001-FinalProject.ipynb

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   "source": [
    "# COGS 108 - Final Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group Members and Contributions\n",
    "\n",
    "(Note: PIDs removed for anonymity*)\n",
    "\n",
    " - XXXXX - Part 3 Data Cleaning, Part 4 Visualizations, Miscellaneous\n",
    " \n",
    " - XXXXX - Part 1 Intro & Background, Part 2 Data Wrangling, gathering + loading .csv files, Part 3 Data Cleaning, and Part 6 Privacy concerns \n",
    "     \n",
    " - XXXXX - Part 1 Intro & Background, Part 2 Data Wrangling, Part 3 Data Cleaning, Part 4 Data Visualization\n",
    " \n",
    " - XXXXX - Part 3 Data Cleaning, Part 4 Visualizations, Miscellaneous\n",
    " \n",
    " - XXXXX - Part 5 Data Analysis, Part 7 Conclusion and Discussion, Miscellaneous\n",
    " \n",
    " - XXXXX - Part 5 Data Analysis, Part 7 Conclusion and Discussion, Miscellaneous\n"
   ]
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   "source": [
    "## Part 1 - Introduction and Background \n",
    "\n"
   ]
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    "The emergence of the Golden State Warriors as a powerhouse team in the NBA has opened up a major dialogue surrounding the recent increase in influence of the 3 point shot in the NBA. There are a multitude of articles that suggest that the 3-pointer plays a crucial role in today's style of basketball. This assertion is an important one because at the time of it's inception many players and coaches wrote the 3-point shot off as a \"gimmick\" and chose to continue prioritizing 2-pointers in their gameplay. Additionally, this recent shift away from close range shots has also affected which positions are important, which has been discussed at length by NBA announcers and analysts.\n",
    "\n",
    "This topic is of particular significance to our group because we all share a love for sports and as fans we are interested in combining sports statistics with data science applications. \n",
    "\n",
    "We are curious to see if the data and our conclusions supports the idea that 3 pointer driven gameplay, as practiced by the Warriors, has really revolutionized the game of basketball. Mathematically speaking, there is a major incentive to apply the 3 point focused game strategy. Today, many recognize that taking the shot that is worth 50% more will have a pay-off even if the shot itself is a little more difficult to make (lower 3 point percentage than 2 point percentage). However, we want to see whether or not real world data supports this statistical claim and what, if any, new or alternate conclusions we can make based on our findings. "
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    "### Revised Research Question\n",
    "\n",
    "Through our analysis of NBA team statisitcs we seek to determine which game strategy is more effective, and explore the ways in which the best strategy has changed throughout NBA history. The specific game strategies we will be examining in this project are 3 point versus 2 point heavy game styles. The comparison of these two strategies will be normalized for each season because we know that initially 3 point shots were not commonly attempted and have increased in popularity over time. We are defining \"effectiveness\" as how many 3 pt shot attempts each playoff team had per season, compared to the league average that season. "
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    "### Hypothesis \n",
    "\n",
    "We predict that since the introduction of the 3 point shot in 1979 the best game strategy has become taking more 3 point shots. As the league average of 3 point shots attempted has steadily increased it make sense that in order to win games regularly a team has to adopt a 3 point driven strategy. \n",
    "\n",
    "It will be fascinating to see at which point there is a shift away from 2 point focused gameplay to 3 point focused gameplay in the NBA as a whole and how long it took for individual teams to make the shift. With our results we might also be able to establish an optimal balance between 3-point and 2-point shots (a combination of the two strategies) that would lead to the highest effectiveness in the NBA today. We predict that given the nature of gameplay seen in the NBA today this ideal strategy will most likely be more focused on attempting 3-pointers than close range shots. "
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   "source": [
    "## Part 2 - Data Wrangling \n",
    "\n",
    "The bulk of our data is sourced from https://www.basketball-reference.com/. We will need to gather a table of data for every season in order to effectively analyze the evolution of basketball strategies over time. We will be exploring the data for each season by team starting in 1979 (the year the 3 point shot was introduced). Basketball-Reference provides tables that include "
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      "/Users/anitayen/anaconda3/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "# Important Imports! \n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "import os  "
   ]
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     "text": [
      "      Rk                    Team   G     MP    FG   FGA    FG%   3P  3PA  \\\n",
      "0    1.0          Denver Nuggets  82  241.5  48.0  97.4  0.493  0.9  3.1   \n",
      "1    2.0       San Antonio Spurs  82  243.7  47.7  94.2  0.506  1.0  3.2   \n",
      "2    3.0         Detroit Pistons  82  242.7  46.3  96.5  0.480  0.4  1.7   \n",
      "3    4.0      Los Angeles Lakers  82  241.2  47.0  88.4  0.532  0.7  2.8   \n",
      "4    5.0               Utah Jazz  82  241.2  44.0  88.3  0.498  1.2  3.9   \n",
      "5    6.0  Portland Trail Blazers  82  241.5  44.3  87.7  0.505  0.3  1.6   \n",
      "6    7.0          Boston Celtics  82  243.4  44.1  88.2  0.500  0.7  2.8   \n",
      "7    8.0            Phoenix Suns  82  241.2  44.8  88.0  0.509  0.9  3.5   \n",
      "8    9.0      San Diego Clippers  82  240.6  44.3  89.3  0.496  0.3  1.6   \n",
      "9   10.0         Houston Rockets  82  240.9  45.5  91.9  0.495  0.4  1.9   \n",
      "10  11.0        Dallas Mavericks  82  243.4  44.1  88.2  0.500  0.5  2.2   \n",
      "11  12.0       Kansas City Kings  82  240.9  42.9  88.2  0.486  0.6  2.3   \n",
      "12  13.0         New Jersey Nets  82  241.5  44.1  88.5  0.498  0.6  2.8   \n",
      "13  14.0   Golden State Warriors  82  241.8  42.9  91.9  0.467  0.7  2.8   \n",
      "14  15.0     Seattle SuperSonics  82  241.2  42.2  86.4  0.488  0.3  1.7   \n",
      "15  16.0      Philadelphia 76ers  82  243.4  41.3  83.3  0.495  0.4  1.3   \n",
      "16  17.0         New York Knicks  82  242.1  41.3  83.4  0.495  0.6  2.0   \n",
      "17  18.0         Milwaukee Bucks  82  242.7  41.9  85.0  0.492  0.7  2.8   \n",
      "18  19.0          Indiana Pacers  82  241.2  42.0  87.0  0.483  0.6  2.5   \n",
      "19  20.0           Chicago Bulls  82  243.7  40.3  85.0  0.474  0.2  1.4   \n",
      "20  21.0      Washington Bullets  82  242.7  40.8  84.2  0.484  0.9  3.4   \n",
      "21  22.0     Cleveland Cavaliers  82  242.1  41.0  88.2  0.465  0.5  2.0   \n",
      "22  23.0           Atlanta Hawks  82  242.1  39.4  83.0  0.474  0.3  1.3   \n",
      "23   NaN          League Average  82  242.0  43.5  88.4  0.492  0.6  2.4   \n",
