PandasDrills.ipynb

{
 "cells": [
  {
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Conference</th>\n",
       "      <th>Division</th>\n",
       "      <th>Team</th>\n",
       "      <th>Player</th>\n",
       "      <th>Age</th>\n",
       "      <th>Games</th>\n",
       "      <th>Games Started</th>\n",
       "      <th>Minutes Played</th>\n",
       "      <th>Field Goals</th>\n",
       "      <th>Field Goal Attempts</th>\n",
       "      <th>...</th>\n",
       "      <th>Free Throw Percentage</th>\n",
       "      <th>Offensive Rebounds</th>\n",
       "      <th>Defensive Rebounds</th>\n",
       "      <th>Total Rebounds</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Steals</th>\n",
       "      <th>Blocks</th>\n",
       "      <th>Turnovers</th>\n",
       "      <th>Personal Fouls</th>\n",
       "      <th>Points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Eastern</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>Jayson Tatum</td>\n",
       "      <td>20</td>\n",
       "      <td>79</td>\n",
       "      <td>79</td>\n",
       "      <td>2455</td>\n",
       "      <td>466</td>\n",
       "      <td>1036</td>\n",
       "      <td>...</td>\n",
       "      <td>0.855</td>\n",
       "      <td>70</td>\n",
       "      <td>407</td>\n",
       "      <td>477</td>\n",
       "      <td>168</td>\n",
       "      <td>84</td>\n",
       "      <td>57</td>\n",
       "      <td>122</td>\n",
       "      <td>168</td>\n",
       "      <td>1243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Eastern</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>Kyrie Irving</td>\n",
       "      <td>26</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>2214</td>\n",
       "      <td>604</td>\n",
       "      <td>1241</td>\n",
       "      <td>...</td>\n",
       "      <td>0.873</td>\n",
       "      <td>71</td>\n",
       "      <td>264</td>\n",
       "      <td>335</td>\n",
       "      <td>464</td>\n",
       "      <td>103</td>\n",
       "      <td>34</td>\n",
       "      <td>172</td>\n",
       "      <td>167</td>\n",
       "      <td>1596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eastern</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>Marcus Smart</td>\n",
       "      <td>24</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>2200</td>\n",
       "      <td>239</td>\n",
       "      <td>567</td>\n",
       "      <td>...</td>\n",
       "      <td>0.806</td>\n",
       "      <td>57</td>\n",
       "      <td>177</td>\n",
       "      <td>234</td>\n",
       "      <td>321</td>\n",
       "      <td>143</td>\n",
       "      <td>28</td>\n",
       "      <td>123</td>\n",
       "      <td>201</td>\n",
       "      <td>708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eastern</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>Marcus Morris</td>\n",
       "      <td>29</td>\n",
       "      <td>75</td>\n",
       "      <td>53</td>\n",
       "      <td>2091</td>\n",
       "      <td>377</td>\n",
       "      <td>844</td>\n",
       "      <td>...</td>\n",
       "      <td>0.844</td>\n",
       "      <td>76</td>\n",
       "      <td>382</td>\n",
       "      <td>458</td>\n",
       "      <td>109</td>\n",
       "      <td>43</td>\n",
       "      <td>25</td>\n",
       "      <td>92</td>\n",
       "      <td>181</td>\n",
       "      <td>1046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eastern</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>Al Horford</td>\n",
       "      <td>32</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>1973</td>\n",
       "      <td>387</td>\n",
       "      <td>723</td>\n",
       "      <td>...</td>\n",
       "      <td>0.821</td>\n",
       "      <td>120</td>\n",
       "      <td>338</td>\n",
       "      <td>458</td>\n",
       "      <td>283</td>\n",
       "      <td>59</td>\n",
       "      <td>86</td>\n",
       "      <td>102</td>\n",
       "      <td>126</td>\n",
       "      <td>925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Conference  Division            Team         Player  Age  Games  \\\n",
       "0    Eastern  Atlantic  Boston Celtics   Jayson Tatum   20     79   \n",
       "1    Eastern  Atlantic  Boston Celtics   Kyrie Irving   26     67   \n",
       "2    Eastern  Atlantic  Boston Celtics   Marcus Smart   24     80   \n",
       "3    Eastern  Atlantic  Boston Celtics  Marcus Morris   29     75   \n",
       "4    Eastern  Atlantic  Boston Celtics     Al Horford   32     68   \n",
       "\n",
       "   Games Started  Minutes Played  Field Goals  Field Goal Attempts  ...  \\\n",
       "0             79            2455          466                 1036  ...   \n",
       "1             67            2214          604                 1241  ...   \n",
       "2             60            2200          239                  567  ...   \n",
       "3             53            2091          377                  844  ...   \n",
       "4             68            1973          387                  723  ...   \n",
       "\n",
       "   Free Throw Percentage  Offensive Rebounds  Defensive Rebounds  \\\n",
       "0                  0.855                  70                 407   \n",
       "1                  0.873                  71                 264   \n",
       "2                  0.806                  57                 177   \n",
       "3                  0.844                  76                 382   \n",
       "4                  0.821                 120                 338   \n",
       "\n",
