NBA_Trends_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Codecademy [NBA Trends Project](https://www.codecademy.com/projects/practice/nba-trends)\n",
    "\n",
    "*Analyze National Basketball Association (NBA) data to look at associations between teams, win-rates, playoff appearances, and more.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, you'll analyze data from the NBA (National Basketball Association) and explore possible associations. \n",
    "\n",
    "This data was originally sourced from 538's Analysis of the [Complete History Of The NBA](http://fivethirtyeight.com/interactives/the-complete-history-of-every-nba-team-by-elo) and contains the original, unmodified data from [Basketball Reference](http://www.basketball-reference.com/) as well as several additional variables 538 added to perform their own analysis. \n",
    "\n",
    "You can read more about the data and how it's being used by 538 [here](https://github.com/fivethirtyeight/data/tree/master/nba-elo). For this project we've limited the data to just 5 teams and 10 columns (plus one constructed column, `point_diff`, the difference between `pts` and `opp_pts`).\n",
    "\n",
    "You will create several charts and tables in this project, so you'll need to use `plt.clf()` between plots in your code so that the plots don't layer on top of one another.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import pearsonr, chi2_contingency\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 665,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to make the output look nicer\n",
    "np.set_printoptions(suppress=True, precision = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 666,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 24359 entries, 0 to 24358\n",
      "Data columns (total 11 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   game_id        24359 non-null  object \n",
      " 1   year_id        24359 non-null  int64  \n",
      " 2   fran_id        24359 non-null  object \n",
      " 3   opp_fran       24359 non-null  object \n",
      " 4   game_location  24359 non-null  object \n",
      " 5   is_playoffs    24359 non-null  int64  \n",
      " 6   pts            24359 non-null  int64  \n",
      " 7   opp_pts        24359 non-null  int64  \n",
      " 8   game_result    24359 non-null  object \n",
      " 9   forecast       24359 non-null  float64\n",
      " 10  point_diff     24359 non-null  int64  \n",
      "dtypes: float64(1), int64(5), object(5)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "nba = pd.read_csv('nba_games.csv')\n",
    "nba.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 667,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subset Data to 2010 Season, 2014 Season\n",
    "nba_2010 = nba[nba.year_id == 2010]\n",
    "nba_2014 = nba[nba.year_id == 2014]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 1\n",
    "The data has been subset for you into two smaller datasets: games from 2010 (named nba_2010) and games from 2014 (named nba_2014). To start, let’s focus on the 2010 data.\n",
    "\n",
    "Suppose you want to compare the knicks to the nets with respect to points earned per game. Using the pts column from the nba_2010 DataFrame, create two series named knicks_pts (fran_id = \"Knicks\") and nets_pts(fran_id = \"Nets\") that represent the points each team has scored in their games.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 669,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the 'pts' column from the dataset.\n",
    "nets_pts_per_game_2010 = nba_2010[nba_2010.fran_id=='Nets']['pts']\n",
    "knicks_pts_per_game_2010 = nba_2010[nba_2010.fran_id=='Knicks']['pts']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2\n",
    "\n",
    "Calculate the difference between the two teams’ average points scored and save the result as diff_means_2010. Based on this value, do you think fran_id and pts are associated? Why or why not?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 671,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The difference in means netween the nets and knicks is 9.73\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Getting the average pts\n",
    "nets_avg_pts = np.around(np.mean(nets_pts_per_game_2010),2)\n",
    "knicks_avg_pts = np.around(np.mean(knicks_pts_per_game_2010),2)\n",
    "diff_means_2010 = np.around(np.absolute(nets_avg_pts - knicks_avg_pts),2)\n",
    "print(f'The difference in means netween the nets and knicks is {diff_means_2010}')\n",
    "\n",
    "# Checking for association by way of box plot\n",
    "nba_2010_nets_knicks = nba_2010[(nba_2010['fran_id']=='Nets') | (nba_2010['fran_id']=='Knicks')]\n",
    "\n",
    "#Creating a basic box plot.\n",
    "ax = sns.catplot(x='fran_id',y='pts',data=nba_2010_nets_knicks,kind='box')\n",
    "plt.xlabel(\"Franchise\")\n",
    "plt.ylabel(\"Points Scored\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Overlap in the side by side box plot suggest less association between the variables being compared. \n",
    "It seems that the lower quartile of the knicks and the upper quartile of the Nets seem to overlap*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3\n",