      "\n",
      "      3P%  ...      FT%   ORB   DRB   TRB   AST   STL  BLK   TOV    PF    PTS  \n",
      "0   0.302  ...    0.818  13.8  29.8  43.6  30.3   8.7  4.3  16.4  27.8  123.7  \n",
      "1   0.300  ...    0.755  15.0  30.8  45.8  28.8   8.4  6.0  17.6  26.2  120.3  \n",
      "2   0.227  ...    0.775  17.4  29.7  47.1  27.5   8.5  5.1  16.0  26.5  117.1  \n",
      "3   0.257  ...    0.754  13.4  30.5  43.8  29.9   8.9  5.8  19.2  25.0  115.6  \n",
      "4   0.319  ...    0.781  13.4  30.8  44.1  27.2   8.5  7.4  18.4  24.1  115.0  \n",
      "5   0.194  ...    0.754  15.3  26.8  42.0  25.4   9.9  4.8  18.1  26.0  113.1  \n",
      "6   0.240  ...    0.792  14.1  31.0  45.1  25.9   8.2  5.2  17.3  23.8  112.1  \n",
      "7   0.254  ...    0.759  13.0  28.0  41.0  27.0   8.5  4.7  17.7  26.2  111.0  \n",
      "8   0.188  ...    0.736  15.9  29.0  45.0  24.2   6.9  4.7  18.5  24.6  110.7  \n",
      "9   0.195  ...    0.740  14.6  30.3  44.9  26.9   7.6  6.3  19.0  28.3  110.6  \n",
      "10  0.228  ...    0.755  13.3  27.6  40.9  26.4   7.1  4.4  15.9  23.2  110.4  \n",
      "11  0.275  ...    0.777  14.0  27.7  41.7  27.2   8.7  4.7  18.3  26.8  110.0  \n",
      "12  0.211  ...    0.700  14.9  28.2  43.1  26.2   9.9  6.1  19.6  27.4  110.0  \n",
      "13  0.243  ...    0.743  17.0  26.5  43.4  22.4  10.1  4.2  18.5  25.7  109.9  \n",
      "14  0.193  ...    0.780  13.0  28.4  41.4  27.2   7.8  4.3  16.6  23.0  108.1  \n",
      "15  0.271  ...    0.754  14.4  29.0  43.5  24.8   9.8  8.0  19.9  24.9  107.8  \n",
      "16  0.285  ...    0.775  13.3  27.2  40.5  24.9   9.8  4.4  19.4  27.8  106.9  \n",
      "17  0.254  ...    0.740  13.8  29.1  42.9  25.8   7.8  6.0  17.3  26.4  105.7  \n",
      "18  0.232  ...    0.766  12.2  29.2  41.5  26.5  10.2  4.9  18.6  25.1  104.5  \n",
      "19  0.171  ...    0.746  13.9  28.0  42.0  25.5   8.4  5.5  19.2  26.8  103.7  \n",
      "20  0.252  ...    0.756  12.5  29.1  41.6  26.7   6.8  3.9  17.7  24.3  102.7  \n",
      "21  0.262  ...    0.743  14.8  29.1  43.9  23.5   7.7  4.6  16.2  26.9  102.3  \n",
      "22  0.217  ...    0.761  13.6  27.2  40.8  22.3   7.6  6.8  16.2  25.5  101.5  \n",
      "23  0.250  ...    0.760  14.2  28.8  43.0  26.2   8.5  5.3  17.9  25.8  110.1  \n",
      "\n",
      "[24 rows x 25 columns]\n",
      "   Column1                 Column2  Column3 Column4 Column5     Column6  \\\n",
      "0      NaN                     NaN      NaN   Place   Place  Conference   \n",
      "1       Rk                    Team  Overall    Home    Road           E   \n",
      "2        1   Golden State Warriors     73-9    39-2    34-7        27-3   \n",
      "3        2       San Antonio Spurs    67-15    40-1   27-14        24-6   \n",
      "4        3     Cleveland Cavaliers    57-25    33-8   24-17       35-17   \n",
      "5        4         Toronto Raptors    56-26    32-9   24-17       39-13   \n",
      "6        5   Oklahoma City Thunder    55-27    32-9   23-18       18-12   \n",
      "7        6    Los Angeles Clippers    53-29   29-12   24-17        22-8   \n",
      "8        7           Atlanta Hawks    48-34   27-14   21-20       29-23   \n",
      "9        8          Boston Celtics    48-34   28-13   20-21       31-21   \n",
      "10       9       Charlotte Hornets    48-34   30-11   18-23       33-19   \n",
      "11      10              Miami Heat    48-34   28-13   20-21       31-21   \n",
      "12      11          Indiana Pacers    45-37   26-15   19-22       30-22   \n",
      "13      12         Detroit Pistons    44-38   26-15   18-23       29-23   \n",
      "14      13  Portland Trail Blazers    44-38   28-13   16-25       15-15   \n",
      "15      14        Dallas Mavericks    42-40   23-18   19-22       15-15   \n",
      "16      15       Memphis Grizzlies    42-40   26-15   16-25       17-13   \n",
      "17      16           Chicago Bulls    42-40   26-15   16-25       25-27   \n",
      "18      17         Houston Rockets    41-41   23-18   18-23       13-17   \n",
      "19      18      Washington Wizards    41-41   22-19   19-22       30-22   \n",
      "20      19               Utah Jazz    40-42   24-17   16-25       16-14   \n",
      "21      20           Orlando Magic    35-47   23-18   12-29       21-31   \n",
      "22      21          Denver Nuggets    33-49   18-23   15-26       15-15   \n",
      "23      22         Milwaukee Bucks    33-49   23-18   10-31       21-31   \n",
      "24      23        Sacramento Kings    33-49   18-23   15-26       14-16   \n",
      "25      24         New York Knicks    32-50   18-23   14-27       21-31   \n",
      "26      25    New Orleans Pelicans    30-52   21-20    9-32       10-20   \n",
      "27      26  Minnesota Timberwolves    29-53   14-27   15-26       11-19   \n",
      "28      27            Phoenix Suns    23-59   14-27    9-32        6-24   \n",
      "29      28           Brooklyn Nets    21-61   14-27    7-34       12-40   \n",
      "30      29      Los Angeles Lakers    17-65   12-29    5-36        9-21   \n",
      "31      30      Philadelphia 76ers    10-72    7-34    3-38        3-49   \n",
      "\n",
      "       Column7   Column8   Column9  Column10   ...     Column15 Column16  \\\n",
      "0   Conference  Division  Division  Division   ...     All-Star   Margin   \n",
      "1            W         A         C        SE   ...         Post       ?3   \n",
      "2         46-6       9-1       8-2      10-0   ...         25-5      7-2   \n",
      "3         43-9       9-1       7-3       8-2   ...         22-7      4-4   \n",
      "4         22-8      14-4       8-8      13-5   ...        19-11      4-7   \n",
      "5        17-13      14-2      11-7      14-4   ...         21-9      6-6   \n",
      "6        37-15       6-4       4-6       8-2   ...        15-13      8-6   \n",
      "7        31-21       7-3       6-4       9-1   ...        18-11      8-4   \n",
      "8        19-11      11-7      10-8       8-8   ...        17-10      5-7   \n",
      "9        17-13      10-6       9-9      12-6   ...        16-11      5-7   \n",
      "10       15-15      13-5      12-6       8-8   ...         21-8      6-5   \n",
      "11       17-13      10-8      11-7      10-6   ...        19-10      7-5   \n",
      "12       15-15      13-5       8-8       9-9   ...        17-12      9-9   \n",
      "13       15-15      11-7      10-6      8-10   ...        17-11      6-5   \n",
      "14       29-23       5-5       5-5       5-5   ...        17-11      2-7   \n",
      "15       27-25       8-2       4-6       3-7   ...        13-14     6-11   \n",
      "16       25-27       7-3       7-3       3-7   ...        11-18      8-3   \n",
      "17       17-13      12-6      10-6      3-15   ...        15-15     10-4   \n",
      "18       28-24       6-4       3-7       4-6   ...        14-13      5-8   \n",
      "19       11-19       9-9      11-7      10-6   ...        18-13      7-6   \n",
      "20       24-28       6-4       6-4       4-6   ...        14-16      6-8   \n",
      "21       14-16      12-6      5-13      4-12   ...        12-18     6-10   \n",
      "22       18-34       6-4       5-5       4-6   ...        11-17     10-5   \n",
      "23       12-18       9-9      4-12      8-10   ...        11-17      6-4   \n",
      "24       19-33       6-4       5-5       3-7   ...        11-18      8-4   \n",
