PY0101EN-5.1_Intro_API.ipynb

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    "<center>\n",
    "    <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n",
    "\n",
    "# Application Programming Interface\n",
    "\n",
    "Estaimted time needed: **15** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After complting this lab you will be able to:\n",
    "\n",
    "-   Create and Use APIs in Python\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction\n",
    "\n",
    "An API lets two pieces of software talk to each other. Just like a function,  you don’t have to know how the API works only its inputs and outputs.  An essential type of API is a REST API that allows you to access resources via the internet. In this lab, we will review the Pandas Library  in the context of an API, we will also review a basic REST API  \n"
   ]
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   "source": [
    "## Table of Contents\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "<li><a href=\"#ref0\">Pandas is an API</a></li>\n",
    "<li><a href=\"#ref1\">REST APIs Basics  </a></li>\n",
    "<li><a href=\"#ref2\">Quiz on Tuples</a></li>\n",
    "\n",
    "<p></p>\n",
    "Estimated Time Needed: <strong>15 min</strong>\n",
    "</div>\n",
    "\n",
    "<hr>\n"
   ]
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     "text": [
      "Collecting nba_api\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f0/07/d32f5106c95fbee8e54b22d2795f94c2d2213ed6d2e5caac390b56667d37/nba_api-1.1.9-py3-none-any.whl (242kB)\n",
      "\u001b[K     |████████████████████████████████| 245kB 11.3MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: requests in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from nba_api) (2.24.0)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->nba_api) (2.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->nba_api) (2020.6.20)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->nba_api) (1.25.10)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests->nba_api) (3.0.4)\n",
      "Installing collected packages: nba-api\n",
      "Successfully installed nba-api-1.1.9\n"
     ]
    }
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   "source": [
    "!pip install nba_api"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"PandasAPI\">Pandas is an API </h2>\n"
   ]
  },
  {
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   "source": [
    "You will use this function in the lab:\n"
   ]
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   "source": [
    "def one_dict(list_dict):\n",
    "    keys=list_dict[0].keys()\n",
    "    out_dict={key:[] for key in keys}\n",
    "    for dict_ in list_dict:\n",
    "        for key, value in dict_.items():\n",
    "            out_dict[key].append(value)\n",
    "    return out_dict    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"PandasAPI\">Pandas is an API </h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is actually set of software components , much of  which is not even written in Python.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You create a dictionary, this is just data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [],
   "source": [
    "dict_={'a':[11,21,31],'b':[12,22,32]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you create a Pandas object with the Dataframe constructor in API lingo, this is an \"instance\". The data in the dictionary is passed along to the pandas API. You then use the dataframe to communicate with the API.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(dict_)\n",
    "type(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%206/images/pandas_api.png\" width = 800, align = \"center\" alt=\"logistic regression block diagram\" />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you call the method head the dataframe communicates with the API displaying the first few rows of the dataframe.\n"
   ]
  },
  {
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   "execution_count": 6,
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       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
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       "      <td>21</td>\n",
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       "    a   b\n",
       "0  11  12\n",
       "1  21  22\n",
       "2  31  32"
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   "source": [
    "df.head()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you call the method mean,the API will calculate the mean and return the value.\n"
   ]
  },
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   "execution_count": 7,
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     "data": {
      "text/plain": [
       "a    21.0\n",
       "b    22.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
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   "source": [
    "df.mean()"
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   "source": [
    "<h2 id=\"ref1\">REST APIs</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Rest API’s function by sending a <b>request</b>,  the request is communicated via HTTP message. The HTTP message usually contains a JSON file. This contains instructions for what operation we would like the service or <b>resource</b> to perform. In a similar manner, API returns a <b>response</b>, via an HTTP message, this response is usually contained within a JSON.</p>\n",
    "<p>In this lab, we will use the <a href=https://pypi.org/project/nba-api/>NBA API</a> to determine how well the Golden State Warriors performed against the Toronto Raptors. We will use the API do the determined number of points the Golden State Warriors won or lost by for each game. So if the value is three, the Golden State Warriors won by three points. Similarly it the  Golden State Warriors lost  by two points the result will be negative two. The API is relatively will handle a lot of the details such a Endpoints and Authentication </p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the nba api to make a request for a specific team, it's quite simple, we don't require a JSON all we require is an id. This information is stored locally in the API we import the module teams \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nba_api.stats.static import teams\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://pypi.org/project/nba-api/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method <code>get_teams()</code> returns a list of dictionaries  the dictionary key id has a unique identifier for each team as a value \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "nba_teams = teams.get_teams()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dictionary key id has a unique identifier for each team as a value, let's look at the first three elements of the list:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'id': 1610612737,\n",
