pandas_intro_hw.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://imgur.com/1ZcRyrc.png\" style=\"float: left; margin: 20px; height: 55px\">\n",
    "\n",
    "## Homework: Intro to Pandas\n",
    "\n",
    "_Author: Kevin Coyle (L.A.)_\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pandas: Intro Practice Problems\n",
    "\n",
    "In this homework, you're going to write code for a few problems. \n",
    "\n",
    "You'll be practicing these programming concepts we've covered in class:\n",
    "* Reading data sets into Pandas.\n",
    "* Filtering, manipulating, and sorting data sets.\n",
    "* Basic exploratory data analysis with Pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### #1. Import Pandas with an alias of `pd`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### #2. Read in the NBA players `csv` into a variable called `nba_df`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a data set of NBA players from 2015. The filename is `NBA_players_2015.csv`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "nba_df = pd.read_csv(\"/Users/laurenmengert/Downloads/hw-10wk-12d/NBA_players_2015.csv\")\n",
    "\n",
    "#I had to open my terminal and drag and drop the nba file to get the directory path to the csv \n",
    "#file. Is there an easier way to do this?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### #3. Look at the first five rows of the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>season_end</th>\n",
       "      <th>player</th>\n",
       "      <th>pos</th>\n",
       "      <th>age</th>\n",
       "      <th>bref_team_id</th>\n",
       "      <th>g</th>\n",
       "      <th>gs</th>\n",
       "      <th>mp</th>\n",
       "      <th>fg</th>\n",
       "      <th>fga</th>\n",
       "      <th>...</th>\n",
       "      <th>TOV%</th>\n",
       "      <th>USG%</th>\n",
       "      <th>OWS</th>\n",
       "      <th>DWS</th>\n",
       "      <th>WS</th>\n",
       "      <th>WS/48</th>\n",
       "      <th>OBPM</th>\n",
       "      <th>DBPM</th>\n",
       "      <th>BPM</th>\n",
       "      <th>VORP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>Quincy Acy</td>\n",
       "      <td>F</td>\n",
       "      <td>24</td>\n",
       "      <td>NYK</td>\n",
       "      <td>52</td>\n",
       "      <td>21</td>\n",
       "      <td>19.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.6</td>\n",
       "      <td>...</td>\n",
       "      <td>15.1</td>\n",
       "      <td>14.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>-0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>Jordan Adams</td>\n",
       "      <td>G</td>\n",
       "      <td>20</td>\n",
       "      <td>MEM</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>...</td>\n",
       "      <td>15.9</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.076</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>Steven Adams</td>\n",
       "      <td>C</td>\n",
       "      <td>21</td>\n",
       "      <td>OKC</td>\n",
       "      <td>51</td>\n",
       "      <td>50</td>\n",
       "      <td>24.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>...</td>\n",
       "      <td>19.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.109</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>Jeff Adrien</td>\n",
       "      <td>F</td>\n",
       "      <td>28</td>\n",
       "      <td>MIN</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>...</td>\n",
       "      <td>12.9</td>\n",
       "      <td>14.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.093</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>Arron Afflalo</td>\n",
       "      <td>G</td>\n",
       "      <td>29</td>\n",
       "      <td>TOT</td>\n",
       "      <td>60</td>\n",
       "      <td>54</td>\n",
       "      <td>32.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>...</td>\n",
       "      <td>10.9</td>\n",
       "      <td>19.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.051</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_end         player pos  age bref_team_id   g  gs    mp   fg   fga  \\\n",
       "0        2015     Quincy Acy   F   24          NYK  52  21  19.2  2.2   4.6   \n",
       "1        2015   Jordan Adams   G   20          MEM  18   0   7.3  1.0   2.1   \n",
       "2        2015   Steven Adams   C   21          OKC  51  50  24.2  3.0   5.5   \n",
       "3        2015    Jeff Adrien   F   28          MIN  17   0  12.6  1.1   2.6   \n",
       "4        2015  Arron Afflalo   G   29          TOT  60  54  32.5  5.0  11.8   \n",
       "\n",
       "   ...   TOV%  USG%  OWS  DWS   WS  WS/48  OBPM  DBPM  BPM  VORP  \n",
       "0  ...   15.1  14.7  0.6  0.5  1.0  0.050  -2.6  -0.7 -3.4  -0.3  \n",
       "1  ...   15.9  17.7  0.0  0.2  0.2  0.076  -2.3   1.8 -0.5   0.0  \n",
       "2  ...   19.2  14.8  1.0  1.8  2.8  0.109  -2.0   2.0 -0.1   0.6  \n",
       "3  ...   12.9  14.1  0.2  0.2  0.4  0.093  -2.6   0.8 -1.8   0.0  \n",