      "25       11-19       8-8      7-11      6-12   ...         9-18      4-7   \n",
      "26       20-32       4-6       4-6       2-8   ...        10-19      5-5   \n",
      "27       18-34       5-5       2-8       4-6   ...        12-16      7-7   \n",
      "28       17-35       1-9       1-9       4-6   ...         9-19      4-5   \n",
      "29        9-21      6-10      4-14      2-16   ...         7-21      7-4   \n",
      "30        8-44       4-6       2-8       3-7   ...         6-21      2-7   \n",
      "31        7-23      2-14      0-18      1-17   ...         2-27      0-6   \n",
      "\n",
      "   Column17 Column18 Column19 Column20 Column21 Column22 Column23 Column24  \n",
      "0    Margin    Month    Month    Month    Month    Month    Month    Month  \n",
      "1       ?10      Oct      Nov      Dec      Jan      Feb      Mar      Apr  \n",
      "2      44-5      3-0     16-0     11-2     14-2      9-1     15-2      5-2  \n",
      "3      44-6      1-1     13-3     14-2     11-2     11-1     13-3      4-3  \n",
      "4      32-8      2-1     11-3      8-5     13-3      8-5     11-5      4-3  \n",
      "5     28-10      2-0      9-7      9-6     12-2      7-4     11-5      6-2  \n",
      "6      33-5      2-0      9-7     12-3     13-3      6-5     11-5      2-4  \n",
      "7     28-15      3-0      7-8     11-5     11-3      7-4      8-8      6-1  \n",
      "8     29-17      2-1     10-7      9-5      6-9      6-5     12-4      3-3  \n",
      "9     26-13      1-1      9-7      8-6      9-8      9-3      7-7      5-2  \n",
      "10    25-17      0-2     10-5      7-7     6-11      7-3     13-3      5-3  \n",
      "11    24-20      1-1      9-5      8-7      9-8      6-5     10-5      5-3  \n",
      "12    21-13      0-3     11-2      7-9      7-8      6-7      8-7      6-1  \n",
      "13    21-20      3-0      6-9      9-6      7-8      6-6      9-6      4-3  \n",
      "14    23-21      1-2      6-9     7-10      9-5      9-2      8-8      4-2  \n",
      "15    19-18      1-1      9-7      9-5      9-9      4-6     5-10      5-2  \n",
      "16    15-23      2-1      8-7      8-8     10-4      7-4     6-10      1-6  \n",
      "17    14-19      2-1      8-4      8-7      8-8      4-8      8-9      4-3  \n",
      "18    14-18      0-2      7-9      9-7      9-7      4-6      8-8      4-2  \n",
      "19    22-22      2-1      4-7      8-8      7-8      8-6      7-9      5-2  \n",
      "20    23-13      2-1      6-7      6-9      7-8      7-6      9-7      3-4  \n",
      "21    12-20      0-2      9-6     10-5     2-12      5-7     6-11      3-4  \n",
      "22    11-21      1-1     5-11      6-9      6-9      5-7      9-8      1-4  \n",
      "23    12-27      0-2      7-9     6-10      7-8      5-6      6-9      2-5  \n",
      "24    11-19      1-2     6-10      5-8      8-7      4-7     6-11      3-4  \n",
      "25    14-23      2-1      6-9      7-8      8-9      2-9     5-10      2-4  \n",
      "26    11-23      0-3     4-10      6-9      8-6      5-7     5-12      2-5  \n",
      "27     9-23      2-0      6-9     4-12     2-14      5-6      6-9      4-3  \n",
      "28    10-32      2-1      6-8     4-14     2-12      1-9     5-11      3-4  \n",
      "29     6-34      0-3     4-10     5-10     3-13      5-7     4-11      0-7  \n",
      "30     4-44      0-2     2-12     4-13     3-14      2-8     5-10      1-6  \n",
      "31     5-41      0-2     0-16     3-13     4-10     1-11     1-14      1-6  \n",
      "\n",
      "[32 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "#Extracting Data Tables from Basketball-Reference\n",
    "path = './NBA/Seasons/'\n",
    "playoffs_path = './NBA/Playoffs/'\n",
    "#make a list containing all of the necessary .csv files\n",
    "filenames = (x for x in os.listdir(path) if x.endswith(\".csv\")) \n",
    "playoff_names = (x for x in os.listdir(playoffs_path) if x.endswith(\".csv\")) \n",
    "\n",
    "#load each data frame into a dict where the key is the name of the file e.g. \"Season_1979.csv\"\n",
    "seasons = {}\n",
    "for file in filenames:\n",
    "    seasons[file] = pd.read_csv(path + file)\n",
    "    \n",
    "playoffs = {}\n",
    "for file in playoff_names:\n",
    "    playoffs[file] = pd.read_csv(playoffs_path + file)\n",
    "\n",
    "#Print out an example to check \n",
    "print(seasons['Season_1983.csv'])\n",
    "print(playoffs['Playoffs_2015.csv'])\n"
   ]
  },
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    "**Note**: For simplicity of coding, the naming convention of each season is \"Season_####.csv\". This might seem countintuitive to some as basketball seasons are more commonly referred to using the starting year and finishing year (e.g. 1979-80 Season) because the season spans two different years over the winter. For our purposes, the name of the data frame will include only the starting year. For example, the key for the 1979-80 season will just be saved as Season_1979.csv. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3 - Data Cleaning and Pre-processing\n",
    "\n",
    "As you can see from printing out the dataframe above for the 1983 Season Statistics, we have a lot of unneccessary columns. The only columns of interest to us are: Rank (Rk), Team, Field goals (FG), Field Goal Attempts (FGA), Field Goal Percentage (FG%) 3 Pointers (3P), 3 Pointers Attempted (3PA), 3 Point Percentage (3P%), 2 Pointers (2P), 2 Pointers Attempted (2PA), 2 Point Percentage (2P%), and Points (PTS). \n",
    "\n",
    "This means we have to remove multiple columns from EVERY Season dataframe. The most efficient way to do this is to construct a method that can do this for us. This is possible because all of our dataframes are the same shape. The method takes in a dataframe as a parameter, removes the unneccessary columns and returns a simplified dataframe. \n",
    "\n",
    "Columns we need to remove: Assists (AST), Blocks (BLK), Steals (STL), Ofensive Rebounds (ORB), Defensive Rebounds (DRB),Total Rebounds (TRB), Turnovers (TOV), Minutes Played (MP), Games (G), Free Throws (FT), Free Throw Attempts (FTA), Free Throw Percentage (FT%) and Personal Fouls (PF). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Method Clean_Cols returns a dataframe with only the columns we want to analyze, drops unneccessary columns \n",
    "def Clean_Cols(df):  \n",
    "     return df.drop(['G', 'MP', 'ORB', 'DRB', 'TRB', 'AST', 'BLK', 'STL', 'TOV', 'FT', 'FTA', 'FT%', 'PF'], axis =1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Let's apply the Clean_Cols method to our data! \n",
    "#Iterate through the seasons dict and send the dataframe as a \n",
    "#parameter to the Clean_Cols method to remove the unwanted cols\n",
    "\n",
    "for key in seasons: \n",
    "    seasons[key] = Clean_Cols(seasons[key])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Rk                    Team    FG   FGA    FG%   3P  3PA    3P%    2P  \\\n",
      "0    1.0          Denver Nuggets  48.0  97.4  0.493  0.9  3.1  0.302  47.0   \n",
      "1    2.0       San Antonio Spurs  47.7  94.2  0.506  1.0  3.2  0.300  46.7   \n",
      "2    3.0         Detroit Pistons  46.3  96.5  0.480  0.4  1.7  0.227  45.9   \n",
      "3    4.0      Los Angeles Lakers  47.0  88.4  0.532  0.7  2.8  0.257  46.3   \n",