    "Rather than comparing means, it’s useful look at the full distribution of values to understand whether a difference in means is meaningful. Create a set of overlapping histograms that can be used to compare the points scored for the Knicks compared to the Nets. Use the series you created in the previous step (1) and the code below to create the plot. Do the distributions appear to be the same?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 674,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qlUKhgIWFBRYuXIjLly/j4MGDSEtLQ3h4OIyMjCTb19fXh76+Pvbt24eCggLJmNLSUri7uyMhIQHbt2/H5cuXsXz5cvEWYnJyMkaNGoXRo0fj4sWLCAwMxMKFC7FlyxaldlauXAk7OzskJydj4cKFL/25lM3ZrVu38Msvv+A///kPwsLCxM0f6xo+k0RE1VbZ51tGZobU8kjK19uqVfUqDltTswOhKnn//ffRo0cPLF68GJGRkSrnV65ciY8//lh83qhDhw5Yu3Yt+vXrh/DwcLRq1QoaGhpo1qwZTE1NxXoZGRnw9PSEvf3zZ6Wsra0rPabS0lIcOXIEhw8fhq+vL65fv46ffvoJp06dgrOzMwDghx9+gIWFBfbt24ePPvqo3Ha2bNmCZs2aAQDGjRuHY8eO4auvvoKBgQG0tbXF1au/j/vNN9+Eo6MjgOerOOXR1NTEli1b4O3tjYiICPTs2RP9+vXD6NGj0a1bNwDA0aNHcebMGaSlpaFjx44qc7F69WoMHDgQCxcuBAB07NgRly9fxsqVKzFx4kQxbsCAAZg9e7b4efz48RX+XDIyMnDw4EH89ttv6N27NwAgMjIStra2L51/deBKEhER1UkrVqxAVFQULl++rHIuOTkZW7ZsEVdN9PX14ebmhtLSUqSnp5fb5owZM7Bs2TL06dMHixcvxoULF146jrCwMOjr60Mul2P48OH45JNPsHjxYqSlpUFTU1P8ZQ8AhoaG6NSpE9LS0sptz9LSUkyQgOevznjZSspnn32GnTt3okePHpg7dy4SEhIqjPf09MS9e/fw008/wc3NDXFxcejZs6e4EpSamoo33nhDTJBelJaWhj59+iiV9enTB9evX0dJSYlYVpa0lXnZz6Vszv5er3PnznX2G4BMkoiIqE7q27cv3NzcEBAQoHKutLQU//znP5Gamioe58+fx/Xr19G+ffty25wyZQpu3ryJcePG4eLFi3B0dMS6desqHMfYsWORmpqKGzdu4K+//kJkZCSaNm1a7jftBEGocDNDLS0tpc8ymQylpaUVjsHd3R23b9+Gr68v7t27h4EDByqt4EiRy+UYPHgwFi1ahISEBEycOBGLFy8G8L9Xd5RH6hqkrldPT0/p88t+LmVt1JeNTpkkERFRnbV8+XL8/PPPKisnPXv2xKVLl2BjY6NyaGtrA3j+lf2/r3qUsbCwgI+PD/bs2YMvvvgCmzZtqnAMBgYGsLGxgYWFhdLX/rt06YLi4mKcPn1aLMvNzcW1a9de6fZReeM2NjbGxIkTsX37doSGhmLjxo1VardLly7iA9TdunVDZmYmrl27Vm7syZMnlcoSEhLQsWPHCrc+eNnPxdbWFsXFxUhKShLrXL16FQ8fPqzStbwuTJKIiKjOsre3x9ixY1VWe+bNm4fExERMmzYNqamp4vNBn3/+uRhjaWmJX3/9FXfv3kVOTg4AwNfXF4cPH0Z6ejrOnTuHX375pdoJTYcOHTBixAh4e3vj5MmTOH/+PD755BO0adMGI0aMqPY1W1pa4vTp07h16xZycnJQWlqKRYsWYf/+/fjjjz9w6dIlHDhwoNxx5+bmYsCAAdi+fTsuXLiA9PR0/Pvf/0ZISIg4rn79+qFv377w9PREbGws0tPTcfDgQRw6dAgA8MUXX+DYsWNYunQprl27hqioKKxfv/6lq1cv+7l06tQJQ4YMgbe3N06fPo3k5GRMmTLlpStb6sIkiYiI6rSlS5eq3Orp1q0b4uPjcf36dbi4uODNN9/EwoULYWZmJsYEBQXh1q1baN++PYyNjQEAJSUlmDZtGmxtbTFkyBB06tQJYWFh1R7b5s2b4eDggKFDh8LJyQmCICAmJkblllpVzJ49GxoaGujSpQuMjY2RkZEBbW1t+Pv7o1u3bujbty80NDSwc+dOyfr6+vro3bs3vvnmG/Tt2xd2dnZYuHAhvL29sX79ejFu9+7d6NWrF8aMGYMuXbpg7ty54gpWz549sWvXLuzcuRN2dnZYtGgRgoKClB7allKZn8vmzZthYWGBfv364YMPPsA//vEPtewlVRkyoSrbl5IoPz8fBgYGyMvLQ/PmzdU9HCK14Lfb6qZnz54hPT0dVlZWkMvl6h4O0WtX0d+Bqvz+VvtKUlhYmHgRDg4OOHHiRIXx8fHxcHBwgFwuh7W1NSIiIsqN3blzJ2QyGUaOHPnK/RIREVHjotYkKTo6Gr6+vliwYAFSUlLg4uICd3d3ZGRkSManp6fDw8MDLi4uSElJQUBAAGbMmIHdu3erxN6+fRuzZ8+Gi4vLK/dLREREjY9ak6TVq1dj8uTJmDJlCmxtbREaGgoLCwuEh4dLxkdERKBt27YIDQ2Fra0tpkyZgkmTJqm8Q6ekpARjx47FkiVLJDcKq2q/RERE1PioLUkqLCxEcnIyXF1dlcpdXV3L3SQrMTFRJd7NzQ1JSUlKW8AHBQXB2NgYkydPrpF+AaCgoAD5+flKBxERETVcakuScnJyUFJSAhMTE6VyExMTZGVlSdbJysqSjC8uLha/3nnq1ClERkaWu+9FdfoFgODgYBgYGIiHhYXFS6+RiIiI6i+1P7gttaNnRTtxlrcDqEwmw6NHj/DJJ59g06ZN5b74r7r9+vv7Iy8vTzzu3LlTYftERERUv6ntBbdGRkbQ0NBQWb3Jzs5WWeUpY2pqKhmvqakJQ0NDXLp0Cbdu3cKwYcPE82VbvWtqauLq1avijqlV6RcAdHR0oKOjU6VrJCIiovpLbStJ2tracHBwQGxsrFJ5bGys+DblFzk5OanEHzlyBI6OjtDS0kLnzp1x8eJFpXfGDB8+HO+++y5SU1NhYWFRrX6JiIio8VHbShIA+Pn5Ydy4cXB0dISTkxM2btyIjIwM+Pj4AHh+i+vu3bvYunUrAMDHxwfr16+Hn58fvL29kZiYiMjISOzYsQPA85f52dnZKfVR9mbhv5e/rF8iIiIitT6T5OXlhdDQUAQFBaFHjx749ddfERMTg3bt2gEAFAqF0t5FVlZWiImJQVxcHHr06IGlS5di7dq18PT0rNF+iYiofrO0tERoaGilYrds2SL+g7ouiIuLg0wmq7Mvfa0r+vfvD19f31rtQ60rSQAwdepUTJ06VfLcli1bVMr69euHc+fOVbp9qTZe1i9RXVDZV34QVcnPM19vf1V8vcvEiRPx8OFD7Nu3Tyz7z3/+g08++QRBQUGYO3dupdo5e/Ys9PT0qtR3TZk4cSKioqIAPH8e1sLCAh988AGWLFlSqTE5OztDoVDAwMCgSn2+OG9V9eTJEwQFBeHf//437t27h2bNmqFr166YPXs2hg4dWu126zO1J0lERETl+e677zBt2jRs2LABU6ZMqXS9shfaqsuQIUOwefNmFBUV4cSJE5gyZQqePHlSqU2LtbW1YWpq+hpGqczHxwdnzpzB+vXr0aVLF+Tm5iIhIQG5ubm11mdhYSG0tbVrrf1XpfYtAIiIiKSEhIRg+vTp+PHHH5USpIkTJ2LkyJFYtWoVzMzMYGhoiGnTpiltKvzi7baHDx/iH//4B0xMTMTnVw8cOCDZb25uLt566y0MHz4cz549w4MHDzB27FgYGxtDV1cXHTp0wObNmyscu46ODkxNTWFhYYGPP/4YY8eOFVd5CgoKMGPGDLRu3RpyuRzvvPMOzp49K9Z98XZb2e3Aw4cPw9bWFvr6+hgyZAgUCgUAIDAwEFFRUdi/fz9kMhlkMhni4uJQWFiI6dOnw8zMDHK5HJaWlggODi53zD///DMCAgLg4eEBS0tLODg44PPPP8eECRPEmIKCAsydOxcWFhbQ0dFBhw4dEBkZKZ6Pj4/HW2+9BR0dHZiZmWH+/PkoLi4Wz/fv3x/Tp0+Hn58fjIyMMHjwYADA5cuX4eHhAX19fZiYmGDcuHHi/ofA81Wu8ePHQ19fH2ZmZvjXv/5V4fzXFCZJRERU58yfPx9Lly7FgQMHJJ87PX78OG7cuIHjx48jKioKW7ZsKffxitLSUri7uyMhIQHbt2/H5cuXsXz5cmhoaKjEZmZmwsXFBZ07d8aePXsgl8uxcOFCXL58GQcPHkRaWhrCw8Nfuhffi3R1dcUkbu7cudi9ezeioqJw7tw52NjYwM3NDffv3y+3/tOnT7Fq1Sps27YNv/76KzIyMjB79mwAwOzZszFq1CgxcVIoFHB2dsbatWvx008/YdeuXbh69Sq2b98OS0vLcvswNTVFTEwMHj16VG7M+PHjsXPnTqxduxZpaWmIiIiAvr4+AODu3bvw8PBAr169cP78eYSHhyMyMhLLli1TaiMqKgqampo4deoUvv32WygUCvTr1w89evRAUlISDh06hD///BOjRo0S68yZMwfHjx/H3r17ceTIEcTFxSE5Ofml8/6qeLuNiIjqlIMHD2L//v04duwYBgwYIBnTsmVLrF+/HhoaGujcuTPee+89HDt2DN7e3iqxR48exZkzZ5CWloaOHTsCgOR7Pa9du4bBgwdjxIgRWLNmjbjBcEZGBt588004OjoCQIWJhpQzZ87gxx9/xMCBA8Vbblu2bIG7uzsAYNOmTYiNjUVkZCTmzJkj2UZRUREiIiLQvn17AMD06dMRFBQEANDX14euri4KCgqUbtNlZGSgQ4cOeOeddyCTyV765aSNGzdi7NixMDQ0RPfu3fHOO+/gww8/RJ8+fcT52bVrF2JjYzFo0CAAyvMYFhYGCwsLrF+/HjKZDJ07d8a9e/cwb948LFq0CE2aPF+XsbGxQUhIiFhv0aJF6NmzJ77++mux7Pvvv4eFhQWuXbsGc3NzREZGYuvWreLKU1RUFN54441KzP6r4UoSERHVKd26dYOlpSUWLVpU7qpG165dlVaCzMzMkJ2dLRmbmpqKN954Q0yQpPz111945513MHLkSKxdu1bpDQyfffYZdu7ciR49emDu3LkVvuezzIEDB6Cvrw+5XA4nJyf07dsX69atw40bN1BUVCQmHgCgpaWFt956C2lpaeW217RpUzFBetn1lpk4cSJSU1PRqVMnzJgxA0eOHKkwvm/fvrh58yaOHTsGT09PXLp0CS4uLli6dCmA5/OooaGBfv36SdZPS0uDk5OT0tz16dMHjx8/RmZmplhWlmyWSU5OxvHjx6Gvry8enTt3BgDcuHEDN27cQGFhIZycnMQ6rVq1QqdOnSq8nprAJImIiOqUNm3aID4+HgqFAkOGDJFMlLS0tJQ+y2Qy8Q0LL9LV1X1pnzo6Ohg0aBD++9//Kv1CBwB3d3fcvn0bvr6+uHfvHgYOHCje6ipP2SbGV69exbNnz7Bnzx60bt1a6VVaf/eyV2NJXW9ZW+Xp2bMn0tPTsXTpUvz1118YNWoUPvzwwwrraGlpwcXFBfPnz8eRI0cQFBSEpUuXorCw8KXzKHUNUtf74jf8SktLMWzYMKWNoFNTU3H9+nX07dv3pddZm5gkERFRndO2bVvEx8cjOzsbrq6uyM/Pr3Zb3bp1Q2ZmJq5du1ZuTJMmTbBt2zY4ODhgwIABuHfvntJ5Y2NjTJw4Edu3b0doaCg2btxYYZ96enqwsbFBu3btlBIcGxsbaGtr4+TJk2JZUVERkpKSYGtrW80rfP6NuJKSEpXy5s2bw8vLC5s2bUJ0dDR2795d4bNPL+rSpQuKi4vx7Nkz2Nvbo7S0FPHx8eXGJiQkKCU1CQkJaNasGdq0aVNuHz179sSlS5dgaWkJGxsbpaNsHrW0tPDbb7+JdR48eFDhz7OmMEkiIqI66Y033kBcXBxyc3Ph6uqKvLy8arXTr18/9O3bF56enoiNjUV6ejoOHjyIQ4cOKcVpaGjghx9+QPfu3TFgwADxHZ+LFi3C/v378ccff+DSpUs4cOBAtRMaPT09fPbZZ5gzZw4OHTqEy5cvw9vbG0+fPsXkyZOr1Sbw/DmpCxcu4OrVq8jJyUFRURG++eYb7Ny5E1euXMG1a9fw73//G6ampuVunNm/f398++23SE5Oxq1btxATE4OAgAC8++67aN68OSwtLTFhwgRMmjQJ+/btQ3p6OuLi4rBr1y4Az/cfvHPnDj7//HNcuXIF+/fvx+LFi+Hn5yc+jyRl2rRpuH//PsaMGYMzZ87g5s2bOHLkCCZNmoSSkhLo6+tj8uTJmDNnDo4dO4bff/8dEydOrLDNmsIkiYiI6qyyW28PHz7E4MGDq70L9e7du9GrVy+MGTMGXbp0wdy5cyVXXjQ1NbFjxw507doVAwYMQHZ2NrS1teHv749u3bqhb9++0NDQwM6dO6t9TcuXL4enpyfGjRuHnj174o8//sDhw4fRsmXLarfp7e2NTp06wdHREcbGxjh16hT09fWxYsUKODo6olevXmLiU15y4ebmhqioKLi6usLW1haff/453NzcxCQIAMLDw/Hhhx9i6tSp6Ny5M7y9vfHkyRMAz39WMTExOHPmDLp37w4fHx9MnjwZX375ZYVjNzc3x6lTp1BSUgI3NzfY2dlh5syZMDAwEMe6cuVK9O3bF8OHD8egQYPwzjvvwMHBodrzVVkyQZ03++qx/Px8GBgYIC8vD82bN1f3cKgBakg7bo/MDHl5UC3pbdWqehWruFN0XfLs2TOkp6fDysoKcrlc3cMheu0q+jtQld/fXEkiIiIiksAkiYiIiEgCkyQiIiIiCUySiIiIiCQwSSIiaqD4vRxqrGrqzz6TJCKiBqZs88KnT5+qeSRE6lH2Z//Fncqrii+4JSJqYDQ0NNCiRQvx3V5Nmzat8JUXRA2FIAh4+vQpsrOz0aJFC6X3+1UHkyQiogao7G3wL3sJKlFD1KJFC/HvwKtgkkRE1ADJZDKYmZmhdevWKCoqUvdwiF4bLS2tV15BKsMkiYioAdPQ0KixXxhEjQ0f3CYiIiKSwCSJiIiISAKTJCIiIiIJTJKIiIiIJDBJIiIiIpLAJImIiIhIApMkIiIiIglMkoiIiIgkMEkiIiIiksAkiYiIiEgCkyQiIiIiCWpPksLCwmBlZQW5XA4HBwecOHGiwvj4+Hg4ODhALpfD2toaERERSuf37NkDR0dHtGjRAnp6eujRowe2bdumFBMYGAiZTKZ01MTbgomIiKjhUGuSFB0dDV9fXyxYsAApKSlwcXGBu7s7MjIyJOPT09Ph4eEBFxcXpKSkICAgADNmzMDu3bvFmFatWmHBggVITEzEhQsX8Omnn+LTTz/F4cOHldrq2rUrFAqFeFy8eLFWr5WIiIjqF011dr569WpMnjwZU6ZMAQCEhobi8OHDCA8PR3BwsEp8REQE2rZti9DQUACAra0tkpKSsGrVKnh6egIA+vfvr1Rn5syZiIqKwsmTJ+Hm5iaWa2pqcvWIiIiIyqW2laTCwkIkJyfD1dVVqdzV1RUJCQmSdRITE1Xi3dzckJSUhKKiIpV4QRBw7NgxXL16FX379lU6d/36dZibm8PKygqjR4/GzZs3X/GKiIiIqCFR20pSTk4OSkpKYGJiolRuYmKCrKwsyTpZWVmS8cXFxcjJyYGZmRkAIC8vD23atEFBQQE0NDQQFhaGwYMHi3V69+6NrVu3omPHjvjzzz+xbNkyODs749KlSzA0NJTsu6CgAAUFBeLn/Pz8al03ERER1Q9qvd0GADKZTOmzIAgqZS+Lf7G8WbNmSE1NxePHj3Hs2DH4+fnB2tpavBXn7u4uxtrb28PJyQnt27dHVFQU/Pz8JPsNDg7GkiVLqnRtREREVH+pLUkyMjKChoaGyqpRdna2ympRGVNTU8l4TU1NpRWgJk2awMbGBgDQo0cPpKWlITg4WOV5pTJ6enqwt7fH9evXyx2vv7+/UgKVn58PCwuLCq+RiIiI6i+1PZOkra0NBwcHxMbGKpXHxsbC2dlZso6Tk5NK/JEjR+Do6AgtLa1y+xIEQelW2YsKCgqQlpYm3q6ToqOjg+bNmysdRERE1HCp9Xabn58fxo0bB0dHRzg5OWHjxo3IyMiAj48PgOerN3fv3sXWrVsBAD4+Pli/fj38/Pzg7e2NxMREREZGYseOHWKbwcHBcHR0RPv27VFYWIiYmBhs3boV4eHhYszs2bMxbNgwtG3bFtnZ2Vi2bBny8/MxYcKE1zsBREREVGepNUny8vJCbm4ugoKCoFAoYGdnh5iYGLRr1w4AoFAolPZMsrKyQkxMDGbNmoUNGzbA3Nwca9euFb/+DwBPnjzB1KlTkZmZCV1dXXTu3Bnbt2+Hl5eXGJOZmYkxY8YgJycHxsbGePvtt/Hbb7+J/RIRERHJhLInn6lK8vPzYWBggLy8PN56o1rhv6fhbHA6MjNEbX33tmpVvYrD1tTsQIioTqjK72+1v5aEiIiIqC5ikkREREQkgUkSERERkQQmSUREREQSmCQRERERSWCSRERERCSBSRIRERGRBCZJRERERBKYJBERERFJYJJEREREJIFJEhEREZEEJklEREREEpgkEREREUlgkkREREQkgUkSERERkQQmSUREREQSmCQRERERSWCSRERERCSBSRIRERGRBCZJRERERBKYJBERERFJYJJEREREJIFJEhEREZEEJklEREREEpgkEREREUlgkkREREQkgUkSERERkQQmSUREREQSmCQRERERSWCSRERERCSBSRIRERGRBCZJRERERBLUniSFhYXBysoKcrkcDg4OOHHiRIXx8fHxcHBwgFwuh7W1NSIiIpTO79mzB46OjmjRogX09PTQo0cPbNu27ZX7JSIiosZFrUlSdHQ0fH19sWDBAqSkpMDFxQXu7u7IyMiQjE9PT4eHhwdcXFyQkpKCgIAAzJgxA7t37xZjWrVqhQULFiAxMREXLlzAp59+ik8//RSHDx+udr9ERETU+MgEQRDU1Xnv3r3Rs2dPhIeHi2W2trYYOXIkgoODVeLnzZuHn376CWlpaWKZj48Pzp8/j8TExHL76dmzJ9577z0sXbq0Wv1Kyc/Ph4GBAfLy8tC8efNK1SGqCv89F9U9hBozMjNEbX33tmpVvYrD1tTsQIioTqjK72+1rSQVFhYiOTkZrq6uSuWurq5ISEiQrJOYmKgS7+bmhqSkJBQVFanEC4KAY8eO4erVq+jbt2+1+wWAgoIC5OfnKx1ERETUcKktScrJyUFJSQlMTEyUyk1MTJCVlSVZJysrSzK+uLgYOTk5YlleXh709fWhra2N9957D+vWrcPgwYOr3S8ABAcHw8DAQDwsLCyqdL1ERERUv6j9wW2ZTKb0WRAElbKXxb9Y3qxZM6SmpuLs2bP46quv4Ofnh7i4uFfq19/fH3l5eeJx586dCq+LiIiI6jdNdXVsZGQEDQ0NldWb7OxslVWeMqamppLxmpqaMDQ0FMuaNGkCGxsbAECPHj2QlpaG4OBg9O/fv1r9AoCOjg50dHSqdI1ERERUf6ltJUlbWxsODg6IjY1VKo+NjYWzs7NkHScnJ5X4I0eOwNHREVpaWuX2JQgCCgoKqt0vERERNT5qW0kCAD8/P4wbNw6Ojo5wcnLCxo0bkZGRAR8fHwDPb3HdvXsXW7duBfD8m2zr16+Hn58fvL29kZiYiMjISOzYsUNsMzg4GI6Ojmjfvj0KCwsRExODrVu3Kn2T7WX9EhEREak1SfLy8kJubi6CgoKgUChgZ2eHmJgYtGvXDgCgUCiU9i6ysrJCTEwMZs2ahQ0bNsDc3Bxr166Fp6enGPPkyRNMnToVmZmZ0NXVRefOnbF9+3Z4eXlVul8iIiIite6TVJ9xnySqbdwnqWZwnyQi+rt6sU8SERERUV3GJImIiIhIApMkIiIiIglMkoiIiIgkMEkiIiIiksAkiYiIiEgCkyQiIiIiCUySiIiIiCQwSSIiIiKSoNbXkhBR1alz92oiosaEK0lEREREEpgkEREREUlgkkREREQkgUkSERERkQQmSUREREQSmCQRERERSWCSRERERCSBSRIRERGRhBpJkh4+fFgTzRARERHVGVVOklasWIHo6Gjx86hRo2BoaIg2bdrg/PnzNTo4IiIiInWpcpL07bffwsLCAgAQGxuL2NhYHDx4EO7u7pgzZ06ND5CIiIhIHar87jaFQiEmSQcOHMCoUaPg6uoKS0tL9O7du8YHSERERKQOVV5JatmyJe7cuQMAOHToEAYNGgQAEAQBJSUlNTs6IiIiIjWp8krSBx98gI8//hgdOnRAbm4u3N3dAQCpqamwsbGp8QESERERqUOVk6RvvvkGlpaWuHPnDkJCQqCvrw/g+W24qVOn1vgAiYiIiNShykmSlpYWZs+erVLu6+tbE+MhIiIiqhOqtU/Stm3b8M4778Dc3By3b98GAISGhmL//v01OjgiIiIidalykhQeHg4/Pz+4u7vj4cOH4sPaLVq0QGhoaE2Pj4iIiEgtqpwkrVu3Dps2bcKCBQugoaEhljs6OuLixYs1OjgiIiIidalykpSeno4333xTpVxHRwdPnjypkUERERERqVuVkyQrKyukpqaqlB88eBBdunSpiTERERERqV2Vv902Z84cTJs2Dc+ePYMgCDhz5gx27NiB4OBgfPfdd7UxRiIiIqLXrsorSZ9++ikWL16MuXPn4unTp/j4448RERGBNWvWYPTo0VUeQFhYGKysrCCXy+Hg4IATJ05UGB8fHw8HBwfI5XJYW1sjIiJC6fymTZvg4uKCli1bomXLlhg0aBDOnDmjFBMYGAiZTKZ0mJqaVnnsRERE1HBVawsAb29v3L59G9nZ2cjKysKdO3cwefLkKrcTHR0NX19fLFiwACkpKXBxcYG7uzsyMjIk49PT0+Hh4QEXFxekpKQgICAAM2bMwO7du8WYuLg4jBkzBsePH0diYiLatm0LV1dX3L17V6mtrl27QqFQiAcfOiciIqK/q/Lttr8zMjJ6pc5Xr16NyZMnY8qUKQCe77V0+PBhhIeHIzg4WCU+IiICbdu2FbcasLW1RVJSElatWgVPT08AwA8//KBUZ9OmTfjPf/6DY8eOYfz48WK5pqYmV4+IiIioXFVeScrNzcW0adPQpUsXGBkZoVWrVkpHZRUWFiI5ORmurq5K5a6urkhISJCsk5iYqBLv5uaGpKQkFBUVSdZ5+vQpioqKVMZ2/fp1mJubw8rKCqNHj8bNmzcrHG9BQQHy8/OVDiIiImq4qryS9Mknn+DGjRuYPHkyTExMIJPJqtVxTk4OSkpKYGJiolRuYmKCrKwsyTpZWVmS8cXFxcjJyYGZmZlKnfnz56NNmzYYNGiQWNa7d29s3boVHTt2xJ9//olly5bB2dkZly5dgqGhoWTfwcHBWLJkSVUvk4iIiOqpKidJJ0+exMmTJ9G9e/caGcCLSZYgCBUmXlLxUuUAEBISgh07diAuLg5yuVwsd3d3F//b3t4eTk5OaN++PaKiouDn5yfZr7+/v9K5/Px8WFhYVHBlREREVJ9VOUnq3Lkz/vrrr1fu2MjICBoaGiqrRtnZ2SqrRWVMTU0l4zU1NVVWgFatWoWvv/4aR48eRbdu3Soci56eHuzt7XH9+vVyY3R0dKCjo1NhO1R/+O/hg/pERFSxKj+TFBYWhgULFiA+Ph65ubnVfk5HW1sbDg4OiI2NVSqPjY2Fs7OzZB0nJyeV+CNHjsDR0RFaWlpi2cqVK7F06VIcOnQIjo6OLx1LQUEB0tLSJG/XERERUeNU5ZWkFi1aIC8vDwMGDFAqL7tNVvbC28rw8/PDuHHj4OjoCCcnJ2zcuBEZGRnw8fEB8PwW1927d7F161YAgI+PD9avXw8/Pz94e3sjMTERkZGR2LFjh9hmSEgIFi5ciB9//BGWlpbiypO+vj709fUBALNnz8awYcPQtm1bZGdnY9myZcjPz8eECROqOh1ERETUQFU5SRo7diy0tbXx448/vtKD2wDg5eWF3NxcBAUFQaFQwM7ODjExMWjXrh0AQKFQKO2ZZGVlhZiYGMyaNQsbNmyAubk51q5dK379H3i+0lVYWIgPP/xQqa/FixcjMDAQAJCZmYkxY8YgJycHxsbGePvtt/Hbb7+J/RIR1Us/z1T3CKpu2Bp1j4CoXDKh7MnnSmratClSUlLQqVOn2hpTvZCfnw8DAwPk5eWhefPm6h4OVVF9fiZpZGaIuodQr/S2qvzWJErq4y9vJklEL1WV399VfibJ0dERd+7cqfbgiIiIiOqDKt9u+/zzzzFz5kzMmTMH9vb2Sg9MA3jpN8mIiIiI6oMqJ0leXl4AgEmTJollMpmsWg9uExEREdVVVU6S0tPTa2McRERERHVKlZMkfgOMiIiIGoMqJ0llLl++jIyMDBQWFiqVDx8+/JUHRURERKRuVU6Sbt68iffffx8XL14Un0UC/vfuND6TRERERA1BlbcAmDlzJqysrPDnn3+iadOmuHTpEn799Vc4OjoiLi6uFoZIRERE9PpVeSUpMTERv/zyC4yNjdGkSRM0adIE77zzDoKDgzFjxgykpKTUxjiJiIiIXqsqrySVlJSI70AzMjLCvXv3ADx/oPvq1as1OzoiIiIiNanySpKdnR0uXLgAa2tr9O7dGyEhIdDW1sbGjRthbW1dG2MkIiIieu2qnCR9+eWXePLkCQBg2bJlGDp0KFxcXGBoaIjo6OgaHyARERGROlQ5SXJzcxP/29raGpcvX8b9+/fRsmVL8RtuRER1xen0+9Wqt+81vgQ5+AP719YXEVVetfdJ+rtWrar5lm0iIiKiOqrSSZJCocD69evx1VdfAQDeeecdPH36VDyvoaGBffv2oU2bNjU/SiKi12xkZsjr6+xn/kOTqC6q9LfbwsLC8PDhQ/Hz+fPn4eLighEjRmDEiBHQ0NDAN998UxtjJCIiInrtKr2S9PPPP2PlypVKZTNnzhS/0fb222/Dz88Pq1atqtkREhEREalBpVeSbt26hfbt24ufBw8eDD09PfFzp06dkJ6eXrOjIyIiIlKTSq8kFRcXIy8vT/y8Z88epfMPHjxAkyZV3puSiIiIqE6qdFbTqVMnJCQklHv+xIkT6NixY40MioiIiEjdKp0kjR49GosWLcKFCxdUzp0/fx5LlizBmDFjanRwREREROpS6dttvr6+OHDgABwcHDB48GB06tQJMpkMV65cQWxsLJycnODr61uLQyUiIiJ6fSqdJGlpaSE2NharV6/Gzp07ERcXBwDo0KEDli5dilmzZkFLS6u2xklERET0WlVpx21tbW3Mnz8f8+fPr63xEBEREdUJ/DoaERERkQQmSUREREQSmCQRERERSahUkpSfn1/b4yAiIiKqUyqVJLVs2RLZ2dkAgAEDBii96JaIiIioIapUkqSvr4/c3FwAQFxcHIqKimp1UERERETqVqktAAYNGoR3330Xtra2AID3338f2trakrG//PJLzY2OiIiISE0qtZK0fft2BAYGwtHREQDQtWtXdO/eXfKoqrCwMFhZWUEul8PBwQEnTpyoMD4+Ph4ODg6Qy+WwtrZGRESE0vlNmzbBxcUFLVu2RMuWLTFo0CCcOXPmlfslIiKixqVSK0m6urrw8fEBACQlJWHFihVo0aLFK3ceHR0NX19fhIWFoU+fPvj222/h7u6Oy5cvo23btirx6enp8PDwgLe3N7Zv345Tp05h6tSpMDY2hqenJ4DntwPHjBkDZ2dnyOVyhISEwNXVFZcuXUKbNm2q1S8RERE1PjJBEITqVi6rKpPJqlW/d+/e6NmzJ8LDw8UyW1tbjBw5EsHBwSrx8+bNw08//YS0tDSxzMfHB+fPn0diYqJkHyUlJWjZsiXWr1+P8ePHV6tfKfn5+TAwMEBeXh6aN29eqTpUd/jvuajuIVTbyMwQdQ+Balhvq1bqHoL6DFuj7hFQI1OV39/V2idp69atsLe3h66uLnR1ddGtWzds27atSm0UFhYiOTkZrq6uSuWurq5ISEiQrJOYmKgS7+bmhqSkpHIfJn/69CmKiorQqlWravcLAAUFBcjPz1c6iIiIqOGqcpK0evVqfPbZZ/Dw8MCuXbsQHR2NIUOGwMfHB998802l28nJyUFJSQlMTEyUyk1MTJCVlSVZJysrSzK+uLgYOTk5knXmz5+PNm3aYNCgQdXuFwCCg4NhYGAgHhYWFi+9RiIiIqq/qvSCWwBYt24dwsPDxVtXADBixAh07doVgYGBmDVrVpXae/FWnSAIFd6+k4qXKgeAkJAQ7NixA3FxcZDL5a/Ur7+/P/z8/MTP+fn5TJSIiIgasConSQqFAs7Ozirlzs7OUCgUlW7HyMgIGhoaKqs32dnZKqs8ZUxNTSXjNTU1YWhoqFS+atUqfP311zh69Ci6dev2Sv0CgI6ODnR0dCp1bURERFT/Vfl2m42NDXbt2qVSHh0djQ4dOlS6HW1tbTg4OCA2NlapPDY2VjIJAwAnJyeV+CNHjsDR0RFaWlpi2cqVK7F06VIcOnRI3LbgVfolIiKixqfKK0lLliyBl5cXfv31V/Tp0wcymQwnT57EsWPHJJOnivj5+WHcuHFwdHSEk5MTNm7ciIyMDHG7AX9/f9y9exdbt24F8PybbOvXr4efnx+8vb2RmJiIyMhI7NixQ2wzJCQECxcuxI8//ghLS0txxUhfXx/6+vqV6peIiIioykmSp6cnTp8+jW+++Qb79u2DIAjo0qULzpw5gzfffLNKbXl5eSE3NxdBQUFQKBSws7NDTEwM2rVrB+D5rb2MjAwx3srKCjExMZg1axY2bNgAc3NzrF27VtwjCXi+SWRhYSE+/PBDpb4WL16MwMDASvVLRERE9Er7JDVm3CepfuM+SVSXcJ8koten1vdJIiIiImromCQRERERSWCSRERERCSBSRIRERGRhCp/u42IiKjG/DxT3SOoOj5s3mjU2EpSWFgYgoKCaqo5IiIiIrWqsSRp9+7d2LJlS001R0RERKRWNXa77dixYzXVFBEREZHavdJKkiAI4F6URERE1BBVK0naunUr7O3toaurC11dXXTr1g3btm2r6bERERERqU2Vb7etXr0aCxcuxPTp09GnTx8IgoBTp07Bx8cHOTk5mDVrVm2Mk4iIiOi1qnKStG7dOoSHh2P8+PFi2YgRI9C1a1cEBgYySSIiIqIGocq32xQKBZydnVXKnZ2doVAoamRQREREROpW5STJxsYGu3btUimPjo5Ghw4damRQREREROpW5dttS5YsgZeXF3799Vf06dMHMpkMJ0+exLFjxySTJyIiIqL6qMorSZ6enjh9+jSMjIywb98+7NmzB0ZGRjhz5gzef//92hgjERER0WtXrc0kHRwcsH379poeCxEREVGdUWOvJSEiIiJqSCq9ktSkSRPIZLIKY2QyGYqLi195UERERETqVukkae/eveWeS0hIwLp16/iKEiIiImowKp0kjRgxQqXsypUr8Pf3x88//4yxY8di6dKlNTo4IiIiInWp1jNJ9+7dg7e3N7p164bi4mKkpqYiKioKbdu2renxEREREalFlZKkvLw8zJs3DzY2Nrh06RKOHTuGn3/+GXZ2drU1PiIiIiK1qPTttpCQEKxYsQKmpqbYsWOH5O03IiIiooai0knS/PnzoaurCxsbG0RFRSEqKkoybs+ePTU2OCIiIiJ1qXSSNH78+JduAUBERETUUFQ6SdqyZUstDoOIiIiobuGO20REREQSmCQRERERSWCSRERERCSBSRIRERGRBCZJRERERBLUniSFhYXBysoKcrkcDg4OOHHiRIXx8fHxcHBwgFwuh7W1NSIiIpTOX7p0CZ6enrC0tIRMJkNoaKhKG4GBgZDJZEqHqalpTV4WERER1XNqTZKio6Ph6+uLBQsWICUlBS4uLnB3d0dGRoZkfHp6Ojw8PODi4oKUlBQEBARgxowZ2L17txjz9OlTWFtbY/ny5RUmPl27doVCoRCPixcv1vj1ERERUf1V6X2SasPq1asxefJkTJkyBQAQGhqKw4cPIzw8HMHBwSrxERERaNu2rbg6ZGtri6SkJKxatQqenp4AgF69eqFXr14Anu8SXh5NTU2uHhEREVG51LaSVFhYiOTkZLi6uiqVu7q6IiEhQbJOYmKiSrybmxuSkpJQVFRUpf6vX78Oc3NzWFlZYfTo0bh582aF8QUFBcjPz1c6iIiIqOFSW5KUk5ODkpISmJiYKJWbmJggKytLsk5WVpZkfHFxMXJycirdd+/evbF161YcPnwYmzZtQlZWFpydnZGbm1tuneDgYBgYGIiHhYVFpfsjIiKi+kftD26/+D44QRAqfEecVLxUeUXc3d3h6ekJe3t7DBo0CP/9738BoNyX9gKAv78/8vLyxOPOnTuV7o+IiIjqH7U9k2RkZAQNDQ2VVaPs7GyV1aIypqamkvGampowNDSs9lj09PRgb2+P69evlxujo6MDHR2davdBRERE9YvaVpK0tbXh4OCA2NhYpfLY2Fg4OztL1nFyclKJP3LkCBwdHaGlpVXtsRQUFCAtLQ1mZmbVboOIiIgaFrXebvPz88N3332H77//HmlpaZg1axYyMjLg4+MD4PktrvHjx4vxPj4+uH37Nvz8/JCWlobvv/8ekZGRmD17thhTWFiI1NRUpKamorCwEHfv3kVqair++OMPMWb27NmIj49Heno6Tp8+jQ8//BD5+fmYMGHC67t4IiIiqtPUugWAl5cXcnNzERQUBIVCATs7O8TExKBdu3YAAIVCobRnkpWVFWJiYjBr1ixs2LAB5ubmWLt2rfj1fwC4d+8e3nzzTfHzqlWrsGrVKvTr1w9xcXEAgMzMTIwZMwY5OTkwNjbG22+/jd9++03sl16d/x7uO0VUWafT76t7CC/V26qVuodA9NrJhLInn6lK8vPzYWBggLy8PDRv3lzdw6lzmCTVnpGZIeoeAjVCTJL+ZtgadY+AXkFVfn+r/dttRERERHURkyQiIiIiCUySiIiIiCQwSSIiIiKSwCSJiIiISAKTJCIiIiIJTJKIiIiIJDBJIiIiIpLAJImIiIhIApMkIiIiIglMkoiIiIgkMEkiIiIiksAkiYiIiEgCkyQiIiIiCUySiIiIiCQwSSIiIiKSwCSJiIiISAKTJCIiIiIJTJKIiIiIJDBJIiIiIpLAJImIiIhIApMkIiIiIglMkoiIiIgkMEkiIiIiksAkiYiIiEiCproHQERERK/BzzPVPYKqG7ZGrd1zJYmIiIhIApMkIiIiIglMkoiIiIgkMEkiIiIiksAkiYiIiEiC2pOksLAwWFlZQS6Xw8HBASdOnKgwPj4+Hg4ODpDL5bC2tkZERITS+UuXLsHT0xOWlpaQyWQIDQ2tkX6JiIiocVFrkhQdHQ1fX18sWLAAKSkpcHFxgbu7OzIyMiTj09PT4eHhARcXF6SkpCAgIAAzZszA7t27xZinT5/C2toay5cvh6mpaY30S0RERI2PWpOk1atXY/LkyZgyZQpsbW0RGhoKCwsLhIeHS8ZHRESgbdu2CA0Nha2tLaZMmYJJkyZh1apVYkyvXr2wcuVKjB49Gjo6OjXSLxERETU+akuSCgsLkZycDFdXV6VyV1dXJCQkSNZJTExUiXdzc0NSUhKKiopqrV8iIiJqfNS243ZOTg5KSkpgYmKiVG5iYoKsrCzJOllZWZLxxcXFyMnJgZmZWa30CwAFBQUoKCgQP+fn57+0LyIiIqq/1P7gtkwmU/osCIJK2cvipcprut/g4GAYGBiIh4WFRZX6IyIiovpFbUmSkZERNDQ0VFZvsrOzVVZ5ypiamkrGa2pqwtDQsNb6BQB/f3/k5eWJx507dyrVHxEREdVPakuStLW14eDggNjYWKXy2NhYODs7S9ZxcnJSiT9y5AgcHR2hpaVVa/0CgI6ODpo3b650EBERUcOltmeSAMDPzw/jxo2Do6MjnJycsHHjRmRkZMDHxwfA89Wbu3fvYuvWrQAAHx8frF+/Hn5+fvD29kZiYiIiIyOxY8cOsc3CwkJcvnxZ/O+7d+8iNTUV+vr6sLGxqVS/RERERGpNkry8vJCbm4ugoCAoFArY2dkhJiYG7dq1AwAoFAqlvYusrKwQExODWbNmYcOGDTA3N8fatWvh6ekpxty7dw9vvvmm+HnVqlVYtWoV+vXrh7i4uEr1S0RERCQTyp58pirJz8+HgYEB8vLyeOtNgv+ei+oeQoM1MjNE3UOgRqi3VSt1D6HuGLZG3SOonp9nqnsEVVcLc12V399q/3YbERERUV3EJImIiIhIApMkIiIiIglMkoiIiIgkMEkiIiIiksAkiYiIiEgCkyQiIiIiCUySiIiIiCQwSSIiIiKSwCSJiIiISAKTJCIiIiIJTJKIiIiIJDBJIiIiIpLAJImIiIhIApMkIiIiIglMkoiIiIgkMEkiIiIiksAkiYiIiEgCkyQiIiIiCUySiIiIiCQwSSIiIiKSwCSJiIiISAKTJCIiIiIJTJKIiIiIJDBJIiIiIpLAJImIiIhIApMkIiIiIglMkoiIiIgkMEkiIiIiksAkiYiIiEgCkyQiIiIiCUySiIiIiCSoPUkKCwuDlZUV5HI5HBwccOLEiQrj4+Pj4eDgALlcDmtra0RERKjE7N69G126dIGOjg66dOmCvXv3Kp0PDAyETCZTOkxNTWv0uoiIiKh+U2uSFB0dDV9fXyxYsAApKSlwcXGBu7s7MjIyJOPT09Ph4eEBFxcXpKSkICAgADNmzMDu3bvFmMTERHh5eWHcuHE4f/48xo0bh1GjRuH06dNKbXXt2hUKhUI8Ll68WKvXSkRERPWLWpOk1atXY/LkyZgyZQpsbW0RGhoKCwsLhIeHS8ZHRESgbdu2CA0Nha2tLaZMmYJJkyZh1apVYkxoaCgGDx4Mf39/dO7cGf7+/hg4cCBCQ0OV2tLU1ISpqal4GBsb1+alEhERUT2jtiSpsLAQycnJcHV1VSp3dXVFQkKCZJ3ExESVeDc3NyQlJaGoqKjCmBfbvH79OszNzWFlZYXRo0fj5s2bFY63oKAA+fn5SgcRERE1XGpLknJyclBSUgITExOlchMTE2RlZUnWycrKkowvLi5GTk5OhTF/b7N3797YunUrDh8+jE2bNiErKwvOzs7Izc0td7zBwcEwMDAQDwsLiypdLxEREdUvan9wWyaTKX0WBEGl7GXxL5a/rE13d3d4enrC3t4egwYNwn//+18AQFRUVLn9+vv7Iy8vTzzu3LnzkisjIiKi+kxTXR0bGRlBQ0NDZdUoOztbZSWojKmpqWS8pqYmDA0NK4wpr00A0NPTg729Pa5fv15ujI6ODnR0dCq8JiIiImo41LaSpK2tDQcHB8TGxiqVx8bGwtnZWbKOk5OTSvyRI0fg6OgILS2tCmPKaxN4/rxRWloazMzMqnMpRERE1ACp9Xabn58fvvvuO3z//fdIS0vDrFmzkJGRAR8fHwDPb3GNHz9ejPfx8cHt27fh5+eHtLQ0fP/994iMjMTs2bPFmJkzZ+LIkSNYsWIFrly5ghUrVuDo0aPw9fUVY2bPno34+Hikp6fj9OnT+PDDD5Gfn48JEya8tmsnIiKiuk1tt9sAwMvLC7m5uQgKCoJCoYCdnR1iYmLQrl07AIBCoVDaM8nKygoxMTGYNWsWNmzYAHNzc6xduxaenp5ijLOzM3bu3Ikvv/wSCxcuRPv27REdHY3evXuLMZmZmRgzZgxycnJgbGyMt99+G7/99pvYLxEREZFMKHvymaokPz8fBgYGyMvLQ/PmzdU9nDrHfw8356wtIzND1D0EaoR6W7VS9xDqjmFr1D2C6vl5prpHUHW1MNdV+f2t9m+3EREREdVFar3dRtXDVRoiet1Op99X9xBeiqtdVNO4kkREREQkgUkSERERkQQmSUREREQSmCQRERERSWCSRERERCSBSRIRERGRBCZJRERERBKYJBERERFJYJJEREREJIFJEhEREZEEJklEREREEpgkEREREUlgkkREREQkgUkSERERkQQmSUREREQSmCQRERERSdBU9wCIiIhqwun0+6+ln317Lr6WfmrayMzKzU9vq1a1PJL6gytJRERERBK4klRX/Tyz3FOV/dcAERHVvJGZIeoeAr0mXEkiIiIiksAkiYiIiEgCkyQiIiIiCUySiIiIiCQwSSIiIiKSwCSJiIiISAKTJCIiIiIJTJKIiIiIJDBJIiIiIpLAJImIiIhIgtqTpLCwMFhZWUEul8PBwQEnTpyoMD4+Ph4ODg6Qy+WwtrZGRESESszu3bvRpUsX6OjooEuXLti7d+8r90tERESNi1qTpOjoaPj6+mLBggVISUmBi4sL3N3dkZGRIRmfnp4ODw8PuLi4ICUlBQEBAZgxYwZ2794txiQmJsLLywvjxo3D+fPnMW7cOIwaNQqnT5+udr9ERETU+MgEQRDU1Xnv3r3Rs2dPhIeHi2W2trYYOXIkgoODVeLnzZuHn376CWlpaWKZj48Pzp8/j8TERACAl5cX8vPzcfDgQTFmyJAhaNmyJXbs2FGtfqXk5+fDwMAAeXl5aN68edUuvDIqeMHt6XS+4JaIiGpHb6tW6h7C/wxbU+NNVuX3t9pWkgoLC5GcnAxXV1elcldXVyQkJEjWSUxMVIl3c3NDUlISioqKKowpa7M6/RIREVHjo6mujnNyclBSUgITExOlchMTE2RlZUnWycrKkowvLi5GTk4OzMzMyo0pa7M6/QJAQUEBCgoKxM95eXkAnmekteJpQbmnnjwrrJ0+iYio0cuv4PfPa1cLv2PLfm9X5kaa2pKkMjKZTOmzIAgqZS+Lf7G8Mm1Wtd/g4GAsWbJEpdzCwqLcOkRERPQqvq21lh89egQDA4MKY9SWJBkZGUFDQ0Nl9SY7O1tllaeMqampZLympiYMDQ0rjClrszr9AoC/vz/8/PzEz6Wlpbh//z4MDQ0rTK7qivz8fFhYWODOnTu18wxVPcQ5UcU5kcZ5UcU5UcU5kVbX5kUQBDx69Ajm5uYvjVVbkqStrQ0HBwfExsbi/fffF8tjY2MxYsQIyTpOTk74+eeflcqOHDkCR0dHaGlpiTGxsbGYNWuWUoyzs3O1+wUAHR0d6OjoKJW1aNGichdbhzRv3rxO/CGtSzgnqjgn0jgvqjgnqjgn0urSvLxsBamMWm+3+fn5Ydy4cXB0dISTkxM2btyIjIwM+Pj4AHi+enP37l1s3boVwPNvsq1fvx5+fn7w9vZGYmIiIiMjxW+tAcDMmTPRt29frFixAiNGjMD+/ftx9OhRnDx5stL9EhEREak1SfLy8kJubi6CgoKgUChgZ2eHmJgYtGvXDgCgUCiU9i6ysrJCTEwMZs2ahQ0bNsDc3Bxr166Fp6enGOPs7IydO3fiyy+/xMKFC9G+fXtER0ejd+/ele6XiIiICAI1Cs+ePRMWL14sPHv2TN1DqTM4J6o4J9I4L6o4J6o4J9Lq87yodTNJIiIiorpK7e9uIyIiIqqLmCQRERERSWCSRERERCSBSRIRERGRBCZJDYilpSVkMpnKMW3aNADPdxkNDAyEubk5dHV10b9/f1y6dEnNo65dxcXF+PLLL2FlZQVdXV1YW1sjKCgIpaWlYkxjnJdHjx7B19cX7dq1g66uLpydnXH27FnxfGOYk19//RXDhg2Dubk5ZDIZ9u3bp3S+MnNQUFCAzz//HEZGRtDT08Pw4cORmZn5Gq+iZr1sTvbs2QM3NzcYGRlBJpMhNTVVpY2GNidAxfNSVFSEefPmwd7eHnp6ejA3N8f48eNx7949pTYa2ry87M9KYGAgOnfuDD09PbRs2RKDBg3C6dOnlWLqw5wwSWpAzp49C4VCIR6xsbEAgI8++ggAEBISgtWrV2P9+vU4e/YsTE1NMXjwYDx69Eidw65VK1asQEREBNavX4+0tDSEhIRg5cqVWLdunRjTGOdlypQpiI2NxbZt23Dx4kW4urpi0KBBuHv3LoDGMSdPnjxB9+7dsX79esnzlZkDX19f7N27Fzt37sTJkyfx+PFjDB06FCUlJa/rMmrUy+bkyZMn6NOnD5YvX15uGw1tToCK5+Xp06c4d+4cFi5ciHPnzmHPnj24du0ahg8frhTX0OblZX9WOnbsiPXr1+PixYs4efIkLC0t4erqiv/7v/8TY+rFnKhz/wGqXTNnzhTat28vlJaWCqWlpYKpqamwfPly8fyzZ88EAwMDISIiQo2jrF3vvfeeMGnSJKWyDz74QPjkk08EQRAa5bw8ffpU0NDQEA4cOKBU3r17d2HBggWNck4ACHv37hU/V2YOHj58KGhpaQk7d+4UY+7evSs0adJEOHTo0Gsbe215cU7+Lj09XQAgpKSkKJU39DkRhIrnpcyZM2cEAMLt27cFQWj481KZOcnLyxMACEePHhUEof7MCVeSGqjCwkJs374dkyZNgkwmQ3p6OrKysuDq6irG6OjooF+/fkhISFDjSGvXO++8g2PHjuHatWsAgPPnz+PkyZPw8PAAgEY5L8XFxSgpKYFcLlcq19XVxcmTJxvlnLyoMnOQnJyMoqIipRhzc3PY2dk1mnl6Eefkuby8PMhkMvH9no19XgoLC7Fx40YYGBige/fuAOrPnKj1tSRUe/bt24eHDx9i4sSJAICsrCwAgImJiVKciYkJbt++/bqH99rMmzcPeXl56Ny5MzQ0NFBSUoKvvvoKY8aMAdA456VZs2ZwcnLC0qVLYWtrCxMTE+zYsQOnT59Ghw4dGuWcvKgyc5CVlQVtbW20bNlSJaasfmPDOQGePXuG+fPn4+OPPxZf5tpY5+XAgQMYPXo0nj59CjMzM8TGxsLIyAhA/ZkTriQ1UJGRkXB3d4e5ublSuUwmU/osCIJKWUMSHR2N7du348cff8S5c+cQFRWFVatWISoqSimusc3Ltm3bIAgC2rRpAx0dHaxduxYff/wxNDQ0xJjGNidSqjMHjXGeXqaxzElRURFGjx6N0tJShIWFvTS+oc/Lu+++i9TUVCQkJGDIkCEYNWoUsrOzK6xT1+aESVIDdPv2bRw9ehRTpkwRy0xNTQFAJUPPzs5W+ddyQzJnzhzMnz8fo0ePhr29PcaNG4dZs2YhODgYQOOdl/bt2yM+Ph6PHz/GnTt3cObMGRQVFcHKyqrRzsnfVWYOTE1NUVhYiAcPHpQb09g05jkpKirCqFGjkJ6ejtjYWHEVCWi886KnpwcbGxu8/fbbiIyMhKamJiIjIwHUnzlhktQAbd68Ga1bt8Z7770nlpX98iv7xhvw/D5xfHw8nJ2d1THM1+Lp06do0kT5j7mGhoa4BUBjnZcyenp6MDMzw4MHD3D48GGMGDGi0c8JULk/Fw4ODtDS0lKKUSgU+P333xvNPL2osc5JWYJ0/fp1HD16FIaGhkrnG+u8vEgQBBQUFACoP3PCZ5IamNLSUmzevBkTJkyApub/frwymQy+vr74+uuv0aFDB3To0AFff/01mjZtio8//liNI65dw4YNw1dffYW2bduia9euSElJwerVqzFp0iQAjXdeDh8+DEEQ0KlTJ/zxxx+YM2cOOnXqhE8//bTRzMnjx4/xxx9/iJ/T09ORmpqKVq1aoW3bti+dAwMDA0yePBlffPEFDA0N0apVK8yePRv29vYYNGiQui7rlbxsTu7fv4+MjAxxD6CrV68CeL4qYGpq2iDnBKh4XszNzfHhhx/i3LlzOHDgAEpKSsQVyFatWkFbW7tBzktFc2JoaIivvvoKw4cPh5mZGXJzcxEWFobMzExxS5p6Mydq+lYd1ZLDhw8LAISrV6+qnCstLRUWL14smJqaCjo6OkLfvn2FixcvqmGUr09+fr4wc+ZMoW3btoJcLhesra2FBQsWCAUFBWJMY5yX6OhowdraWtDW1hZMTU2FadOmCQ8fPhTPN4Y5OX78uABA5ZgwYYIgCJWbg7/++kuYPn260KpVK0FXV1cYOnSokJGRoYarqRkvm5PNmzdLnl+8eLHYRkObE0GoeF7KtkOQOo4fPy620dDmpaI5+euvv4T3339fMDc3F7S1tQUzMzNh+PDhwpkzZ5TaqA9zIhMEQaj9VIyIiIiofuEzSUREREQSmCQRERERSWCSRERERCSBSRIRERGRBCZJRERERBKYJBERERFJYJJEREREJIFJEhHVaXFxcZDJZHj48KG6h1Kn9e/fH76+vuoeBlGDwiSJiGrVxIkTIZPJIJPJoKWlBWtra8yePRtPnjypVH1nZ2coFAoYGBhUqc+RI0dWc8TPPXnyBPPmzYO1tTXkcjmMjY3Rv39/HDhw4JXaJaL6g+9uI6JaN2TIEGzevBlFRUU4ceIEpkyZgidPniA8PPyldbW1tWFqavoaRqnMx8cHZ86cwfr169GlSxfk5uYiISEBubm5tdZnYWEhtLW1a619IqoariQRUa3T0dGBqakpLCws8PHHH2Ps2LHYt28fAKCgoAAzZsxA69atIZfL8c477+Ds2bNi3Rdvt23ZsgUtWrTA4cOHYWtrC319fQwZMgQKhQIAEBgYiKioKOzfv19cwYqLi0NhYSGmT58OMzMzyOVyWFpaIjg4uNwx//zzzwgICICHhwcsLS3h4OCAzz//HBMmTBBjCgoKMHfuXFhYWEBHRwcdOnRAZGSkeD4+Ph5vvfUWdHR0YGZmhvnz56O4uFg8379/f0yfPh1+fn4wMjLC4MGDAQCXL1+Gh4cH9PX1YWJignHjxiEnJ0es9+TJE4wfPx76+vowMzPDv/71r+r/cIioXEySiOi109XVRVFREQBg7ty52L17N6KionDu3DnY2NjAzc0N9+/fL7f+06dPsWrVKmzbtg2//vorMjIyMHv2bADA7NmzMWrUKDFxUigUcHZ2xtq1a/HTTz9h165duHr1KrZv3w5LS8ty+zA1NUVMTAwePXpUbsz48eOxc+dOrF27FmlpaYiIiIC+vj4A4O7du/Dw8ECvXr1w/vx5hIeHIzIyEsuWLVNqIyoqCpqamjh16hS+/fZbKBQK9OvXDz169EBSUhIOHTqEP//8E6NGjRLrzJkzB8ePH8fevXtx5MgRxMXFITk5+aXzTkRVpO437BJRwzZhwgRhxIgR4ufTp08LhoaGwqhRo4THjx8LWlpawg8//CCeLywsFMzNzYWQkBBBEP73tvEHDx4IgvC/N9H/8ccfYp0NGzYIJiYm5fYpCILw+eefCwMGDBBKS0srNe74+HjhjTfeELS0tARHR0fB19dXOHnypHj+6tWrAgAhNjZWsn5AQIDQqVMnpf42bNgg6OvrCyUlJYIgCEK/fv2EHj16KNVbuHCh4OrqqlR2584dAYBw9epV4dGjR4K2trawc+dO8Xxubq6gq6srzJw5s1LXRkSVw5UkIqp1Bw4cgL6+PuRyOZycnNC3b1+sW7cON27cQFFREfr06SPGamlp4a233kJaWlq57TVt2hTt27cXP5uZmSE7O7vCMUycOBGpqano1KkTZsyYgSNHjlQY37dvX9y8eRPHjh2Dp6cnLl26BBcXFyxduhQAkJqaCg0NDfTr10+yflpaGpycnCCTycSyPn364PHjx8jMzBTLHB0dleolJyfj+PHj0NfXF4/OnTsDAG7cuIEbN26gsLAQTk5OYp1WrVqhU6dOFV4PEVUdkyQiqnXvvvsuUlNTcfXqVTx79gx79uxB69atIQgCACglEgAgCIJK2d9paWkpfZbJZGJb5enZsyfS09OxdOlS/PXXXxg1ahQ+/PDDCutoaWnBxcUF8+fPx5EjRxAUFISlS5eisLAQurq6FdaVugap69XT01OKKS0txbBhw5Camqp0XL9+HX379n3pdRJRzWGSRES1Tk9PDzY2NmjXrp1SgmNjYwNtbW2cPHlSLCsqKkJSUhJsbW2r3Z+2tjZKSkpUyps3bw4vLy9s2rQJ0dHR2L17d4XPPr2oS5cuKC4uxrNnz2Bvb4/S0lLEx8eXG5uQkKCU1CQkJKBZs2Zo06ZNuX307NkTly5dgqWlJWxsbJSOsnnU0tLCb7/9JtZ58OABrl27VunrIKLKYZJERGqjp6eHzz77DHPmzMGhQ4dw+fJleHt74+nTp5g8eXK127W0tMSFCxdw9epV5OTkoKioCN988w127tyJK1eu4Nq1a/j3v/8NU1NTtGjRQrKN/v3749tvv0VycjJu3bqFmJgYBAQE4N1330Xz5s1haWmJCRMmYNKkSdi3bx/S09MRFxeHXbt2AQCmTp2KO3fu4PPPP8eVK1ewf/9+LF68GH5+fmjSpPz/9U6bNg3379/HmDFjcObMGdy8eRNHjhzBpEmTUFJSAn19fUyePBlz5szBsWPH8Pvvv2PixIkVtklE1cN9kohIrZYvX47S0lKMGzcOjx49gqOjIw4fPoyWLVtWu01vb2/ExcXB0dERjx8/Fp/xWbFiBa5fvw4NDQ306tULMTEx5SYXbm5uiIqKQkBAAJ4+fQpzc3MMHToUixYtEmPCw8MREBCAqVOnIjc3F23btkVAQAAAoE2bNoiJicGcOXPQvXt3tGrVCpMnT8aXX35Z4djNzc1x6tQpzJs3D25ubigoKEC7du0wZMgQcawrV67E48ePMXz4cDRr1gxffPEF8vLyqj1fRCRNJvAGNxEREZEKrs8SERERSWCSRERERCSBSRIRERGRBCZJRERERBKYJBERERFJYJJEREREJIFJEhEREZEEJklEREREEpgkEREREUlgkkREREQkgUkSERERkQQmSUREREQS/h+TzD3HlBjujgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "plt.hist(nets_pts_per_game_2010,density=True,alpha=0.6,label='Nets Points Scored')\n",