       "  'full_name': 'Atlanta Hawks',\n",
       "  'abbreviation': 'ATL',\n",
       "  'nickname': 'Hawks',\n",
       "  'city': 'Atlanta',\n",
       "  'state': 'Atlanta',\n",
       "  'year_founded': 1949},\n",
       " {'id': 1610612738,\n",
       "  'full_name': 'Boston Celtics',\n",
       "  'abbreviation': 'BOS',\n",
       "  'nickname': 'Celtics',\n",
       "  'city': 'Boston',\n",
       "  'state': 'Massachusetts',\n",
       "  'year_founded': 1946},\n",
       " {'id': 1610612739,\n",
       "  'full_name': 'Cleveland Cavaliers',\n",
       "  'abbreviation': 'CLE',\n",
       "  'nickname': 'Cavaliers',\n",
       "  'city': 'Cleveland',\n",
       "  'state': 'Ohio',\n",
       "  'year_founded': 1970}]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_teams[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make things easier, we can convert the dictionary to a table. First, we use the function <code>one dict</code>, to create a dictionary. We use the common keys for each team as the keys,  the value is a list; each element of the list corresponds to the values for each team.\n",
    "We then convert the dictionary to a dataframe, each row contains the information for a different team.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>full_name</th>\n",
       "      <th>abbreviation</th>\n",
       "      <th>nickname</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1610612737</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Hawks</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>1949</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1610612738</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Celtics</td>\n",
       "      <td>Boston</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>1946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1610612739</td>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Cavaliers</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1610612740</td>\n",
       "      <td>New Orleans Pelicans</td>\n",
       "      <td>NOP</td>\n",
       "      <td>Pelicans</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1610612741</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>CHI</td>\n",
       "      <td>Bulls</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>1966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id             full_name abbreviation   nickname         city  \\\n",
       "0  1610612737         Atlanta Hawks          ATL      Hawks      Atlanta   \n",
       "1  1610612738        Boston Celtics          BOS    Celtics       Boston   \n",
       "2  1610612739   Cleveland Cavaliers          CLE  Cavaliers    Cleveland   \n",
       "3  1610612740  New Orleans Pelicans          NOP   Pelicans  New Orleans   \n",
       "4  1610612741         Chicago Bulls          CHI      Bulls      Chicago   \n",
       "\n",
       "           state  year_founded  \n",
       "0        Atlanta          1949  \n",
       "1  Massachusetts          1946  \n",
       "2           Ohio          1970  \n",
       "3      Louisiana          2002  \n",
       "4       Illinois          1966  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_nba_team=one_dict(nba_teams)\n",
    "dict_nba_team\n",
    "df_teams=pd.DataFrame(dict_nba_team)\n",
    "df_teams.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Will use the team's nickname to find the unique id, we can see the row that contains the warriors by using the column nickname as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
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    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>full_name</th>\n",
       "      <th>abbreviation</th>\n",
       "      <th>nickname</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1610612744</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Warriors</td>\n",
       "      <td>Golden State</td>\n",
       "      <td>California</td>\n",
       "      <td>1946</td>\n",
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      "text/plain": [
       "           id              full_name abbreviation  nickname          city  \\\n",
       "7  1610612744  Golden State Warriors          GSW  Warriors  Golden State   \n",
       "\n",
       "        state  year_founded  \n",
       "7  California          1946  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_warriors=df_teams[df_teams['nickname']=='Warriors']\n",
    "df_warriors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can use the following line of code to access the first column of the dataframe:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "1610612744"
      ]
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     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "id_warriors=df_warriors[['id']].values[0][0]\n",
    "#we now have an integer that can be used   to request the Warriors information \n",
    "id_warriors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function \"League Game Finder \" will make an API call, its in the module <code>stats.endpoints</code> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nba_api.stats.endpoints import leaguegamefinder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameter <code>team_id_nullable</code> is the unique ID for the warriors. Under the hood, the NBA API is making a HTTP request.  \n",
    "The information requested is provided and is transmitted via an HTTP response this is assigned to the object <code>gamefinder</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