       "   Total Rebounds  Assists  Steals  Blocks  Turnovers  Personal Fouls  Points  \n",
       "0             477      168      84      57        122             168    1243  \n",
       "1             335      464     103      34        172             167    1596  \n",
       "2             234      321     143      28        123             201     708  \n",
       "3             458      109      43      25         92             181    1046  \n",
       "4             458      283      59      86        102             126     925  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "# Read data from file 'filename.csv' \n",
    "# nbaStats = pd.read_csv(\"NBA 2018-19 Season.csv\") \n",
    "nbaStats = pd.read_csv(\"C:/Users/jpkee/Desktop/Udemy Tableau Certified Associate Exam/P86-Section-3-Tableau-Fundamentals-Part-1/P86-Section-3-Tableau-Fundamentals-Part-1/NBA 2018-19 Season.csv\") \n",
    "# C:\\Users\\jpkee\\Desktop\\Udemy Tableau Certified Associate Exam\\P86-Section-3-Tableau-Fundamentals-Part-1\\P86-Section-3-Tableau-Fundamentals-Part-1\\NBA 2018-19 Season.csv\n",
    "# Preview the first 5 lines of the loaded data \n",
    "nbaStats.head()\n",
    "# or we can get a sample of rows w this command\n",
    "# nbaStats.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=622, step=1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbaStats.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(622, 30)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now let's get the shape\n",
    "nbaStats.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 622 entries, 0 to 621\n",
      "Data columns (total 30 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   Conference                       622 non-null    object \n",
      " 1   Division                         622 non-null    object \n",
      " 2   Team                             622 non-null    object \n",
      " 3   Player                           622 non-null    object \n",
      " 4   Age                              622 non-null    int64  \n",
      " 5   Games                            622 non-null    int64  \n",
      " 6   Games Started                    622 non-null    int64  \n",
      " 7   Minutes Played                   622 non-null    int64  \n",
      " 8   Field Goals                      622 non-null    int64  \n",
      " 9   Field Goal Attempts              622 non-null    int64  \n",
      " 10  Field Goal Percentage            616 non-null    float64\n",
      " 11  3-Point Field Goals              622 non-null    int64  \n",
      " 12  3-Point Field Goal Attempts      622 non-null    int64  \n",
      " 13  3-Point Field Goal Percentage    578 non-null    float64\n",
      " 14  2-Point Field Goals              622 non-null    int64  \n",
      " 15  2-Point Field Goal Attempts      622 non-null    int64  \n",
      " 16  2-Point Field Goal Percentage    607 non-null    float64\n",
      " 17  Effective Field Goal Percentage  616 non-null    float64\n",
      " 18  Free Throws                      622 non-null    int64  \n",
      " 19  Free Throw Attempts              622 non-null    int64  \n",
      " 20  Free Throw Percentage            581 non-null    float64\n",
      " 21  Offensive Rebounds               622 non-null    int64  \n",
      " 22  Defensive Rebounds               622 non-null    int64  \n",
      " 23  Total Rebounds                   622 non-null    int64  \n",
      " 24  Assists                          622 non-null    int64  \n",
      " 25  Steals                           622 non-null    int64  \n",
      " 26  Blocks                           622 non-null    int64  \n",
      " 27  Turnovers                        622 non-null    int64  \n",
      " 28  Personal Fouls                   622 non-null    int64  \n",
      " 29  Points                           622 non-null    int64  \n",
      "dtypes: float64(5), int64(21), object(4)\n",
      "memory usage: 145.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# .info is super useful too\n",
    "nbaStats.info()\n",
    "#try it with and w/o the '()'\n",
    "# is it like pivoting?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Division</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Boston Celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Boston Celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Boston Celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Boston Celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Boston Celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>Southwest</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>Southwest</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>Southwest</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>Southwest</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>Southwest</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>622 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Division              Team\n",
       "0     Atlantic    Boston Celtics\n",
       "1     Atlantic    Boston Celtics\n",
       "2     Atlantic    Boston Celtics\n",
       "3     Atlantic    Boston Celtics\n",
       "4     Atlantic    Boston Celtics\n",
       "..         ...               ...\n",
       "617  Southwest  Dallas Mavericks\n",
       "618  Southwest  Dallas Mavericks\n",
       "619  Southwest  Dallas Mavericks\n",
       "620  Southwest  Dallas Mavericks\n",
       "621  Southwest  Dallas Mavericks\n",
       "\n",
       "[622 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grab a couple columns\n",
    "# per https://data36.com/pandas-tutorial-1-basics-reading-data-files-dataframes-data-selection/\n",
