{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Steph Curry Shot Analysis\n",
    "\n",
    "\n",
    "In this practice, we will answer the questions posed at the beginning of the Powerpoint lecture.  We will plot the Curry's cumulative field goal percentage  over each game. Then, on the same axis, plot his cumulative average distance from closest defender (for each shot) over each game.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Import pandas,numpy, matplotlib here here\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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>fgm</th>\n",
       "      <th>close_def_dist</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>0</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>1</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>0</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fgm  close_def_dist\n",
       "Date                           \n",
       "2014-10-29    0             3.3\n",
       "2014-10-29    1             4.2\n",
       "2014-10-29    0             4.1\n",
       "2014-10-29    1             5.1\n",
       "2014-10-29    1             4.4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read in the data here - make the date the index\n",
    "df_curry  = pd.read_csv(\"../Data_PreLab_and_Practice_Notes/Stephen_Curry_Shots.csv\",\\\n",
    "                        parse_dates=[\"Date\"], index_col = \"Date\")\n",
    "\n",
    "#Sort the data frame by the index using sort_index\n",
    "df_curry.sort_index(inplace = True, ascending = True)\n",
    "\n",
    "df_curry.columns =  df_curry.columns.str.lower()\n",
    "\n",
    "df_curry.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we need to find the number of shots taken and made by Curry in each game. The method cumsum() might be useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Made</th>\n",
       "      <th>Taken</th>\n",
       "      <th>FG_Percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-10-29</th>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>17.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.472222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>23.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.425926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>32.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-08</th>\n",
       "      <td>45.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.494505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Made  Taken  FG_Percentage\n",
       "Date                                  \n",
       "2014-10-29   7.0   17.0       0.411765\n",
       "2014-11-01  17.0   36.0       0.472222\n",
       "2014-11-02  23.0   54.0       0.425926\n",
       "2014-11-05  32.0   72.0       0.444444\n",
       "2014-11-08  45.0   91.0       0.494505"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Resample daily\n",
    "df_shots = df_curry.fgm.resample(rule=\"D\").\\\n",
    "    agg([\"sum\", \"count\"]).dropna()\n",
    "df_shots.columns = [\"Made\", \"Taken\"]\n",
    "\n",
    "#do cumulative sums\n",
    "df_shots_cumulative = df_shots.cumsum()\n",
    "\n",
    "#compute FG percentage\n",
    "df_shots_cumulative[\"FG_Percentage\"] = df_shots_cumulative.Made/\\\n",
    "                                    df_shots_cumulative.Taken\n",
    "df_shots_cumulative.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now do the same thing for the average distance from the closest defender.  You might find the expanding() method useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2014-10-29    3.800000\n",
       "2014-11-01    4.013158\n",
       "2014-11-02    3.877290\n",
       "2014-11-05    4.071857\n",
       "2014-11-08    4.098538\n",
       "Name: close_def_dist, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_def = df_curry.close_def_dist.resample(\"D\").mean().dropna()\n",
    "\n",
    "df_def_cumulative = df_def.expanding().mean()\n",
    "\n",
    "df_def_cumulative.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create the plot. You may find the ax.twinx() useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11d43c358>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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sa3Bre9WmW0g3ujXoRqf6nThy4wgrzq5g75W9pR6nRqVhZteZPNP2mVJZ9QDu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pAAAA\nlklEQVTN4p133iE2NpbHH3/c5rXXr19PcXExZrPZZuKUgoIC1q5dy4kTJ6itrcXb25vw8HBGjx5N\nv3792uzvF8IZScEWQgghnIAcwxZCCCGcgBRsIYQQwglIwRZCCCGcgBRsIYQQwglIwRZCCCGcgBRs\nIYQQwglIwRZCCCGcgBRsIYQQwglIwRZCCCGcgBRsIYQQwgn8f87Ld5ce1++ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d6160f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "df_shots_cumulative.FG_Percentage.plot(ax = ax,\\\n",
    "                                       color = \"b\",\\\n",
    "                                     label = \"FG %\")\n",
    "ax.legend(loc = 0)\n",
    "ax1 = ax.twinx()\n",
    "df_def_cumulative.plot(ax=ax1, color = \"g\", label = \"Dist. Def.\")\n",
    "\n",
    "ax1.legend(loc= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "navigate_num": "#000000",
    "navigate_text": "#333333",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700",
    "sidebar_border": "#EEEEEE",
    "wrapper_background": "#FFFFFF"
   },
   "moveMenuLeft": true,
   "nav_menu": {
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}