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   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-3b1fd35c0dc9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Since https://stats.nba.com does lot allow api calls from Cloud IPs and Skills Network Labs uses a Cloud IP.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# The following code is comment out, you can run it on jupyter labs on your own computer.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mgamefinder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mleaguegamefinder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLeagueGameFinder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mteam_id_nullable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mid_warriors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mgamefinder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/nba_api/stats/endpoints/leaguegamefinder.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, player_or_team_abbreviation, conference_nullable, date_from_nullable, date_to_nullable, division_simple_nullable, draft_number_nullable, draft_round_nullable, draft_team_id_nullable, draft_year_nullable, eq_ast_nullable, eq_blk_nullable, eq_dd_nullable, eq_dreb_nullable, eq_fg3a_nullable, eq_fg3m_nullable, eq_fg3_pct_nullable, eq_fga_nullable, eq_fgm_nullable, eq_fg_pct_nullable, eq_fta_nullable, eq_ftm_nullable, eq_ft_pct_nullable, eq_minutes_nullable, eq_oreb_nullable, eq_pf_nullable, eq_pts_nullable, eq_reb_nullable, eq_stl_nullable, eq_td_nullable, eq_tov_nullable, game_id_nullable, gt_ast_nullable, gt_blk_nullable, gt_dd_nullable, gt_dreb_nullable, gt_fg3a_nullable, gt_fg3m_nullable, gt_fg3_pct_nullable, gt_fga_nullable, gt_fgm_nullable, gt_fg_pct_nullable, gt_fta_nullable, gt_ftm_nullable, gt_ft_pct_nullable, gt_minutes_nullable, gt_oreb_nullable, gt_pf_nullable, gt_pts_nullable, gt_reb_nullable, gt_stl_nullable, gt_td_nullable, gt_tov_nullable, league_id_nullable, location_nullable, lt_ast_nullable, lt_blk_nullable, lt_dd_nullable, lt_dreb_nullable, lt_fg3a_nullable, lt_fg3m_nullable, lt_fg3_pct_nullable, lt_fga_nullable, lt_fgm_nullable, lt_fg_pct_nullable, lt_fta_nullable, lt_ftm_nullable, lt_ft_pct_nullable, lt_minutes_nullable, lt_oreb_nullable, lt_pf_nullable, lt_pts_nullable, lt_reb_nullable, lt_stl_nullable, lt_td_nullable, lt_tov_nullable, outcome_nullable, po_round_nullable, player_id_nullable, rookie_year_nullable, season_nullable, season_segment_nullable, season_type_nullable, starter_bench_nullable, team_id_nullable, vs_conference_nullable, vs_division_nullable, vs_team_id_nullable, years_experience_nullable, proxy, headers, timeout, get_request)\u001b[0m\n\u001b[1;32m    202\u001b[0m         }\n\u001b[1;32m    203\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mget_request\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/nba_api/stats/endpoints/leaguegamefinder.py\u001b[0m in \u001b[0;36mget_request\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0mproxy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproxy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m             \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m             \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    213\u001b[0m         )\n\u001b[1;32m    214\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/nba_api/library/http.py\u001b[0m in \u001b[0;36msend_api_request\u001b[0;34m(self, endpoint, parameters, referer, proxy, headers, timeout, raise_exception_on_error)\u001b[0m\n\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcontents\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbase_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest_headers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproxies\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mproxies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    131\u001b[0m             \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m             \u001b[0mstatus_code\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/requests/api.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'allow_redirects'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'get'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    528\u001b[0m         }\n\u001b[1;32m    529\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 530\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    532\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    642\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 643\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    645\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    447\u001b[0m                     \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m                     \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    450\u001b[0m                 )\n\u001b[1;32m    451\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    675\u001b[0m                 \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    676\u001b[0m                 \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 677\u001b[0;31m                 \u001b[0mchunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mchunked\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    678\u001b[0m             )\n\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    424\u001b[0m                     \u001b[0;31m# Python 3 (including for exceptions like SystemExit).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    425\u001b[0m                     \u001b[0;31m# Otherwise it looks like a bug in the code.