    "# the outer bracket frames tell pandas that you want to select columns, \n",
    "# and the inner brackets are for the list of the column names.\n",
    "nbaStats[['Division', 'Team']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Conference</th>\n",
       "      <th>Division</th>\n",
       "      <th>Team</th>\n",
       "      <th>Player</th>\n",
       "      <th>Age</th>\n",
       "      <th>Games</th>\n",
       "      <th>Games Started</th>\n",
       "      <th>Minutes Played</th>\n",
       "      <th>Field Goals</th>\n",
       "      <th>Field Goal Attempts</th>\n",
       "      <th>...</th>\n",
       "      <th>Free Throw Percentage</th>\n",
       "      <th>Offensive Rebounds</th>\n",
       "      <th>Defensive Rebounds</th>\n",
       "      <th>Total Rebounds</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Steals</th>\n",
       "      <th>Blocks</th>\n",
       "      <th>Turnovers</th>\n",
       "      <th>Personal Fouls</th>\n",
       "      <th>Points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>Western</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>Harrison Barnes</td>\n",
       "      <td>26</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>1584</td>\n",
       "      <td>290</td>\n",
       "      <td>717</td>\n",
       "      <td>...</td>\n",
       "      <td>0.833</td>\n",
       "      <td>36</td>\n",
       "      <td>171</td>\n",
       "      <td>207</td>\n",
       "      <td>62</td>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>68</td>\n",
       "      <td>79</td>\n",
       "      <td>866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Conference   Division              Team           Player  Age  Games  \\\n",
       "605    Western  Southwest  Dallas Mavericks  Harrison Barnes   26     49   \n",
       "\n",
       "     Games Started  Minutes Played  Field Goals  Field Goal Attempts  ...  \\\n",
       "605             49            1584          290                  717  ...   \n",
       "\n",
       "     Free Throw Percentage  Offensive Rebounds  Defensive Rebounds  \\\n",
       "605                  0.833                  36                 171   \n",
       "\n",
       "     Total Rebounds  Assists  Steals  Blocks  Turnovers  Personal Fouls  \\\n",
       "605             207       62      33      11         68              79   \n",
       "\n",
       "     Points  \n",
       "605     866  \n",
       "\n",
       "[1 rows x 30 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://www.marsja.se/pandas-sample-randomly-select-rows/\n",
    "# now let's get a sample\n",
    "# w/o a parameter we'll just get 1 row\n",
    "nbaStats.sample()\n",
    "# but let's get a sample of 10\n",
    "# nbaStats.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Offensive Rebounds    423\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataframes can be sliced, like numpy arrays or python lists\n",
    "# use colons to specify the start, end and stride of a slice\n",
    "\n",
    "#iloc is the position, loc is by label\n",
    "\n",
    "# nbaStats.loc[:, ['Age']].max()\n",
    "# nbaStats.loc[:, ['Games']].max()\n",
    "# nbaStats.loc[:, ['Minutes Played']].max()\n",
    "# nbaStats.loc[:, ['Field Goals']].max()\n",
    "nbaStats.loc[:, ['Offensive Rebounds']].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age    26.048232\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#how to select an average from a column?\n",
    "#here is the average age\n",
    "nbaStats.loc[:, ['Age']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Games    43.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbaStats.loc[:, ['Games']].median()\n",
    "# nbaStats.loc[:, ['Games']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.loc[:, ['col1', 'col2']]\n",
    "# nbaStats.loc[:, ['Team', 'Player', 'Age', 'Games Played']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.3</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>142</td>\n",
       "      <td>[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>175</td>\n",
       "      <td>[u'Marlon Brando', u'Al Pacino', u'James Caan']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.1</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>200</td>\n",
       "      <td>[u'Al Pacino', u'Robert De Niro', u'Robert Duv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>152</td>\n",
       "      <td>[u'Christian Bale', u'Heath Ledger', u'Aaron E...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.9</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>154</td>\n",
       "      <td>[u'John Travolta', u'Uma Thurman', u'Samuel L....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   star_rating                     title content_rating   genre  duration  \\\n",
       "0          9.3  The Shawshank Redemption              R   Crime       142   \n",
       "1          9.2             The Godfather              R   Crime       175   \n",
       "2          9.1    The Godfather: Part II              R   Crime       200   \n",
       "3          9.0           The Dark Knight          PG-13  Action       152   \n",
       "4          8.9              Pulp Fiction              R   Crime       154   \n",
       "\n",
       "                                         actors_list  \n",
       "0  [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...  \n",
       "1    [u'Marlon Brando', u'Al Pacino', u'James Caan']  \n",
       "2  [u'Al Pacino', u'Robert De Niro', u'Robert Duv...  \n",
       "3  [u'Christian Bale', u'Heath Ledger', u'Aaron E...  \n",