       "4  ...   10.9  19.6  1.4  0.7  2.1  0.051  -0.2  -1.4 -1.6   0.2  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### #4. Check out the shape of the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(478, 49)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### #5. Run some summary stats on the data set with the `describe()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>season_end</th>\n",
       "      <th>age</th>\n",
       "      <th>g</th>\n",
       "      <th>gs</th>\n",
       "      <th>mp</th>\n",
       "      <th>fg</th>\n",
       "      <th>fga</th>\n",
       "      <th>fg_</th>\n",
       "      <th>x3p</th>\n",
       "      <th>x3pa</th>\n",
       "      <th>...</th>\n",
       "      <th>TOV%</th>\n",
       "      <th>USG%</th>\n",
       "      <th>OWS</th>\n",
       "      <th>DWS</th>\n",
       "      <th>WS</th>\n",
       "      <th>WS/48</th>\n",
       "      <th>OBPM</th>\n",
       "      <th>DBPM</th>\n",
       "      <th>BPM</th>\n",
       "      <th>VORP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>478.0</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>478.000000</td>\n",
       "      <td>478.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>26.573222</td>\n",
       "      <td>42.104603</td>\n",
       "      <td>19.874477</td>\n",
       "      <td>20.045607</td>\n",
       "      <td>3.018201</td>\n",
       "      <td>6.847699</td>\n",
       "      <td>0.429230</td>\n",
       "      <td>0.630544</td>\n",
       "      <td>1.855649</td>\n",
       "      <td>...</td>\n",
       "      <td>13.424268</td>\n",
       "      <td>18.915272</td>\n",
       "      <td>1.055858</td>\n",
       "      <td>0.971339</td>\n",
       "      <td>2.027197</td>\n",
       "      <td>0.073575</td>\n",
       "      <td>-1.396862</td>\n",
       "      <td>-0.495816</td>\n",
       "      <td>-1.892678</td>\n",
       "      <td>0.485983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.219585</td>\n",
       "      <td>18.950602</td>\n",
       "      <td>22.170034</td>\n",
       "      <td>9.313441</td>\n",
       "      <td>2.052790</td>\n",
       "      <td>4.386828</td>\n",
       "      <td>0.111007</td>\n",
       "      <td>0.665808</td>\n",
       "      <td>1.790564</td>\n",
       "      <td>...</td>\n",
       "      <td>6.559989</td>\n",
       "      <td>5.493491</td>\n",
       "      <td>1.589667</td>\n",
       "      <td>0.876932</td>\n",
       "      <td>2.243138</td>\n",
       "      <td>0.133530</td>\n",
       "      <td>4.251216</td>\n",
       "      <td>2.299572</td>\n",
       "      <td>5.116001</td>\n",
       "      <td>1.013097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.100000</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>-0.800000</td>\n",
       "      <td>-1.059000</td>\n",
       "      <td>-36.800000</td>\n",
       "      <td>-13.500000</td>\n",
       "      <td>-50.300000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>1.425000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.391000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>14.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.040250</td>\n",
       "      <td>-2.800000</td>\n",
       "      <td>-1.600000</td>\n",
       "      <td>-3.300000</td>\n",
       "      <td>-0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>19.850000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>0.432000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>12.900000</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.082500</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-1.300000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>28.175000</td>\n",
       "      <td>4.275000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>0.481750</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.075000</td>\n",
       "      <td>...</td>\n",
       "      <td>15.900000</td>\n",
       "      <td>21.900000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.123000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>38.900000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>45.900000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.489000</td>\n",
       "      <td>28.300000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>6.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season_end         age           g          gs          mp          fg  \\\n",
       "count       478.0  478.000000  478.000000  478.000000  478.000000  478.000000   \n",
       "mean       2015.0   26.573222   42.104603   19.874477   20.045607    3.018201   \n",
       "std           0.0    4.219585   18.950602   22.170034    9.313441    2.052790   \n",
       "min        2015.0   19.000000    1.000000    0.000000    0.700000    0.000000   \n",
       "25%        2015.0   23.000000   27.000000    0.000000   12.600000    1.425000   \n",
       "50%        2015.0   26.000000   47.000000   10.500000   19.850000    2.700000   \n",
       "75%        2015.0   29.000000   59.000000   39.000000   28.175000    4.275000   \n",
       "max        2015.0   38.000000   66.000000   66.000000   38.900000    9.500000   \n",
       "\n",
       "              fga         fg_         x3p        x3pa     ...            TOV%  \\\n",
