     Ayoung   Atiches       16\n",
       "6          5      Brian     Black       16\n",
       "7          7      Bryce     Brice       14\n",
       "8          8      Betty    Btisan       15"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Step 7. Table join all_data and data3 using shared key subject_id (Hint: merge())\n",
    "\n",
    "result = pd.merge(all_data, data3, on='subject_id')\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Deliverable: Upload your finished notebook file (renamed it to \"LastName_Lab2.ipynb\") on UNM Learn in the Assignments section.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Q2: step5: users.tail(10) (-2)\n",
    "\n",
    "#Q3: step9: round(discipline['Yellow Cards'].mean()) (-3)﻿﻿﻿\n",
    "\n",
    "#Good job! Please submit on time next time.﻿﻿"
   ]
  }
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