       "4  [u'John Travolta', u'Uma Thurman', u'Samuel L....  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's use IMDB ratings, from https://www.youtube.com/watch?v=hSrDViyKWVk\n",
    "# https://raw.githubusercontent.com/justmarkham/pandas-videos/master/data/imdb_1000.csv\n",
    "movieRatings = pd.read_csv(\"https://raw.githubusercontent.com/justmarkham/pandas-videos/master/data/imdb_1000.csv\") \n",
    "movieRatings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>979.000000</td>\n",
       "      <td>979.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.889785</td>\n",
       "      <td>120.979571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.336069</td>\n",
       "      <td>26.218010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.600000</td>\n",
       "      <td>102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.800000</td>\n",
       "      <td>117.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.100000</td>\n",
       "      <td>134.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.300000</td>\n",
       "      <td>242.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       star_rating    duration\n",
       "count   979.000000  979.000000\n",
       "mean      7.889785  120.979571\n",
       "std       0.336069   26.218010\n",
       "min       7.400000   64.000000\n",
       "25%       7.600000  102.000000\n",
       "50%       7.800000  117.000000\n",
       "75%       8.100000  134.000000\n",
       "max       9.300000  242.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe will just give you the numeric descriptive statistics of all number columns\n",
    "movieRatings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'nbaStats' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-5e576a211447>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# now let's try .describe on the NBA data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnbaStats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;31m#NICE!\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'nbaStats' is not defined"
     ]
    }
   ],
   "source": [
    "# now let's try .describe on the NBA data\n",
    "nbaStats.describe()\n",
    "#NICE!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(979, 6)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape just gives us a tuple\n",
    "movieRatings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "star_rating       float64\n",
       "title              object\n",
       "content_rating     object\n",
       "genre              object\n",
       "duration            int64\n",
       "actors_list        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movieRatings.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(movieRatings)\n",
    "# https://www.youtube.com/watch?v=hSrDViyKWVk\n",
    "\n",
    "# so movieRatings is a dataframe, so has certain methods and attributes\n",
    "\n",
    "# head, describe etc..\n",
    "\n",
    "# attributes are those w/o parentheses, like shape and dtype\n",
    "\n",
    "# methods are 'Actions'\n",
    "\n",
    "# attributes are 'descriptions'\n",
    "\n",
    "# attribute = NO parens\n",
    "\n",
    "# more here on methods vs attributes:\n",
    "# https://stackoverflow.com/questions/46312470/difference-between-methods-and-attributes-in-python\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "star_rating       float64\n",
       "title              object\n",
       "content_rating     object\n",
       "genre              object\n",
       "duration            int64\n",
       "actors_list        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# so if you want to see what you want to perform just type the . and tab\n",
    "movieRatings.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>979</td>\n",
       "      <td>976</td>\n",
       "      <td>979</td>\n",
       "      <td>979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>975</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>True Grit</td>\n",
       "      <td>R</td>\n",
       "      <td>Drama</td>\n",
       "      <td>[u'Daniel Radcliffe', u'Emma Watson', u'Rupert...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>278</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            title content_rating  genre  \\\n",
       "count         979            976    979   \n",
       "unique        975             12     16   \n",
       "top     True Grit              R  Drama   \n",
       "freq            2            460    278   \n",
       "\n",
       "                                              actors_list  \n",
       "count                                                 979  \n",
       "unique                                                969  \n",
       "top     [u'Daniel Radcliffe', u'Emma Watson', u'Rupert...  \n",
       "freq                                                    6  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# so pd.read_csv is just a Pandas function, not a method on an object\n",
    "# a dataframe method will sometimes require arguments \n",
    "# include = will describe columns with type of 'object'\n",
    "movieRatings.describe(include=['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Conference</th>\n",
       "      <th>Division</th>\n",
       "      <th>Team</th>\n",
       "      <th>Player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>622</td>\n",
       "      <td>622</td>\n",
       "      <td>622</td>\n",
       "      <td>622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Eastern</td>\n",