       "count  478.000000  478.000000  478.000000  478.000000     ...      478.000000   \n",
       "mean     6.847699    0.429230    0.630544    1.855649     ...       13.424268   \n",
       "std      4.386828    0.111007    0.665808    1.790564     ...        6.559989   \n",
       "min      0.000000    0.000000    0.000000    0.000000     ...        0.000000   \n",
       "25%      3.400000    0.391000    0.000000    0.100000     ...       10.000000   \n",
       "50%      5.900000    0.432000    0.450000    1.400000     ...       12.900000   \n",
       "75%      9.600000    0.481750    1.000000    3.075000     ...       15.900000   \n",
       "max     21.400000    1.000000    3.400000    8.000000     ...      100.000000   \n",
       "\n",
       "             USG%         OWS         DWS          WS       WS/48        OBPM  \\\n",
       "count  478.000000  478.000000  478.000000  478.000000  478.000000  478.000000   \n",
       "mean    18.915272    1.055858    0.971339    2.027197    0.073575   -1.396862   \n",
       "std      5.493491    1.589667    0.876932    2.243138    0.133530    4.251216   \n",
       "min      0.000000   -2.100000   -0.100000   -0.800000   -1.059000  -36.800000   \n",
       "25%     14.800000    0.000000    0.200000    0.300000    0.040250   -2.800000   \n",
       "50%     18.400000    0.600000    0.800000    1.400000    0.082500   -1.000000   \n",
       "75%     21.900000    1.600000    1.500000    3.000000    0.123000    0.500000   \n",
       "max     45.900000    9.500000    4.300000   13.000000    1.489000   28.300000   \n",
       "\n",
       "             DBPM         BPM        VORP  \n",
       "count  478.000000  478.000000  478.000000  \n",
       "mean    -0.495816   -1.892678    0.485983  \n",
       "std      2.299572    5.116001    1.013097  \n",
       "min    -13.500000  -50.300000   -1.000000  \n",
       "25%     -1.600000   -3.300000   -0.100000  \n",
       "50%     -0.500000   -1.300000    0.100000  \n",
       "75%      0.875000    0.500000    0.700000  \n",
       "max      6.500000   15.100000    6.200000  \n",
       "\n",
       "[8 rows x 46 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### #6. Sort the data set in on the `players` column in alphabetical order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "359           A.J. Price\n",
       "60          Aaron Brooks\n",
       "166         Aaron Gordon\n",
       "348        Adreian Payne\n",
       "212           Al Horford\n",
       "225         Al Jefferson\n",
       "11       Al-Farouq Aminu\n",
       "14         Alan Anderson\n",
       "68            Alec Burks\n",
       "252            Alex Kirk\n",
       "267             Alex Len\n",
       "394         Alexey Shved\n",
       "5          Alexis Ajinca\n",
       "99          Allen Crabbe\n",
       "160           Alonzo Gee\n",
       "413    Amar'e Stoudemire\n",
       "232         Amir Johnson\n",
       "439     Anderson Varejao\n",
       "112        Andre Dawkins\n",
       "127       Andre Drummond\n",
       "217       Andre Iguodala\n",
       "305         Andre Miller\n",
       "372       Andre Roberson\n",
       "30       Andrea Bargnani\n",
       "251     Andrei Kirilenko\n",
       "52          Andrew Bogut\n",
       "329     Andrew Nicholson\n",
       "458       Andrew Wiggins\n",
       "44       Anthony Bennett\n",
       "109        Anthony Davis\n",
       "             ...        \n",
       "452          Travis Wear\n",
       "21          Trevor Ariza\n",
       "54         Trevor Booker\n",
       "67            Trey Burke\n",
       "429     Tristan Thompson\n",
       "106         Troy Daniels\n",
       "262            Ty Lawson\n",
       "136          Tyler Ennis\n",
       "185     Tyler Hansbrough\n",
       "237        Tyler Johnson\n",
       "477         Tyler Zeller\n",
       "139         Tyreke Evans\n",
       "425         Tyrus Thomas\n",
       "83        Tyson Chandler\n",
       "193        Udonis Haslem\n",
       "89         Victor Claver\n",
       "338       Victor Oladipo\n",
       "79          Vince Carter\n",
       "133      Wayne Ellington\n",
       "238       Wesley Johnson\n",
       "288      Wesley Matthews\n",
       "34           Will Barton\n",
       "85           Will Cherry\n",
       "179         Willie Green\n",
       "84       Wilson Chandler\n",
       "199         Xavier Henry\n",
       "261          Zach LaVine\n",
       "366        Zach Randolph\n",
       "340        Zaza Pachulia\n",
       "125         Zoran Dragic\n",
       "Length: 478, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(nba_df['player'].unique()).sort_values(ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### #7. Filter the data set. Create three sub DataFrames from the `position` column for `G`, `F`, and `C`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### #8.  Run `describe()` on these new DataFrames. Compare the mean field goals (the `fg` column) between positions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The end!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Great job!"
   ]
  }
 ],
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