    "plt.hist(knicks_pts_per_game_2010,density=True,alpha=0.6,label='Knicks Points Scored')\n",
    "plt.ylabel(\"No. of Games\")\n",
    "plt.xlabel(\"Points Scored\")\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('2010 Season')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4\n",
    "Now, let’s compare the 2010 games to 2014. Replicate the steps from Tasks 2 and 3 using `nba_2014`. First, calculate the mean difference between the two teams points scored. Save and print the value as `diff_means_2014`. Did the difference in points get larger or smaller in 2014? Then, plot the overlapping histograms. Does the mean difference you calculated make sense?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 676,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The difference in means netween the nets and knicks is 0.45\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Getting the 'pts' column from the dataset (year 2014).\n",
    "nets_pts_per_game_2014 = nba_2014[nba_2014.fran_id=='Nets']['pts']\n",
    "knicks_pts_per_game_2014 = nba_2014[nba_2014.fran_id=='Knicks']['pts']\n",
    "\n",
    "# Getting the average pts\n",
    "nets_avg_pts = np.around(np.mean(nets_pts_per_game_2014),2)\n",
    "knicks_avg_pts = np.around(np.mean(knicks_pts_per_game_2014),2)\n",
    "diff_means_2014 = np.around(np.absolute(nets_avg_pts - knicks_avg_pts),2)\n",
    "print(f'The difference in means netween the nets and knicks is {diff_means_2014}')\n",
    "\n",
    "# Plotting the histogram\n",
    "\n",
    "plt.clf()\n",
    "plt.hist(nets_pts_per_game_2014,density=True,alpha=0.6,label='Nets Points Scored')\n",
    "plt.hist(knicks_pts_per_game_2014,density=True,alpha=0.6,label='Knicks Points Scored')\n",
    "plt.ylabel(\"No. of Games\")\n",
    "plt.xlabel(\"Points Scored\")\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('2014 Season')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5\n",
    "For the remainder of this project, we’ll focus on data from 2010. Let’s now include all teams in the dataset and investigate the relationship between franchise and points scored per game.\n",
    "\n",
    "Using nba_2010, generate side-by-side boxplots with points scored (pts) on the y-axis and team (fran_id) on the x-axis. Is there any overlap between the boxes? Does this chart suggest that fran_id and pts are associated? Which pairs of teams, if any, earn different average scores per game?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 678,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentile Statistics for 25th, 50th and 75th percentile:         <lambda_0>  <lambda_1>  <lambda_2>\n",
      "fran_id                                    \n",
      "Celtics     68.5750      70.150     71.7250\n",
      "Knicks      69.2275      71.455     73.6825\n",
      "Nets        68.8100      69.620     70.4300\n",
      "Spurs       77.1375      78.275     79.4125\n",
      "Thunder     75.0875      76.175     77.2625\n"
     ]
    }
   ],
   "source": [
    "box_plot_data = nba_2010[['fran_id','pts']]\n",
    "sns.boxplot(data=box_plot_data, x=\"fran_id\", y=\"pts\",saturation=0.85,dodge='auto',width=0.8)\n",
    "plt.xlabel('Franchise')\n",
    "plt.ylabel('Points Scored')\n",
    "plt.title('Distribution of Points')\n",
    "plt.grid(alpha=0.5)\n",
    "plt.show()\n",
    "#Getting the percentile Statistics\n",
    "grouped = nba_2010.groupby('fran_id')['pts'].agg({lambda x: np.percentile(x,0.25), lambda x: np.percentile(x,0.5), lambda x: np.percentile(x,0.75)})\n",
    "print(f\"Percentile Statistics for 25th, 50th and 75th percentile:{grouped}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 6\n",
    "We'd like to know if teams tend to win more games at home compared to away.\n",
    "\n",
    "The variable, `game_result`, indicates whether a team won a particular game ('W' stands for “win” and 'L' stands for “loss”). The variable, `game_location`, indicates whether a team was playing at home or away ('H' stands for “home” and 'A' stands for “away”). \n",
    "\n",
    "Data scientists will often calculate a contingency table of frequencies to help them determine if categorical variables are associated. Calculate a table of frequencies that shows the counts of game_result and game_location.\n",
    "\n",
    "Save your result as `location_result_freq` and print your result. Based on this table, do you think the variables are associated?`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 680,
   "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>game_location</th>\n",
       "      <th>A</th>\n",
       "      <th>H</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_result</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>133</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>92</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "game_location    A    H\n",
       "game_result            \n",
       "L              133  105\n",
       "W               92  120"
      ]
     },
     "execution_count": 680,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing if teams win more games at home or away.\n",
    "#using the 2010 data\n",