       "      <td>Central</td>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>Wesley Matthews</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>316</td>\n",
       "      <td>110</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Conference Division               Team           Player\n",
       "count         622      622                622              622\n",
       "unique          2        6                 30              530\n",
       "top       Eastern  Central  Memphis Grizzlies  Wesley Matthews\n",
       "freq          316      110                 28                3"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbaStats.describe(include=['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hit shift + tab inside the parentheses to see the arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Games</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>39.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>36.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>38.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>33.417910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>45.226415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>45.673077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>41.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>41.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>41.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>45.205882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>47.805556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>47.409091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>39.884615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>42.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>57.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>38.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>47.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Games\n",
       "Age           \n",
       "19   39.727273\n",
       "20   55.000000\n",
       "21   36.060000\n",
       "22   38.280000\n",
       "23   33.417910\n",
       "24   45.226415\n",
       "25   45.673077\n",
       "26   41.909091\n",
       "27   41.727273\n",
       "28   41.020000\n",
       "29   45.205882\n",
       "30   47.805556\n",
       "31   47.409091\n",
       "32   39.884615\n",
       "33   43.000000\n",
       "34   42.454545\n",
       "35   57.333333\n",
       "36   38.250000\n",
       "37   47.750000\n",
       "38   26.000000\n",
       "40   51.000000\n",
       "42   76.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#working from http://localhost:8888/notebooks/Desktop/PythonProjects/Udemy_Master%20Data%20Analysis%20with%20Python%20-%20Intro%20to%20Pandas/UdemyIntroToPandas.ipynb\n",
    "# use group by, sex is a column and charges is a column in my 'college' dataset\n",
    "# ins.groupby('sex').agg({'charges' : 'mean'}).round(-3)\n",
    "# so just use a couple from the nba bit\n",
    "nbaStats.groupby('Age').agg({'Games' : 'mean'})\n",
    "# so this is showing mean age by games? not sure how useful that is\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of     Conference   Division              Team                Player  Age  Games  \\\n",
       "0      Eastern   Atlantic    Boston Celtics          Jayson Tatum   20     79   \n",
       "1      Eastern   Atlantic    Boston Celtics          Kyrie Irving   26     67   \n",
       "2      Eastern   Atlantic    Boston Celtics          Marcus Smart   24     80   \n",
       "3      Eastern   Atlantic    Boston Celtics         Marcus Morris   29     75   \n",
       "4      Eastern   Atlantic    Boston Celtics            Al Horford   32     68   \n",
       "..         ...        ...               ...                   ...  ...    ...   \n",
       "617    Western  Southwest  Dallas Mavericks           Salah Mejri   32     36   \n",
       "618    Western  Southwest  Dallas Mavericks          Courtney Lee   33     22   \n",
       "619    Western  Southwest  Dallas Mavericks           Daryl Macon   23      8   \n",
       "620    Western  Southwest  Dallas Mavericks  Kostas Antetokounmpo   21      2   \n",
       "621    Western  Southwest  Dallas Mavericks          Ray Spalding   21      1   \n",
       "\n",
       "     Games Started  Minutes Played  Field Goals  Field Goal Attempts  ...  \\\n",
       "0               79            2455          466                 1036  ...   \n",
       "1               67            2214          604                 1241  ...   \n",
       "2               60            2200          239                  567  ...   \n",
       "3               53            2091          377                  844  ...   \n",
       "4               68            1973          387                  723  ...   \n",
       "..             ...             ...          ...                  ...  ...   \n",
       "617              4             399           55                  112  ...   \n",
       "618              4             268           32                   82  ...   \n",
       "619              0              90           10                   27  ...   \n",
       "620              0              11            0                    3  ...   \n",
       "621              0               1            0                    0  ...   \n",
       "\n",
       "     Free Throw Percentage  Offensive Rebounds  Defensive Rebounds  \\\n",
       "0                    0.855                  70                 407   \n",
       "1                    0.873                  71                 264   \n",
       "2                    0.806                  57                 177   \n",