      "4    5.0               Utah Jazz  44.0  88.3  0.498  1.2  3.9  0.319  42.7   \n",
      "5    6.0  Portland Trail Blazers  44.3  87.7  0.505  0.3  1.6  0.194  44.0   \n",
      "6    7.0          Boston Celtics  44.1  88.2  0.500  0.7  2.8  0.240  43.4   \n",
      "7    8.0            Phoenix Suns  44.8  88.0  0.509  0.9  3.5  0.254  43.9   \n",
      "8    9.0      San Diego Clippers  44.3  89.3  0.496  0.3  1.6  0.188  44.0   \n",
      "9   10.0         Houston Rockets  45.5  91.9  0.495  0.4  1.9  0.195  45.1   \n",
      "10  11.0        Dallas Mavericks  44.1  88.2  0.500  0.5  2.2  0.228  43.6   \n",
      "11  12.0       Kansas City Kings  42.9  88.2  0.486  0.6  2.3  0.275  42.2   \n",
      "12  13.0         New Jersey Nets  44.1  88.5  0.498  0.6  2.8  0.211  43.5   \n",
      "13  14.0   Golden State Warriors  42.9  91.9  0.467  0.7  2.8  0.243  42.2   \n",
      "14  15.0     Seattle SuperSonics  42.2  86.4  0.488  0.3  1.7  0.193  41.9   \n",
      "15  16.0      Philadelphia 76ers  41.3  83.3  0.495  0.4  1.3  0.271  40.9   \n",
      "16  17.0         New York Knicks  41.3  83.4  0.495  0.6  2.0  0.285  40.7   \n",
      "17  18.0         Milwaukee Bucks  41.9  85.0  0.492  0.7  2.8  0.254  41.1   \n",
      "18  19.0          Indiana Pacers  42.0  87.0  0.483  0.6  2.5  0.232  41.5   \n",
      "19  20.0           Chicago Bulls  40.3  85.0  0.474  0.2  1.4  0.171  40.1   \n",
      "20  21.0      Washington Bullets  40.8  84.2  0.484  0.9  3.4  0.252  39.9   \n",
      "21  22.0     Cleveland Cavaliers  41.0  88.2  0.465  0.5  2.0  0.262  40.5   \n",
      "22  23.0           Atlanta Hawks  39.4  83.0  0.474  0.3  1.3  0.217  39.1   \n",
      "23   NaN          League Average  43.5  88.4  0.492  0.6  2.4  0.250  42.9   \n",
      "\n",
      "     2PA    2P%    PTS  \n",
      "0   94.2  0.499  123.7  \n",
      "1   91.0  0.514  120.3  \n",
      "2   94.7  0.485  117.1  \n",
      "3   85.7  0.540  115.6  \n",
      "4   84.5  0.506  115.0  \n",
      "5   86.1  0.511  113.1  \n",
      "6   85.4  0.508  112.1  \n",
      "7   84.5  0.520  111.0  \n",
      "8   87.8  0.502  110.7  \n",
      "9   90.0  0.501  110.6  \n",
      "10  86.0  0.507  110.4  \n",
      "11  85.9  0.492  110.0  \n",
      "12  85.7  0.507  110.0  \n",
      "13  89.1  0.474  109.9  \n",
      "14  84.7  0.494  108.1  \n",
      "15  82.0  0.499  107.8  \n",
      "16  81.4  0.500  106.9  \n",
      "17  82.2  0.501  105.7  \n",
      "18  84.4  0.491  104.5  \n",
      "19  83.6  0.479  103.7  \n",
      "20  80.8  0.494  102.7  \n",
      "21  86.2  0.470  102.3  \n",
      "22  81.7  0.478  101.5  \n",
      "23  86.0  0.499  110.1  \n"
     ]
    }
   ],
   "source": [
    "#Let's look at the modified dataframe for the 1983 Season! Now, it only has the columns we are interested in. \n",
    "print(seasons['Season_1983.csv'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to note that for each season the dataframe is organized by team ranking. For example, while the Denver Nuggets may appear in the 0 index for one season's dataframe, they may be located in the 11 index in another season. This means that when we are comparing the performanc of a certain team over time we must be cognizant of this organization and use the 'Team' column to access the correct data versus simply relying on a certain index number. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trying to see the effectivness of 3 pt shots throughout the years, calculate effectivness for each season and see if\n",
    "there has been a change. We will calculate the effectiveness by finding the percentage of 3pt shots for each team and\n",
    "comparing that to the league avg of 3pt shots that season. We will then see the effectivness of the 3pt shot\n",
    "correlating the three pt shot percentage to the win percentage for each team"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to be able to visualize the evolution of the NBA since 1980 it would be helpful to have a separate dataframe that holds the information regarding league averages per season. This way we can analyze the how strategies in the game itself, rather than per team, have changed over time. Luckily at the bottom of each dataframe we have in Seasons contains league averages for that year. So, all we need to do is extract that data from each year and add it to a new dataframe called 'League_avg'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The easiest way to extract the data from each season's data table is to iterate through the Seasons dict. \n",
    "\n",
    "# The 'get_league_data()' method below takes in a key called 'year' that corresponds to the key name for the season we want the data for. \n",
    "# Using the key we can extract the dataframe we need and then return the row of interest, the last row in each table, \n",
    "# which in our case is where the league average data is stored. \n",
    "\n",
    "def get_league_data (year):    \n",
    "    df = seasons['Season_' + str(year) + '.csv']\n",
    "   \n",
    "    #add the year as a column to the data frame\n",
    "    df['Year'] = year \n",
    "    return (df[-1:])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Rk            Team    FG   FGA    FG%   3P  3PA    3P%    2P   2PA    2P%  \\\n",
      "22 NaN  League Average  43.6  90.6  0.481  0.8  2.8  0.280  42.9  87.9  0.488   \n",
      "23 NaN  League Average  43.0  88.4  0.486  0.5  2.0  0.245  42.5  86.4  0.491   \n",
      "23 NaN  League Average  43.3  88.2  0.491  0.6  2.3  0.262  42.7  86.0  0.497   \n",
      "23 NaN  League Average  43.5  89.7  0.485  0.5  2.3  0.238  43.0  87.4  0.492   \n",
      "23 NaN  League Average  43.5  88.4  0.492  0.6  2.4  0.250  42.9  86.0  0.499   \n",
      "23 NaN  League Average  43.8  89.1  0.491  0.9  3.1  0.282  42.9  86.0  0.499   \n",
      "23 NaN  League Average  43.2  88.6  0.487  0.9  3.3  0.282  42.3  85.3  0.495   \n",
      "23 NaN  League Average  42.6  88.8  0.480  1.4  4.7  0.301  41.2  84.1  0.490   \n",
      "23 NaN  League Average  42.1  87.7  0.480  1.6  5.0  0.316  40.6  82.7  0.490   \n",
      "25 NaN  League Average  42.5  89.0  0.477  2.1  6.6  0.323  40.4  82.4  0.490   \n",
      "\n",
      "      PTS  Year  \n",
      "22  109.3  1979  \n",
      "23  108.1  1980  \n",
      "23  108.6  1981  \n",
      "23  108.5  1982  \n",
      "23  110.1  1983  \n",
      "23  110.8  1984  \n",
      "23  110.2  1985  \n",
      "23  109.9  1986  \n",
      "23  108.2  1987  \n",
      "25  109.2  1988  \n"
     ]
    }
   ],
   "source": [
    "#Now that we have a method to extract the data we can go through the years from 1979 to 2016 \n",
    "# and add the results to our 'League_avg' dataframe. \n",
    "\n",
    "League_avg = pd.DataFrame()\n",
    "\n",
    "for year in range(1979, 2017):\n",
    "    League_avg = League_avg.append(get_league_data(year)) \n",
    "\n",
    "#Let's peek at what we have \n",
    "print(League_avg[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# There are a couple of unneeded columns when it comes to reporting the league average (Rk & Team)\n",
    "# We remove them below and reorder the columns. \n",
    "# Also the indexes are a little funky so let's make the index equal to the year. \n",
    "\n",
    "League_avg = League_avg.drop(['Rk', 'Team'], axis = 1)\n",
    "\n",
    "#in case we need data with year as a value\n",