    "counts_game_result = nba_2010['game_result']\n",
    "counts_game_location =  nba_2010['game_location']\n",
    "\n",
    "#using crosstab to calculate a contingency table.\n",
    "location_result_freq = pd.crosstab(counts_game_result,counts_game_location)\n",
    "location_result_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Based on this table alone it is not sufficiently proven that we can reject our null hypothesis of association between the two variables.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 7\n",
    "\n",
    "Convert this table of frequencies to a table of proportions and save the result as `location_result_proportions`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 683,
   "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>game_location</th>\n",
       "      <th>A</th>\n",
       "      <th>H</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>game_result</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>0.295556</td>\n",
       "      <td>0.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>0.204444</td>\n",
       "      <td>0.266667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "game_location         A         H\n",
       "game_result                      \n",
       "L              0.295556  0.233333\n",
       "W              0.204444  0.266667"
      ]
     },
     "execution_count": 683,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_result_proportions = location_result_freq/len(nba_2010)\n",
    "location_result_proportions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 8\n",
    "Using the contingency table created above (Task 6), calculate the expected contingency table (if there were no association) and the Chi-Square statistic.\n",
    "\n",
    "Does the actual contingency table look similar to the expected table — or different? Based on this output, do you think there is an association between these variables?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 685,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.501704455367053\n",
      "[[119. 119.]\n",
      " [106. 106.]]\n"
     ]
    }
   ],
   "source": [
    "chi2, pval, dof, expected = chi2_contingency(location_result_freq)\n",
    "print(chi2)\n",
    "print(expected)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*For a 2x2 table, Chi-squared greater than about 4 indicates an association. It seems teh value is greater than 4 indicating an association. However, the expected and observed values are quite different.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 9\n",
    "\n",
    "For each game, 538 has calculated the probability that each team will win the game. We want to know if teams with a higher probability of winning (according to 538) also tend to win games by more points. \n",
    "\n",
    "In the data, 538's prediction is saved as `forecast`. The `point_diff` column gives the margin of victory/defeat for each team (positive values mean that the team won; negative values mean that they lost). \n",
    "\n",
    "Using `nba_2010`, calculate the covariance between `forecast` (538's projected win probability) and `point_diff` (the margin of victory/defeat) in the dataset. Save and print your result. Looking at the matrix, what is the covariance between these two variables?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 688,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The covariance of the variables is: 1.3746125676458796\n"
     ]
    }
   ],
   "source": [
    "# Calculating the covariance.\n",
    "cov = np.cov(nba_2010.forecast,nba_2010.point_diff)\n",
    "print(f\"The covariance of the variables is: {cov[1][0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 10\n",
    "\n",
    "Because 538’s forecast variable is reported as a probability (not a binary), we can calculate the strength of the correlation.\n",
    "\n",
    "Using nba_2010, calculate the correlation between `forecast` and `point_diff`. Call this `point_diff_forecast_corr`. Save and print your result. Does this value suggest an association between the two variables?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 690,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.4402088708468084, pvalue=9.410391573139194e-23)"
      ]
     },
     "execution_count": 690,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Calculate the strength of the correlation.\n",
    "point_diff_forecast_corr = pearsonr(nba_2010.forecast,nba_2010.point_diff)\n",
    "point_diff_forecast_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*This seems to suggest an association between the two variables.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 11\n",
    "\n",
    "Generate a scatter plot of `forecast` (on the x-axis) and `point_diff` (on the y-axis). Does the correlation value make sense?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 693,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "sns.scatterplot(data=nba_2010,x='forecast',y='point_diff',hue='game_location')\n",
    "plt.xlabel('Forecasted Win Prob.')\n",
    "plt.ylabel('Point Differential')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Despite the outliers we can suggest that there is a weak positive correlation between the forecasted and actual point difference observations.*"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}