       "3                    0.844                  76                 382   \n",
       "4                    0.821                 120                 338   \n",
       "..                     ...                 ...                 ...   \n",
       "617                  0.625                  36                  95   \n",
       "618                  0.714                   3                  24   \n",
       "619                  0.571                   2                  10   \n",
       "620                  0.500                   0                   1   \n",
       "621                    NaN                   0                   0   \n",
       "\n",
       "     Total Rebounds  Assists  Steals  Blocks  Turnovers  Personal Fouls  \\\n",
       "0               477      168      84      57        122             168   \n",
       "1               335      464     103      34        172             167   \n",
       "2               234      321     143      28        123             201   \n",
       "3               458      109      43      25         92             181   \n",
       "4               458      283      59      86        102             126   \n",
       "..              ...      ...     ...     ...        ...             ...   \n",
       "617             131       35      10      26         21              55   \n",
       "618              27       21      13       1          9              19   \n",
       "619              12        7       1       0          6               8   \n",
       "620               1        0       2       0          1               0   \n",
       "621               0        0       0       0          0               0   \n",
       "\n",
       "     Points  \n",
       "0      1243  \n",
       "1      1596  \n",
       "2       708  \n",
       "3      1046  \n",
       "4       925  \n",
       "..      ...  \n",
       "617     141  \n",
       "618      80  \n",
       "619      29  \n",
       "620       2  \n",
       "621       0  \n",
       "\n",
       "[622 rows x 30 columns]>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbaStats.describe\n",
    "# nbaStats.index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Conference', 'Division', 'Team', 'Player', 'Age', 'Games',\n",
       "       'Games Started', 'Minutes Played', 'Field Goals', 'Field Goal Attempts',\n",
       "       'Field Goal Percentage', '3-Point Field Goals',\n",
       "       '3-Point Field Goal Attempts', '3-Point Field Goal Percentage',\n",
       "       '2-Point Field Goals', '2-Point Field Goal Attempts',\n",
       "       '2-Point Field Goal Percentage', 'Effective Field Goal Percentage',\n",
       "       'Free Throws', 'Free Throw Attempts', 'Free Throw Percentage',\n",
       "       'Offensive Rebounds', 'Defensive Rebounds', 'Total Rebounds', 'Assists',\n",
       "       'Steals', 'Blocks', 'Turnovers', 'Personal Fouls', 'Points'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbaStats.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    " # More Drill sfrom here: https://towardsdatascience.com/pandas-cheat-sheet-4c4eb6802a4b\n",
    "import pandas as pd\n",
    "# creating a dataframe with all the grades and subjects\n",
    "grades = pd.DataFrame(\n",
    "                 {'Math'     : [80, 89, 93,66, 84, 85,74,64],\n",
    "                  'Science'  : [94, 76, 88, 78, 88, 92, 60, 85],\n",
    "                  'English'  : [83, 76, 93, 96, 77, 85, 92, 60],\n",
    "                  'History'  : [96, 66, 76, 85, 78, 88, 69, 99]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Math</th>\n",
       "      <th>Science</th>\n",
       "      <th>English</th>\n",
       "      <th>History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
       "      <td>83</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>89</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>93</td>\n",
       "      <td>88</td>\n",
       "      <td>93</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66</td>\n",
       "      <td>78</td>\n",
       "      <td>96</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>85</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>74</td>\n",
       "      <td>60</td>\n",
       "      <td>92</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Math  Science  English  History\n",
       "0    80       94       83       96\n",
       "1    89       76       76       66\n",
       "2    93       88       93       76\n",
       "3    66       78       96       85\n",
       "4    84       88       77       78\n",
       "5    85       92       85       88\n",
       "6    74       60       92       69\n",
       "7    64       85       60       99"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Math', 'Science', 'English', 'History'], dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# just get the colmnns:\n",
    "grades.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
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       "        }</style><table id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Math</th>        <th class=\"col_heading level0 col1\" >Science</th>        <th class=\"col_heading level0 col2\" >English</th>        <th class=\"col_heading level0 col3\" >History</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row0_col0\" class=\"data row0 col0\" >80</td>\n",
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       "                        <th id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row2_col0\" class=\"data row2 col0\" >93</td>\n",