    "League_avg2 = League_avg\n",
    "\n",
    "League_avg = League_avg.set_index('Year', drop=True)\n",
    "League_avg = League_avg[['FG','FGA', 'FG%', '3P', '3PA', '3P%', '2P', '2PA', '2P%', 'PTS']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        FG   FGA    FG%   3P  3PA    3P%    2P   2PA    2P%    PTS\n",
      "Year                                                              \n",
      "1979  43.6  90.6  0.481  0.8  2.8  0.280  42.9  87.9  0.488  109.3\n",
      "1980  43.0  88.4  0.486  0.5  2.0  0.245  42.5  86.4  0.491  108.1\n",
      "1981  43.3  88.2  0.491  0.6  2.3  0.262  42.7  86.0  0.497  108.6\n",
      "1982  43.5  89.7  0.485  0.5  2.3  0.238  43.0  87.4  0.492  108.5\n",
      "1983  43.5  88.4  0.492  0.6  2.4  0.250  42.9  86.0  0.499  110.1\n",
      "1984  43.8  89.1  0.491  0.9  3.1  0.282  42.9  86.0  0.499  110.8\n",
      "1985  43.2  88.6  0.487  0.9  3.3  0.282  42.3  85.3  0.495  110.2\n",
      "1986  42.6  88.8  0.480  1.4  4.7  0.301  41.2  84.1  0.490  109.9\n",
      "1987  42.1  87.7  0.480  1.6  5.0  0.316  40.6  82.7  0.490  108.2\n",
      "1988  42.5  89.0  0.477  2.1  6.6  0.323  40.4  82.4  0.490  109.2\n"
     ]
    }
   ],
   "source": [
    "#Everything should be cleaned up now\n",
    "print(League_avg[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing using the year is quick and intuitive. Now the league average values for each year are all organized into one cohesive dataframe! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#playoffs = pd.DataFrame([playoffs], columns = playoffs.keys())\n",
    "\n",
    "#This method allows us to take only the rank and team name for the top 16 teams when we are given a dataframe. We will\n",
    "#use the playoffs dictionary that we got from reading all the csv files.\n",
    "def clean_playoffs(copy):    \n",
    "    copy.columns = copy.iloc[1] #the first line is a filler, so we drop it\n",
    "    copy = copy.iloc[2:] \n",
    "    copy = copy.reset_index() #reset indexing\n",
    "    copy = copy[['Rk', 'Team']] #we dont need any other column\n",
    "    copy = copy.drop(copy.index[16:]) #only top 16 teams\n",
    "    return copy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#We have to edit the playoffs page for each year\n",
    "for key in playoffs:\n",
    "    playoffs[key] = clean_playoffs(playoffs[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1   Rk                    Team\n",
      "0    1       San Antonio Spurs\n",
      "1    2      Los Angeles Lakers\n",
      "2    3      Philadelphia 76ers\n",
      "3    4        Sacramento Kings\n",
      "4    5        Dallas Mavericks\n",
      "5    6               Utah Jazz\n",
      "6    7         Milwaukee Bucks\n",
      "7    8            Phoenix Suns\n",
      "8    9              Miami Heat\n",
      "9   10  Portland Trail Blazers\n",
      "10  11         New York Knicks\n",
      "11  12  Minnesota Timberwolves\n",
      "12  13         Toronto Raptors\n",
      "13  14       Charlotte Hornets\n",
      "14  15         Houston Rockets\n",
      "15  16     Seattle SuperSonics\n"
     ]
    }
   ],
   "source": [
    "#This is just to see what it looks like\n",
    "print(playoffs['Playoffs_2000.csv'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#We want to show effectiveness of the 3 pt shot. To show the \"effectiveness\" we will plot how many 3 pt \n",
    "#attempts top 16 team had compared to the league average that year. We will also weight according to how\n",
    "#each team ranked overall. For example, the best team would have more data points on the graph, compared to \n",
    "#the worst team recorded. There are a few reasons to use the top 16 teams. We originally planned on using playoff teams,\n",
    "#however 1) the number of playoff teams increased from 12 to 16, so we wanted to keep it consistent, 2) some seasons had teams\n",
    "#with worse records than other teams making the playoffs because of how the western and eastern conferences work.\n",
    "#We won't use amount of wins directly either, because some seasons had less total games because of various reasons.\n",
    "\n",
    "#We will use effectiveness to record the number of 3 pt attempts compared to the average each top 16 team has\n",
    "effectiveness = pd.DataFrame(columns = ['Year', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15'])\n",
    "effectiveness['Year'] = League_avg.index\n",
    "effectiveness = effectiveness.set_index('Year', drop = True)\n",
    "\n",
    "\n",
    "#We have to iterate through each playoff year, and get the data from the top 16 teams, and put it in our effectiveness dataframe\n",
    "for key in playoffs:\n",
    "    for index, row in playoffs[key].iterrows():\n",
    "        season = 'Season_'+str(key[9:13])+'.csv' #this is to get the key for the season data\n",
    "        data_index = seasons[season][seasons[season]['Team'] == row[1]].index[0] \n",
    "        effectiveness.set_value(index = int(key[9:13]), col = str(index), value = seasons[season][seasons[season]['Team'] == row[1]]['3PA'][data_index])\n",
    "    \n",
    "#Now, we have to subtract the league average from each data point in effectiveness\n",
    "for index, row in effectiveness.iterrows():\n",
    "    for i in range(0, 16):\n",
    "        row.loc[str(i)] = row.loc[str(i)] - League_avg.loc[index]['3PA']\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        0    1     2    3    4    5    6    7    8    9   10   11    12   13  \\\n",
      "Year                                                                           \n",
      "1979  2.3 -1.6  -1.3 -0.5  0.6 -1.9 -0.9 -1.4  1.8 -0.3  0.1 -0.5  -1.2 -0.5   \n",
      "1980  0.9   -1  -0.4    0 -0.9   -1  0.9  0.2 -0.2  0.1 -0.6    0   0.6  0.9   \n",
      "1981 -0.1 -0.6  -1.2 -0.3 -0.4  0.8 -0.5 -0.2 -0.2  1.7 -0.5  0.6  -0.7 -0.6   \n",
      "1982   -1 -1.1     0 -0.4  1.5 -0.2 -0.5 -0.6 -0.5 -0.8  0.3 -0.7     0  0.6   \n",
      "1983  0.4  0.4  -1.1  0.4 -0.7 -0.8 -0.4  0.4  1.5 -0.2 -0.7  1.1  -1.1  0.7   \n",
      "1984  0.7  0.5   0.5 -0.4 -0.2 -0.8 -0.7  2.3 -0.4 -0.6 -0.6  0.6   1.8 -1.1   \n",
      "1985  1.5  1.7   1.4 -0.6  0.5 -1.3  0.4 -1.1  2.1 -1.2  0.1 -0.7   1.7 -1.7   \n",
      "1986  0.8  2.2   0.5  3.3 -2.6  2.3 -0.6 -0.6  0.8 -0.3 -0.7   -2  -0.8 -1.1   \n",
      "1987  0.8  3.6   1.9 -2.5  1.4 -0.4 -1.6   -2 -0.1 -1.5  2.8 -0.9     0  1.9   \n",
      "1988 -1.7 -0.8   1.5 -0.7 -1.8  7.4   -2  0.3 -0.1  2.8  1.3 -0.2   1.6  1.1   \n",
      "1989  3.7    0   0.3 -3.8  1.6  1.1    0    0 -1.7 -0.7  2.1  1.6   1.7  3.8   \n",
      "1990  3.9 -1.9     2 -2.9 -1.8 -3.5 -1.5    5 -1.7  2.1  2.7  0.4   3.1    2   \n",
      "1991 -2.1    1   3.9  1.7   -2 -0.3 -3.2 -0.1 -1.2 -2.7  0.3 -1.5  -2.2  4.1   \n",
      "1992  4.4 -1.6  -0.8  4.1 -1.6    0  1.3 -0.6 -4.3   -4 -2.5  4.1    -3  0.6   \n",
      "1993 -1.1  5.8   0.2  1.2  2.8 -1.9 -1.2 -3.1  0.6    4    0 -3.8  -0.5 -1.6   \n",
      "1994 -1.2 -5.5     4  1.9  0.6  2.3 -3.3  1.9  2.9 -0.8  6.1  0.1  -2.7    4   \n",
      "1995  0.5  3.5   4.1  0.1 -3.6  0.6 -4.1  5.5  3.3 -0.3  3.5  0.5   0.6  1.8   \n",
      "1996  0.3 -5.8   5.9  5.6   -1  2.5  5.6  1.5  0.1  1.5  0.3  3.5  -4.6 -1.1   \n",
      "1997   -1 -4.5   4.6  6.4 -0.2  2.1 -2.2  6.1 -1.7 -0.3 -2.9  0.1  -2.1 -1.3   \n",
      "1998 -2.8 -5.4   0.3  2.8  2.9  0.3 -0.3  5.1  0.5  0.4 -0.8 -5.8  -1.4  0.8   \n",