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 426\u001b[0;31m                     \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    427\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSocketError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    428\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mread_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/urllib3/packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    419\u001b[0m                 \u001b[0;31m# Python 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m                     \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    422\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    423\u001b[0m                     \u001b[0;31m# Remove the TypeError from the exception chain in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1362\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1363\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1364\u001b[0;31m                 \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1365\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1366\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    305\u001b[0m         \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m             \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m         \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    269\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"status line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    584\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1010\u001b[0m                   \u001b[0;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1011\u001b[0m                   self.__class__)\n\u001b[0;32m-> 1012\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1013\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m    872\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Read on closed or unwrapped SSL socket.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 874\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    875\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mSSLError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    876\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mSSL_ERROR_EOF\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuppress_ragged_eofs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/conda/envs/python/lib/python3.6/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m    629\u001b[0m         \"\"\"\n\u001b[1;32m    630\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 631\u001b[0;31m             \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    632\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m             \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Since https://stats.nba.com does lot allow api calls from Cloud IPs and Skills Network Labs uses a Cloud IP.\n",
    "# The following code is comment out, you can run it on jupyter labs on your own computer.\n",
    "gamefinder = leaguegamefinder.LeagueGameFinder(team_id_nullable=id_warriors)\n",
    "\n",
    "gamefinder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see the json file by running the following line of code. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'gamefinder' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-fd5a55f068d8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Since https://stats.nba.com does lot allow api calls from Cloud IPs and Skills Network Labs uses a Cloud IP.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# The following code is comment out, you can run it on jupyter labs on your own computer.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mgamefinder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'gamefinder' is not defined"
     ]
    }
   ],
   "source": [
    "# Since https://stats.nba.com does lot allow api calls from Cloud IPs and Skills Network Labs uses a Cloud IP.\n",
    "# The following code is comment out, you can run it on jupyter labs on your own computer.\n",
    "gamefinder.get_json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The game finder object has a method <code>get_data_frames()</code>, that returns a dataframe.  If we view the dataframe, we can see it contains information about all the games the Warriors played.  The <code>PLUS_MINUS</code> column contains information on the score, if the value is negative the Warriors lost by that many points, if the value is positive, the warriors one by that amount of points. The column <code>MATCHUP </code>had the team the Warriors were playing, GSW stands for Golden State Warriors and TOR means Toronto Raptors; <code>vs</code> signifies it was a home game and the <code>@ </code>symbol means an away game.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since https://stats.nba.com does lot allow api calls from Cloud IPs and Skills Network Labs uses a Cloud IP.\n",
    "# The following code is comment out, you can run it on jupyter labs on your own computer.\n",
    "# games = gamefinder.get_data_frames()[0]\n",
    "# games.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can download the dataframe from the API call for Golden State and run the rest like a video.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-09-21 09:35:17--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%205/Labs/Golden_State.pkl\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 811065 (792K) [application/octet-stream]\n",
      "Saving to: ‘Golden_State.pkl’\n",
      "\n",
      "Golden_State.pkl    100%[===================>] 792.06K  3.60MB/s    in 0.2s    \n",
      "\n",
      "2020-09-21 09:35:18 (3.60 MB/s) - ‘Golden_State.pkl’ saved [811065/811065]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "! wget https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%205/Labs/Golden_State.pkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>SEASON_ID</th>\n",
       "      <th>TEAM_ID</th>\n",
       "      <th>TEAM_ABBREVIATION</th>\n",
       "      <th>TEAM_NAME</th>\n",
       "      <th>GAME_ID</th>\n",
       "      <th>GAME_DATE</th>\n",
       "      <th>MATCHUP</th>\n",
       "      <th>WL</th>\n",
       "      <th>MIN</th>\n",
       "      <th>PTS</th>\n",
       "      <th>...</th>\n",
       "      <th>FT_PCT</th>\n",
       "      <th>OREB</th>\n",
       "      <th>DREB</th>\n",
       "      <th>REB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PLUS_MINUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22019</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>1521900066</td>\n",
       "      <td>2019-07-12</td>\n",
       "      <td>GSW vs. LAL</td>\n",
       "      <td>L</td>\n",
       "      <td>200</td>\n",
       "      <td>87</td>\n",
       "      <td>...</td>\n",
       "      <td>0.800</td>\n",
       "      <td>13.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>13</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>21</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22019</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>1521900058</td>\n",