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       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row2_col2\" class=\"data row2 col2\" >93</td>\n",
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       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row3_col0\" class=\"data row3 col0\" >66</td>\n",
       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row3_col1\" class=\"data row3 col1\" >78</td>\n",
       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row3_col2\" class=\"data row3 col2\" >96</td>\n",
       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row3_col3\" class=\"data row3 col3\" >85</td>\n",
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       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row4_col1\" class=\"data row4 col1\" >88</td>\n",
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       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row5_col0\" class=\"data row5 col0\" >85</td>\n",
       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row5_col1\" class=\"data row5 col1\" >92</td>\n",
       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row5_col2\" class=\"data row5 col2\" >85</td>\n",
       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row5_col3\" class=\"data row5 col3\" >88</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row6_col0\" class=\"data row6 col0\" >74</td>\n",
       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row6_col1\" class=\"data row6 col1\" >60</td>\n",
       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row6_col2\" class=\"data row6 col2\" >92</td>\n",
       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row6_col3\" class=\"data row6 col3\" >69</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row7_col0\" class=\"data row7 col0\" >64</td>\n",
       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row7_col1\" class=\"data row7 col1\" >85</td>\n",
       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row7_col2\" class=\"data row7 col2\" >60</td>\n",
       "                        <td id=\"T_5e43f99e_7c2c_11ea_b8e1_7cb27dedc1c7row7_col3\" class=\"data row7 col3\" >99</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x26798130bc8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades.describe()\n",
    "grades.style.highlight_max(color='purple')\n",
    "grades.style.highlight_min(color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>Math</th>\n",
       "      <th>Science</th>\n",
       "      <th>English</th>\n",
       "      <th>History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>79.375000</td>\n",
       "      <td>82.625000</td>\n",
       "      <td>82.750000</td>\n",
       "      <td>82.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.528024</td>\n",
       "      <td>11.070392</td>\n",
       "      <td>11.756457</td>\n",
       "      <td>11.993302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10%</th>\n",
       "      <td>65.400000</td>\n",
       "      <td>71.200000</td>\n",
       "      <td>71.200000</td>\n",
       "      <td>68.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40%</th>\n",
       "      <td>78.800000</td>\n",
       "      <td>83.600000</td>\n",
       "      <td>81.800000</td>\n",
       "      <td>77.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>86.500000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>81.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60%</th>\n",
       "      <td>84.200000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>86.400000</td>\n",
       "      <td>85.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70%</th>\n",
       "      <td>84.900000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>91.300000</td>\n",
       "      <td>87.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80%</th>\n",
       "      <td>87.400000</td>\n",
       "      <td>90.400000</td>\n",
       "      <td>92.600000</td>\n",
       "      <td>92.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>90.200000</td>\n",
       "      <td>92.600000</td>\n",
       "      <td>93.900000</td>\n",
       "      <td>96.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Math    Science    English    History\n",
       "count   8.000000   8.000000   8.000000   8.000000\n",
       "mean   79.375000  82.625000  82.750000  82.125000\n",
       "std    10.528024  11.070392  11.756457  11.993302\n",
       "min    64.000000  60.000000  60.000000  66.000000\n",
       "10%    65.400000  71.200000  71.200000  68.100000\n",
       "40%    78.800000  83.600000  81.800000  77.600000\n",
       "50%    82.000000  86.500000  84.000000  81.500000\n",
       "60%    84.200000  88.000000  86.400000  85.600000\n",
       "70%    84.900000  88.000000  91.300000  87.700000\n",
       "80%    87.400000  90.400000  92.600000  92.800000\n",
       "90%    90.200000  92.600000  93.900000  96.900000\n",
       "max    93.000000  94.000000  96.000000  99.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the .describe() function we automatically get quantiles for 25, 50, and 75. \n",
    "# Create your own quantiles like this:\n",
    "grades.describe([0.1, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])\n",
    "# grades.describe([0.1, 0.9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# More on quantiles: \n",
    "    # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.quantile.html\n",
    "# Basically, quantiles are just lines that divide data into equally-sized groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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