      "1999 -0.9  0.1   4.4 -3.3  1.5 -2.9    1   -5 -2.3 -1.5 -5.9  5.9   0.6  6.5   \n",
      "2000 -0.4  1.8  -3.9  2.8  2.8 -3.3  4.4 -0.8  3.2 -0.8 -0.1 -2.7   0.5 -1.7   \n",
      "2001 -0.6  2.8   0.1  5.4 -0.1  3.7 -1.9    9  1.4  1.1 -2.6  5.5  -4.4   -6   \n",
      "2002  5.6  0.8     1 -4.9  3.4    2 -0.7   -2 -1.1 -5.1 -1.6 -6.9  11.6 -0.7   \n",
      "2003  0.7   -4    -1 -1.3  3.4 -3.1  2.9  1.1 -1.2  2.2 -2.9  1.6  -5.3 -0.9   \n",
      "2004  8.9 -0.4   1.2 -0.3   -3  6.4  2.7  1.2 -4.3  1.7 -0.5  2.3   2.5  3.4   \n",
      "2005  1.7  0.6  -2.4  9.6  1.6  1.9  3.2  1.6 -5.7  3.3 -2.9  1.2     1    2   \n",
      "2006  0.2  7.1   2.1   -1  6.2   -4  0.2 -1.8    1  0.7  1.9  7.1   4.1  3.5   \n",
      "2007    1 -1.9   3.3  1.7  1.5  2.7  3.4 -4.7  7.2   -1  1.5  8.5   0.7  1.6   \n",
      "2008  2.3  0.4  -1.6  8.1 -0.1  0.9  1.7  2.1  1.8  0.5 -4.4  1.8  -0.5  1.8   \n",
      "2009  1.2  9.2   0.9  0.2  3.5 -0.4  0.4 -3.4 -0.6 -3.1 -1.2  0.8  -0.7    4   \n",
      "2010 -0.7  3.1     0  3.6  0.1 -4.4 -0.9  7.6  2.8  0.3 -6.7   -3  -0.6  4.5   \n",
      "2011 -1.5  2.9   1.6 -2.8 -2.3 -1.6 -5.5  1.8  3.4 -3.4  1.5  8.6   3.8  4.9   \n",
      "2012  2.1 -0.6   1.5 -1.5  1.4 -6.5  8.9  1.5 -0.3 -0.1 -4.6  8.9   4.6  3.2   \n",
      "2013 -0.1  0.9   2.5 -2.7  5.1  0.8  3.8  3.3 -7.5  1.4 -3.7  1.9   3.6  1.9   \n",
      "2014  4.6  3.8  10.3  4.5 -7.2  0.1  5.1  4.8 -0.1    3  2.7 -5.6  -3.1  0.3   \n",
      "2015  7.5 -5.6   5.5 -0.7 -0.4  2.6  4.3    2  5.3 -6.1 -1.1  2.1   4.4  4.5   \n",
      "2016  4.2 -3.5  13.3  6.4  6.9  0.4 -2.7   -1 -2.2 -1.2 -0.9 -0.5    -4 -3.3   \n",
      "\n",
      "       14   15  \n",
      "Year            \n",
      "1979    1  3.8  \n",
      "1980 -0.2    3  \n",
      "1981  0.3  1.6  \n",
      "1982    0    1  \n",
      "1983 -0.1  0.4  \n",
      "1984    1  0.6  \n",
      "1985 -0.9 -0.5  \n",
      "1986  2.3  0.1  \n",
      "1987 -3.3  0.9  \n",
      "1988 -2.8 -3.6  \n",
      "1989  2.8 -0.6  \n",
      "1990 -1.9 -0.3  \n",
      "1991  3.9  0.6  \n",
      "1992   -3  1.8  \n",
      "1993 -2.6  2.3  \n",
      "1994 -1.2 -2.7  \n",
      "1995   -4  2.5  \n",
      "1996 -3.5  0.6  \n",
      "1997  1.2 -1.2  \n",
      "1998  5.7  1.5  \n",
      "1999  1.2 -0.7  \n",
      "2000  3.5  0.6  \n",
      "2001 -0.1 -1.2  \n",
      "2002  0.8  3.9  \n",
      "2003  5.4 -1.5  \n",
      "2004 -1.8  1.9  \n",
      "2005  2.7 -4.7  \n",
      "2006  2.8 -5.2  \n",
      "2007 -0.3 -0.7  \n",
      "2008 -2.3   -5  \n",
      "2009 -1.9  4.3  \n",
      "2010  7.4 -2.8  \n",
      "2011 -5.6 -3.8  \n",
      "2012 -3.1 -2.8  \n",
      "2013 -0.7 -3.6  \n",
      "2014 -4.1  2.2  \n",
      "2015 -5.6 -2.7  \n",
      "2016 -4.7  0.7  \n"
     ]
    }
   ],
   "source": [
    "#This is show an example of s\n",
    "print(effectiveness)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 4 - Data Visualization  \n",
    "\n",
    "Here we will start exploring our data visually, using different types of plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QNxERybNgtaNI5R3xy5VHWrWsHL7nUqg7g/LQeV3SkgGRklKR2owNVnCKtO8c/I/OnWTu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\nRGBP4HhgA6EXwcsp9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1941d860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#First is a line graph that plots the columns 3PA and 2PA. The line graph describes the league average\n",
    "#of 3 point shots attempted and 2 point shots attempted per year.\n",
    "\n",
    "#plot columns that contain 3 point shots attempted and 2 point shots attempted\n",
    "plt.plot(League_avg['3PA'], marker='o')\n",
    "plt.plot(League_avg['2PA'], marker='o')\n",
    "\n",
    "#Labels for the line graph\n",
    "plt.legend(['3pt Shots Attempted', '2pt Shots Attempted'], loc='upper right', fontsize=14)\n",
    "plt.ylabel('Number of Shots Attempted', fontsize=16)\n",
    "plt.xlabel('Year', fontsize=16)\n",
    "plt.title('3pt Shots Attempted vs. 2pt Shots Attempted', fontsize=18)\n",
    "\n",
    "#set the tickmarks of the x-axis\n",
    "plt.xticks(np.arange(1979, 2017))\n",
    "plt.xticks(rotation=70)\n",
    "\n",
    "#Change dimensions of the plot\n",
    "fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "fig_size[0] = 12\n",
    "fig_size[1] = 9\n",
    "plt.rcParams[\"figure.figsize\"] = fig_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pKXw/yteeXjED2u4Bh9/kP9OnvZTdn9thdvytXwbv/gWmvgjNtofj7oVdjtnS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sQfVPWRBBoGYHXCkSLSHxiuqtPCudAE6RiEXQePAAeJyLfAviKyDXBXgmsi5aOQevI0oKGq9gw9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\nNFTnY56qz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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c196eba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Next, we graph the 3 point percentage and 2 point percentage for the entire league throughout the years.\n",
    "\n",
    "#plot columns that contain 3 point percentage and 2 point percentage\n",
    "plt.plot(League_avg['3P%'], marker='o')\n",
    "plt.plot(League_avg['2P%'], marker='o')\n",
    "\n",
    "#Labels for the plot\n",
    "plt.legend(['3pt Percentage', '2pt Percentage'], loc='lower right', fontsize=14)\n",
    "plt.ylabel('Percentage of shots', fontsize=16)\n",
    "plt.xlabel('Year', fontsize=16)\n",
    "plt.title('3pt Percentage vs. 2pt Percentage', fontsize=20)\n",
    "\n",
    "#set the tickmarks of the x-axis\n",
    "plt.xticks(np.arange(1979, 2017))\n",
    "plt.xticks(rotation=70)\n",
    "\n",
    "#Change dimensions of the plot\n",
    "fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "fig_size[0] = 12\n",
    "fig_size[1] = 9\n",
    "plt.rcParams[\"figure.figsize\"] = fig_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3PvzwQ2JjY+nXrx8Aw4cPZ/ny5ezbty/LbZcfitqJjyXHttnwQQMYGWZut832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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c19452d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#For our graphs and data analysis, we look at the actual number of 3 pt attempts. Some people may say that\n",
    "#it would be better to use the ratio of 3pt attempts to total shots, because a team that averages a higher amount of total shots \n",
    "#will normally have a higher amount of 3pt attempts as well. However, this graph shows us that there is little to no difference\n",
    "#between a graph that displays the actual number of 3pt and 2 pt attempts(above), compared to a graph that \n",
    "#shows the ratio of 3 pt shots and 2pt shots over the total shots(below). \n",
    "#Therefore, there is no worry that our data will be affected by the total number of shots each team takes.\n",
    "#We will use the actual number of 3 pt attempts in order to make our analysis look cleaner.\n",
    "\n",
    "#plot columns that contain 3 point shots attempted and 2 point shots attempted divided by the league average\n",
    "plt.plot(League_avg['3PA']/League_avg['FGA'], marker='o')\n",
    "plt.plot(League_avg['2PA']/League_avg['FGA'], marker='o')\n",
    "\n",
    "#Labels for the line graph\n",
    "plt.legend(['3pt Shots Attempted', '2pt Shots Attempted'], loc='upper right', fontsize=14)\n",
    "plt.ylabel('Number of Shots Attempted', fontsize=16)\n",
    "plt.xlabel('Year', fontsize=16)\n",