       "      <td>2019-07-10</td>\n",
       "      <td>GSW @ DEN</td>\n",
       "      <td>W</td>\n",
       "      <td>201</td>\n",
       "      <td>73</td>\n",
       "      <td>...</td>\n",
       "      <td>0.867</td>\n",
       "      <td>7.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20</td>\n",
       "      <td>-8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22019</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>1521900039</td>\n",
       "      <td>2019-07-08</td>\n",
       "      <td>GSW @ LAL</td>\n",
       "      <td>W</td>\n",
       "      <td>200</td>\n",
       "      <td>88</td>\n",
       "      <td>...</td>\n",
       "      <td>0.621</td>\n",
       "      <td>8.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>21</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "      <td>13.0</td>\n",
       "      <td>22</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22019</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>1521900020</td>\n",
       "      <td>2019-07-07</td>\n",
       "      <td>GSW vs. TOR</td>\n",
       "      <td>W</td>\n",
       "      <td>201</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>0.923</td>\n",
       "      <td>6.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>18</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>25</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22019</td>\n",
       "      <td>1610612744</td>\n",
       "      <td>GSW</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>1521900007</td>\n",
       "      <td>2019-07-05</td>\n",
       "      <td>GSW vs. CHA</td>\n",
       "      <td>L</td>\n",
       "      <td>200</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>0.889</td>\n",
       "      <td>8.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>19</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15</td>\n",
       "      <td>-8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  SEASON_ID     TEAM_ID TEAM_ABBREVIATION              TEAM_NAME     GAME_ID  \\\n",
       "0     22019  1610612744               GSW  Golden State Warriors  1521900066   \n",
       "1     22019  1610612744               GSW  Golden State Warriors  1521900058   \n",
       "2     22019  1610612744               GSW  Golden State Warriors  1521900039   \n",
       "3     22019  1610612744               GSW  Golden State Warriors  1521900020   \n",
       "4     22019  1610612744               GSW  Golden State Warriors  1521900007   \n",
       "\n",
       "    GAME_DATE      MATCHUP WL  MIN  PTS  ...  FT_PCT  OREB  DREB   REB  AST  \\\n",
       "0  2019-07-12  GSW vs. LAL  L  200   87  ...   0.800  13.0  29.0  42.0   13   \n",
       "1  2019-07-10    GSW @ DEN  W  201   73  ...   0.867   7.0  27.0  34.0   10   \n",
       "2  2019-07-08    GSW @ LAL  W  200   88  ...   0.621   8.0  29.0  37.0   21   \n",
       "3  2019-07-07  GSW vs. TOR  W  201   80  ...   0.923   6.0  37.0  43.0   18   \n",
       "4  2019-07-05  GSW vs. CHA  L  200   85  ...   0.889   8.0  28.0  36.0   19   \n",
       "\n",
       "    STL  BLK   TOV  PF  PLUS_MINUS  \n",
       "0  10.0    3  11.0  21         3.2  \n",
       "1  11.0    7  20.0  20        -8.0  \n",
       "2  10.0    4  13.0  22         8.0  \n",
       "3   8.0    3  20.0  25        10.0  \n",
       "4   9.0    3  13.0  15        -8.0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name = \"Golden_State.pkl\"\n",
    "games = pd.read_pickle(file_name)\n",
    "games.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create two dataframes, one  for the games that the Warriors faced the raptors at home and the second for away games.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "games_home=games [games ['MATCHUP']=='GSW vs. TOR']\n",
    "games_away=games [games ['MATCHUP']=='GSW @ TOR']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can calculate the mean for the column  <code>PLUS_MINUS</code> for the dataframes  <code>games_home</code> and <code> games_away</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.730769230769231"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games_home.mean()['PLUS_MINUS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6071428571428571"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "games_away.mean()['PLUS_MINUS']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot out the <code>PLUS MINUS</code> column for  for the dataframes  <code>games_home</code> and <code> games_away</code>.\n",
    "We see the warriors played better at home.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/pandas/plotting/_matplotlib/core.py:1192: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(xticklabels)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "games_away.plot(x='GAME_DATE',y='PLUS_MINUS', ax=ax)\n",
    "games_home.plot(x='GAME_DATE',y='PLUS_MINUS', ax=ax)\n",
    "ax.legend([\"away\", \"home\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <a href=\"https://cloud.ibm.com/catalog/services/watson-studio\"><img src = \"https://ibm.box.com/shared/static/irypdxea2q4th88zu1o1tsd06dya10go.png\" width = 750, align = \"center\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors:\n",
    "\n",
    " [Joseph Santarcangelo](https://www.linkedin.com/in/joseph-s-50398b136?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork-19487395&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork-19487395&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) \n",
    "\n",
    "Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description                 |\n",
    "| ----------------- | ------- | ------------- | ---------------------------------- |\n",
    "| 2020-09-09        | 2.1     | Malika Singla | Spell Check                        |\n",
    "| 2020-08-26        | 2.0     | Lavanya       | Moved lab to course repo in GitLab |\n",
    "|                   |         |               |                                    |\n",
    "|                   |         |               |                                    |\n",
    "\n",
    "<hr/>\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}