    "plt.title('3pt Shots Attempted vs. 2pt Shots Attempted Ratio', fontsize=18)\n",
    "\n",
    "#set the tickmarks of the x-axis\n",
    "plt.xticks(np.arange(1979, 2017))\n",
    "plt.xticks(rotation=70)\n",
    "\n",
    "#Change dimensions of the plot\n",
    "fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "fig_size[0] = 12\n",
    "fig_size[1] = 9\n",
    "plt.rcParams[\"figure.figsize\"] = fig_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                ThreePA   R-squared:                       0.933\n",
      "Model:                            OLS   Adj. R-squared:                  0.931\n",
      "Method:                 Least Squares   F-statistic:                     504.1\n",
      "Date:                Thu, 22 Mar 2018   Prob (F-statistic):           9.20e-23\n",
      "Time:                        21:52:36   Log-Likelihood:                -75.610\n",
      "No. Observations:                  38   AIC:                             155.2\n",
      "Df Residuals:                      36   BIC:                             158.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      1.3227      0.578      2.287      0.028       0.150       2.496\n",
      "Year           0.6039      0.027     22.452      0.000       0.549       0.658\n",
      "==============================================================================\n",
      "Omnibus:                       23.330   Durbin-Watson:                   0.523\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               33.648\n",
      "Skew:                           1.839   Prob(JB):                     4.94e-08\n",
      "Kurtosis:                       5.778   Cond. No.                         42.2\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": 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EAwDgwyZPnqzTTjut7HWzZs20bNkyjRkzRkFB/MIZ8BZCNAAAPuyMM87Q1KlT\nJUn9+/dXenq6evTo4XBXQO3HP1EBAPBx11xzjZYtW6aePXvKGON0O4BfYBINAEANl52drWuvvVYr\nVqw46jF///vfCdBANWISDQBADbZixQoNGDBAGRkZWrNmjTZs2KDo6Gin2wL8HpNoAABqoJKSEo0Z\nM0Y9e/ZURkaGJGnbtm0aOnSorLUOdweASTQAADVMRkaGBg4cqM8++8yj9ttvv2nv3r2KiopyoDMA\nhzGJBgCgBlmwYIFcLleFAXrEiBFauXIlARqoAZhEAwBQAxQUFGjEiBF66aWXPGqNGzfW7Nmz1adP\nHwc6A1ARQjQAAA7bsmWL4uPjlZ6e7lHr06ePZs2apSZNmjjQGYCjYTsHAAAOsdZqxowZ6tChg0eA\nDgoK0jPPPKMPP/yQAA3UQEyiAQBwgNvt1k033aTExESPWosWLZScnKzOnTs70BmAE8EkGgAABwQE\nBKhZs2Ye6/Hx8Vq/fj0BGqjhCNEAADhk7Nix6tSpkySpTp06mjZtmpKSklS/fn2HOwNwPIRoAAAc\nEhwcrOTkZHXv3l1paWkaMmQIj+4GfAQhGgAAL1u9erXcbneFtZYtW+rTTz/VOeecU81dATgVhGgA\nALyksLBQI0aMUJcuXTR+/Hin2wFQhQjRAAB4wU8//aRu3bqVhedHHnlEq1evdrgrAFWFEA0AQBVL\nSUlRu3bttGbNmrK14uJi3XjjjSoqKnKwMwBVhRANAEAVycvL09ChQ5WQkKB9+/aVqzVr1kzTpk1T\ncHCwQ90BqEo8bAUAgCqQnp6u+Ph4bdmyxaN2xRVXaPr06WrQoIEDnQHwBibRAACcAmutXn75ZXXu\n3NkjQIeEhOill17Su+++S4AGahkm0QAAnKTs7GwNGTJE8+fP96j99a9/VUpKimJjYx3oDIC3MYkG\nAOAkrFixQi6Xq8IAPWTIEKWlpRGggVqMSTQAAJV04MABXXfddcrMzCy3HhkZqddee00JCQkOdQag\nujCJBgCgkurUqaOpU6eWW+vYsaPWr19PgAb8BCEaAICT0K9fP917772SpBEjRmjlypVq1aqVw10B\nqC5e285hjAmT9Jmk0NLP+a+1drQxpoWkFEnRktZJGmStLfRWHwAAeMszzzyja665Rt27d3e6FQDV\nzJuT6IOSLrLWuiTFSuprjOki6RlJE6y1Z0n6XdJQL/YAAMBJ27Jliy699FLt3r27wnpoaCgBGvBT\nXgvR9pD9pS+DS7+spIsk/bd0/Q1JV3mrBwAAToa1VtOnT1eHDh20aNEi3XLLLbLWOt0WgBrEq3ui\njTGBxpgNknZJWirpR0k51tpXVqSwAAAgAElEQVTi0kMyJDU7yrm3G2NSjTGpR5sAAABQ1fbu3asB\nAwZo6NChOnDggCTp/fff18SJEx3uDEBN4tUQba0tsdbGSoqR1ElS64oOO8q5U6y1cdbauEaNGnmz\nTQAAJElr1qxRu3btlJKS4lHbsGED02gAZarl7hzW2hxJn0jqIinKGHP4gsYYSdurowcAAI7G7Xbr\nP//5jy644AL9/PPP5Wp16tTRtGnTNGPGDBljHOoQQE3jtRBtjGlkjIkq/Xu4pIslbZa0XNK1pYcN\nluT5qCcAAKrJzp07demll2rkyJEqLi4uV2vbtq1SU1M1ZMgQAjSAcrw5iW4qabkxZqOktZKWWmvf\nkzRS0v3GmB8kNZA0zYs9AABwVEuWLJHL5dKSJUs8asOGDdPq1avVunVFOxEB+Duv3SfaWrtRUrsK\n1n/Sof3RAAA4orCwUI888ojGjx/vUYuOjtb06dN15ZVXOtAZAF/htRANAEBNtGvXLvXr109r1671\nqHXv3l2JiYmKiYlxoDMAvoTHfgMA/Ep0dLRCQkLKrQUEBOjxxx/XsmXLCNAATgghGgDgV4KCgpSY\nmKioqChJUkxMjJYvX67Ro0crMDDQ4e4A+ApCNADA75x55pmaNm2arrzySm3YsIFHdwOoNEI0AKBW\nstYqNTX1qPVrrrlG77zzjho0aFCNXQGoLQjRAIBaZ8+ePbrqqqvUuXNnrVy58qjHce9nACeLEA0A\nqFU+++wzuVwuLViwQG63WwMGDFB2drbTbQGoZQjRAIBaobi4WI8//rj+/ve/67fffitb37Ztm+67\n7z4HOwNQG3GfaACAz9u2bZsGDhyoFStWeNQ6deqkxx9/vPqbAlCrMYkGAPi0+fPny+VyVRigR4wY\noZUrV6ply5YOdAagNmMSDQDwSQUFBXrwwQc1adIkj1qTJk00e/Zs9e7d24HOAPgDQjQAwOds3rxZ\n8fHx2rhxo0etT58+mjVrlpo0aeJAZwD8Bds5AAA+w1qr6dOnKy4uziNABwUF6T//+Y8+/PBDAjQA\nr2MSDQDwGenp6Ro6dKjHesuWLZWcnKxOnTo50BUAf8QkGgDgM2JjY/XQQw+VW0tISND69esJ0ACq\nFSEaAOBTxo4dq44dO6pOnTqaPn26EhMTVa9ePafbAuBn2M4BAPApISEhmjt3rg4ePKhzzjnH6XYA\n+Ckm0QCAGmfx4sUaNGiQ3G53hfUWLVoQoAE4ihANAKgxCgsLNWLECPXt21dz5szRs88+63RLAFAh\nQjQAoEb46aefdOGFF2r8+PFla//617+0evVqB7sCgIoRogEAjktOTlZsbKzWrl1bbt3tdis1NdWh\nrgDg6LiwEAB8zI6cfKVn5Cg7r1DRESFyxUSpaVS4022dlLy8PN1zzz2aMWOGR61Zs2ZKSkpS9+7d\nHegMAI6NSTQA+JAdOflauilT+YUlalg3VPmFJVq6KVM7cvKdbq3SNmzYoA4dOlQYoK+88kqlp6cT\noAHUWIRoAPAh6Rk5igwLUmRYsAKMUWRYsCLDgpSekeN0ayfMWquXXnpJnTt31rfffluuFhoaqpdf\nflnvvPOOGjRo4FCHAHB8bOcAAB+SnVeohnVDy61FhAYpa/9BhzqqnD179mjIkCFasGCBR+2cc85R\nSkqKXC6XA50BQOUwiQYAHxIdEaK8g8Xl1vIOFis6IsShjk5camqqXC5XhQF66NChZXUA8AWEaADw\nIa6YKOUWFCu3oEhua5VbUKTcgmK5YqKcbu24mjZtqoKCgnJr9erVU3JysqZOnaqIiAiHOgOAyiNE\nA4APaRoVrt5tmig8JFBZ+w8qPCRQvds08Ym7czRr1qzcRYSdOnXS+vXrFR8f72BXAHBy2BMNAD6m\naVS4T4TmivTv31/33XefQkND9eSTTyo4ONjplgDgpBCiAQBVqqCgQD/99JPatGlTYf3555+XMaaa\nuwKAqsV2DgBAldm8ebM6d+6s3r17Kysrq8JjCNAAagNCNADglFlrNW3aNMXFxWnjxo3avn27brnl\nFllrnW4NALyCEA0AOCV79+5VQkKCbr31Vh04cKBs/b333tPUqVMd7AwAvIc90QCAk7Z69WolJCTo\n559/9qjFx8frhhtucKArAPA+JtEAgEpzu9165plndOGFF3oE6Dp16mj69OlKSkpSvXr1HOoQALyL\nSTQAoFJ27typm266SUuXLvWouVwupaSk6JxzznGgMwCoPkyiAQAnbPHixXK5XBUG6HvuuUdffvkl\nARqAXyBEAwCOq7CwUA899JD69u2rXbt2latFR0dr/vz5evHFFxUWFuZQhwBQvdjOAQA4rqlTp+rZ\nZ5/1WO/evbsSExMVExPjQFcA4Bwm0QCA47r99tvVtWvXstcBAQF6/PHHtWzZMgI0AL/EJBoAcFxB\nQUFKSkpSbGys6tatq6SkJHXr1s3ptgDAMYRoAMAJOfPMM7Vw4UK1adNG0dHRTrcDAI4iRAMAJB16\ndPfLL7+s/Px8jRgxosJjLrzwwmruCgBqJkI0AEB79uzRkCFDtGDBAgUGBuqCCy7QBRdc4HRbAFBj\ncWEhAPi5Tz/9VC6XSwsWLJAklZSUaMCAAfr9998d7gwAai5CNAD4qeLiYo0ePVoXXXSRfvvtt3K1\nnJwcff311w51BgA1H9s5AMAPbdu2TQMHDtSKFSs8ap07d1ZSUpJatmzpQGcA4BuYRAOAn5k/f75c\nLleFAXrkyJFasWIFARoAjqPSIdoY08oY84gxht/zAYAPKSgo0LBhw3TVVVd57Hdu0qSJlixZonHj\nxik4ONihDgHAd5xQiDbGNDXG3GeMWSPpG0mBkhK82hkAoMps3rxZnTp10qRJkzxql1xyidLT09W7\nd28HOgMA33TMPdHGmNt0KCzHSHpT0q2S5ltrx1RDbwDgl3bk5Cs9I0fZeYWKjgiRKyZKTaPCT/r9\nkpKSdOuttyo/P7/celBQkMaNG6fhw4crIIDdfQBQGce7sHCSpC8kDbDWpkqSMcZ6vSsA8FM7cvK1\ndFOmIsOC1LBuqPIOFmvppkz1btPkpIN0w4YNPQJ0y5YtlZKSoo4dO1ZF2wDgd443eviTpBRJzxtj\nvjXGPCmJzXIA4CXpGTmKDAtSZFiwAoxRZFiwIsOClJ6Rc9Lv2adPHz300ENlrwcMGKD169cToAHg\nFBwzRFtrs6y1k6213SVdLGmvpF3GmM3GmKerpUMA8CPZeYWKCC3/S8KI0CBl5xWe0vuOHTtWPXv2\n1IwZMzRnzhzVq1fvlN4PAPzdCd8n2lq7TdKzkp41xvxVUrzXugIAPxUdEaK8g8WKDPvfL/3yDhYr\nOiLkuOfu3LlTAQEBaty4sUctJCREy5YtkzGmSvsFAH91zEm0MeYsY8x8Y8zXxphkY0wzSbLWfsvF\nhQBQ9VwxUcotKFZuQZHc1iq3oEi5BcVyxUQd87zFixfL5XJp0KBBcrvdFR5DgAaAqnO8PdHTJb0n\n6R+S1kl6yesdAYAfaxoVrt5tmig8JFBZ+w8qPCTwmBcVFhYW6qGHHlLfvn21a9cuLVmyRM8991w1\ndw0A/sdYe/SbbRhjNlhrY494vc5a275aOjtCXFycTU1Nre6PBYAa7ccff1RCQoLWrl1bbj0oKEjr\n1q3T+eef71BnAOCbjDFp1tq4Ezn2eHuiw4wx7SQd/h1guDGmLERba9edZI8AgFOQlJSkO++8U7m5\nueXWAwIC9Oijj6pNmzYOdQYA/uF4IXqnpOf0vxC9U4cuLjSSrKSLvNcaAOCP9u/fr3vuuUczZ870\nqMXExCgpKUndunWr/sYAwM8cM0Rba3saYzpLcltr1xpjzpXUV9Jma+0H1dIhAECStGHDBsXHx+vb\nb7/1qF111VWaNm2aoqOjHegMAPzP8R77PVrSpZKCjDFLJXWS9Kmkh40x7ay1T1VDjwDg16y1euml\nl/TQQw+psLD8/aJDQ0P1/PPP66677uLuGwBQjY63neNaSbGSQnVoK0eMtXafMWa8pNWSCNEA4EVZ\nWVkaMmSIFi5c6FFr3bq1UlJS1LZtWwc6AwD/drxb3BVba0ustQck/Wit3SdJ1tp8SRXfiBQAUGX+\n3//7fxUG6FtvvVVr164lQAOAQ44XoguNMXVK/97h8KIxpr4I0QDgdePHj1eDBg3KXterV08pKSl6\n/fXXFRER4WBnAODfjheiu5dOoWWtPTI0B0sa7LWuAACSpGbNmmnGjBmSpM6dO2vDhg264YYbHO4K\nAHC8u3McPMp6lqQsr3QEACinf//+WrBggfr27avg4GCn2wEA6PgXFgIAqtiOnHylZ+QoO69Q0REh\nOrtBqJ5/6jFde+216tmzZ4Xn9O/fv3qbBAAc0/G2cwAAqtCOnHwt3ZSp/MISNawbqu+/3aIe3bpq\n0qRJGjhwoLKy+CUfAPgCQjQAVKP0jBxFhgWpbmiQFs1L0kM3Xq6MHw89PGX79u265ZZbZK11uEsA\nwPGwnQMAqlF2XqHC7UE99a+H9OmiBR71zZs3KzMzU6effroD3QEAThSTaACoRpk/fKU7/3FxhQF6\nwIABWrduHQEaAHwAIRoAqoHb7da4ceM04uartWv7tnK1kLBwTZj0mubMmaN69eo51CEAoDLYzgEA\nXrZz504NGjRIH330kUet5TnnauasRHXr6HKgMwDAyWISDQBetGjRIrVt27bCAH3PPffom/WpBGgA\n8EGEaADwAmutHn74YV166aXavXt3uVqDBg20YMECvfjiiwoLC3OoQwDAqWA7BwBUscMPU9mSmedR\n69GjhxITE9WsWTMHOgMAVBUm0QBQhY58mMod943UX11xkqSAgAA98cQT+vjjjwnQAFALEKIBoAod\nfphKZFiwgoOD9eizk3XmWa31zIx5evTRRxUYGOh0iwCAKkCIBoBTtGXLFpWUlEg69DCViND/7ZRr\n8qc/67W3P9af/trOqfYAAF5AiAaAk2St1YsvviiXy6Vx48ZJkqIjQpR3sLjccQcKSxQdEeJEiwAA\nLyFEA8BJyMrK0pVXXql7771XhYWFGj16tD7//HO5YqKUW1Cs3IIiua1VbkGRcguK5YqJcrplAEAV\nIkQDQCV98skncrlcWrhwYdlaSUmJBgwYoPohVr3bNFF4SKCy9h9UeEigerdpoqZR4Q52DACoatzi\nDgBOUHFxsZ544gmNHTtW1tpytfr162v8+PGqU6eO6tQRoRkAajlCNACcgF9//VUDBw7UypUrPWpd\nunRRcnKymjdvXv2NAQAcwXYOADiOd955R7GxsR4B2hijUaNG6bPPPiNAA4CfYRINAEeRn5+vBx54\nQJMnT/aonX766Zo9e7YuvvhiBzoDADiNEA0AFdi0aZPi4+P11VdfedQuvfRSzZw5U40bN3agMwBA\nTcB2DgD4g+LiYl1++eUeATo4OFjPPvus3nvvPQI0APg5QjQA/EFQUJBeffXVcmutWrXSqlWr9MAD\nDygggP90AoC/89r/Exhj/myMWW6M2WyM+cYYc2/perQxZqkx5vvSP0/zVg8AcLIuueQSPfjgg5Kk\nAQMGaN26dYqLi3O4KwBATWH+eK/TKntjY5pKamqtXWeMiZSUJukqSTdLyrbWjjPGPCzpNGvtyGO9\nV1xcnE1NTfVKnwBwNIWFhXr//fd11VVXyRhTtr4jJ1/pGTnKzitUdESIXDFR3BcaAGoBY0yatfaE\nJiZem0Rba3dYa9eV/j1X0mZJzSRdKemN0sPe0KFgDQDVbseOHUpISFBmZmaF9ZCQEF199dUeAXrp\npkzlF5aoYd1Q5ReWaOmmTO3Iya+utgEANUC13J3DGNNcUjtJqyU1sdbukA4FbWMMV+cAqHYffvih\nBg8erN27dys7O1sffvjhCe11Ts/IUWRYkCLDgiWp7M/0jBym0QDgR7x+dYwxpq6keZLus9buq8R5\ntxtjUo0xqbt37/ZegwD8SmFhoR544AFddtllOvzfliVLlui55547ofOz8woVEVp+/hARGqTsvMIq\n7xUAUHN5NUQbY4J1KEAnWmvfLl3OLN0vfXjf9K6KzrXWTrHWxllr4xo1auTNNgH4iR9++EEXXHCB\nnn/+eY/a0qVL5Xa7j/se0REhyjtYXG4t72CxoiNCqqxPAEDN5827cxhJ0yRtttYe+f9YCyQNLv37\nYEnzvdUDAByWmJiodu3a6Y8XKQcEBOiJJ5444e0crpgo5RYUK7egSG5rlVtQpNyCYrliorzVOgCg\nBvLmnugLJA2S9JUxZkPp2j8ljZP0pjFmqKRfJV3nxR4A+Ln9+/dr2LBheuONNzxqf/7zn5WUlKQL\nL7zwhN+vaVS4erdpovSMHGXtP6joiBB1admA/dAA4Ge8FqKttSslmaOUe3nrcwHgsPXr1ys+Pl7f\nffedR+3qq6/W1KlTFR0dXen3bRoVTmgGAD/HY7cA1DrWWk2cOFFdunTxCNChoaGaPHmy5s2bd1IB\nGgAAqZpucQcA1SU3N1cDBw7UwoULPWqtW7fW3Llzdf755zvQGQCgNmESDaBWqVOnjvbt87yb5m23\n3abU1FQCNACgShCiAdQqgYGBmjNnTtlWjfr162vu3LmaMmWK6tSp43B3AIDaghANoNaJiYnRjBkz\n1KVLF61fv17XX3+90y0BAGoZQjQAn7Vly5aj1q644gp9/vnnatGiRTV2BADwF4RoAD4nPz9fd999\nt84991x98sknRz3uRB6eAgDAyeD/YQD4lE2bNqlz586aPHmy3G63Bg4cqKysLKfbAgD4GUI0AJ9g\nrdXrr7+uuLg4ffXVV2Xr27dv1x133FGln7UjJ1+Lvt6hpNW/aNHXO7QjJ79K3x8A4PsI0QBqvJyc\nHN1www26/fbblZ9fPtC2atVKo0aNqrLP2pGTr6WbMpVfWKKGdUOVX1iipZsyCdIAgHII0QBqtC++\n+EKxsbF66623PGoDBgzQunXrFBcXV2Wfl56Ro8iwIEWGBSvAGEWGBSsyLEjpGTlV9hkAAN/HEwsB\n1Dg7cvK1/tdsvfHqRP13yvNyl5SUq9eJiNCdo55S+15XatWveXLFBKtpVHi589MzcpSdV6joiBC5\nYqLK1Y8lO69QDeuGlluLCA1S1v6Dp/6NAQBqDUI0gBplR06+3vrsK0176gFtXLPSo35eW5cGjXpe\nZ519tiJCg5R3sFhLN2Wqd5smahoVXrYdIzIsSA3rhnrUD3/G0UJ2dESI8g4WKzIsuOwz8w4WKzoi\npHp+AAAAn8B2DgA1ytTkt/XozZdVGKDvvfdePTXjXZ119tlH3W5xvO0Yx9vz7IqJUm5BsXILiuS2\nVrkFRcotKJYrJqr6fggAgBqPEA2gxvjxxx81ethN2vf7nnLr9aJO0wPPTtMLL7yg/UVGEaHlf4kW\nERqk7LxCSYe2YxyrfryQ3TQqXL3bNFF4SKCy9h9UeEhguSk2AAAS2zkA1CCtWrXS9UOHae7rL5at\nuTp21bAnXlBMTIyk42+3OF79RPY8N40KJzQDAI6JSTSAGuU/Tz2pv5zXXgGBgRp8zwg9MilJIfUa\nlW2nON52i+PVD4fsI7HnGQBQWcZa63QPxxUXF2dTU1OdbgNANVm78Vt9lr5FTc+OrfDuGse7+8ax\n6kdeeHj4wsTcgmK2bAAAZIxJs9ae0H1TCdEAqt369ev1yiuv6NVXX1VgYGC1f/6p3AIPAFB7VSZE\nsycaQLWx1mrixIkaOXKkCgsL1bx5c/3rX/+q9j7Y8wwAOFXsiQZQLXbv3q3+/ftr+PDhKiw8dKeM\n0aNHa9WqVQ53BgBA5RGiAXjd8uXL5XK59P7775dbLykp0eLFix3qCgCAk0eIBuA1xcXFevTRR9Wr\nVy/t2LGjXK1+/fp68803NWbMGIe6AwDg5BGiAXjFL7/8oh49emjs2LH64wXMHTp21sS5S1R0Rict\n+npH2dMCAQDwFVxYCFQSd3Y4vrfffltDhw5VTk5OuXVjjO4Z/qBcV96menXDy24xt3RTJreYAwD4\nFCbRQCUcvsdwfmGJGtYNVX5hiZZuymSSWio/P1933XWX/vGPf3gE6NNPP11Lly7VpbcM12l1w4/6\n2G0AAHwBIRqohPSMHEWGBREAK/D999+rU6dOevXVVz1ql156qdLT09WrVy9l5xUqIrT8L8EiQoOU\nnVdYXa0CAHDKCNFAJRAAj65u3brKzMwstxYcHKznnntO7733nho3biyJx24DAGoHQjRQCQTAo2va\ntKlmzZpV9vovf/mLVq1apfvvv18BAf/7T40rJkq5BcXKLSiS21rlFhQpt6BYrpgoJ9oGAOCkEKKB\nSiAAHlvfvn31wAMP6MYbb9S6desUF+f55NSmUeHq3aaJwkMClbX/oMJDArmoEADgc8wfbz1VE8XF\nxdnU1FSn2wAkcXeOkpISbd26Va1ataqw7na7y02eAQDwFcaYNGut5wSoAtziDviD44XkplHhfhWa\nj7R9+3bddNNN+vrrr5Wenq4mTZp4HBMQEOD3/9AAANR+jIuAI3ALu6P74IMP5HK59PHHHyszM1OD\nBw+W2+32OI6fIQDAHxCigSNwCztPhYWFeuCBB3T55ZcrKyurbH3x4sW6+cExHk8c5GcIAPAHhGjg\nCNzCrrwffvhBXbt21fPPP+9RO6/D39S331Uek2Z+hgAAf0CIBo7ALez+Z86cOWrXrp3S0tLKrQcE\nBmrA3Q/p2Rn/VePT/+QxaeZnCADwB4Ro4Ajcwk7av3+/Bg8erEGDBmn//v3lameccYYeeWWuBt99\nvwIDA8vWj5w08zMEAPgDQjRwBH+/h/G6devUvn37cg9NOeyaa67Rhg0b9LeuXY85afb3nyEAwD9w\nizv4HW5h58laq4kTJ2rEiBEqKioqVwsLC9OECRN0xx13yBgjlzl09w3p0AQ672CxcguK1aVlg7Jz\n/PFnCADwL0yi4Ve4/VrFEhMTNXz4cI8A3aZNG61du1Z33nmnjDGSmDQDACAxiYafOfL2a5LK/kzP\nyPHrEJiQkKDXX39dn332Wdna7bffrgkTJqhOnToexzNpBgD4OybR8Cvcfq1igYGBSkxMVHR0tOrX\nr68333xTr732WoUBGgAAMImGnzl8+7XDE2iJ268dFhMTo3nz5ql58+YKjWqiRV/v4LHdAAAcBZNo\n+BV/v/3avHnzNH78+KPWe/bsqdCoJuwbBwDgOJhEw68cviguPSNHWfsPKjoiRF1aNqhVU9aK7j4S\nFSoNHz5cr732mgICAtSqjUt1zjy/wkkz+8YBADg+QjT8Tm2+KO7w3Uciw4LUsG6o8g4Wa8Z7KzTr\nqeH6dssmSZLb7dattwzWCylLdHqTRso7WKylmzLL7rCRnVeohnVDy71vRGiQsvYfdOJbAgCgRmI7\nB1CLHDlFNpI+XZCsMbdeURagD8v9fY+2blqvAGN4bDcAACeBSTRQixyeIufuzdGExx/UiiXveRzT\nJKa5Hn3uVf31vNiytSMnza6YqOM+TAUAAH9HiAZqkeiIEKWt+VIv/GuYdu34zaN+44036uphj8kE\nl9/OUtFju2vzvnEAAE4VIRqoJUpKSvRJymsaP26s3CUl5Wp1IiI0+ZVXdNNNN5Xtm5Z4bDcAACeL\nEA3UAtu3b9egQYO0bNkyj9p5bV2a99abOvvssyUxaQYAoCoQogEft2TJEg0cOFBZWVketfvuu0/j\nxo1TaGj5u20waQYA4NQQogEfFxgYqD179pRba9iwoWbOnKnLL7/coa4AAKjduMUd4ON69eqlhx9+\nuOz13//+d6WnpxOgAQDwIibRQA1T0RMHj9x6UVF9zJgxWrlypS655BI9/PDDCgwMdPA7AACg9iNE\nAzVIRU8cPPw0wbqBxdq6M1vrd9sK68uXLyc8AwBQTdjOAdQgRz5x8MinCc5b8pk6dOigQYNuVJ1g\n41FPz8ghQAMAUI0I0UANkp1XqIjQ//2CyFqrxW9O130D++v7779X+uqVen/Oq+XOiQgNUnZeYXW3\nCgCAXyNEAzVIdESI8g4WS5JysrP0yN036rVnRqukuKjsmFmTxmvLxnVlr4982iAAAKge7IkGqtmx\nLhx0xURp6aZMfbXmc0187F5l7870OL97v+vV8M9/kdvaCp82CAAAvI9JNFCNDl84mF9YooZ1Q5Vf\nWKKlmzK1IydfktSobrDS5r2ix+9O8AjQ9evX15tvvqmkmdMUVb+usvYfVHhIoHq3acKDUwAAqGZM\nooFqdOSFg5LK/kzPyFHh3l0aMGCAVq1a5XHe3/72NyUlJal58+aSRGj+/+3deXjV1b3v8c+XzIZ4\nYwilgYAelA5ImzBoFevEIDid9tQrhoqiVhxwqGivVStYUfQoikef63OpKIcpkIrVY1sUDIh4oAcl\nQFMRq4xqSiBQjAkhc9b9Y29iNjvTzvTbm7xfz8OT/Vvrt/f+Zj3Z8GFlrd8PAACPMRMNdKHjNw5K\nvo2Bb//pv5SRkREUoM1MDz30kNatW1cfoAEAgPeYiQa60LGNg8dmoCvKj+qFJ6Yr9/XsoHPT0tK0\nZMkSjRo1qqvLBAAALSBEAx2sNRsHJd8M9P99ckajAfqyyy7TggUL1Lt37y6tHQAAtA7LOYAO1NLG\nwbTkBI0d3EcJsVE6dKRS199xn3qlfhOUY2JiNGfOHP3pT38iQAMAEMaYiQY6UHMbB4/NRqclJzTY\nGHiqTlm0UJdddpnOOOMM5eTkaPjw4V6UDgAAQkCIBjrQ4bIqpfaMC2hLjIvWoSOVTT7n0ksv1bJl\ny3T55ZcrKSmps0sEAAAdgOUcQAdqeMfBY0qOVuqdpXO1evXqJp+XlZVFgAYAIIIQooEOlJGerNKK\nGpVWVKvOOX3+5Zd65Pafa+EL/67rrrtORUVFXpcIAAA6ACEa6EANNw7mrnpb904cp+2bfdd+3r9/\nvyZPnqy6ujqPqwQAAO1FiAY6WEpCD70zf7aeue8mlXx1OKAvLy9Pe/bs8agyAADQUQjRQAfasWOH\nRo4cqeeeey6o7+KLLwbmWj8AABoMSURBVFZ+fr5OP/10DyoDAAAdiRANdJDFixdr2LBh2rJlS0B7\nVFSUHn/8ceXm5qpv374eVQcAADoSl7gD2qm0tFR33HGHFi9eHNQ3YMAALVu2TCNHjvSgMgAA0FkI\n0UA7bN68WVlZWdq5c2dQ31VXXaV58+bplFNO8aAyAADQmVjOAbTR888/r3PPPTcoQMfHx2vu3Lla\nvnw5ARoAgBMUM9FAG3311Veqrq4OaDvzzDOVk5OjIUOGeFQVAADoCsxEA2308MMP6/zzz68/vvXW\nW/Xhhx8SoAEA6AYI0UAbRUdHKzs7WwMHDtRrr72muXPn6qSTTvK6LAAA0AVYzgG0oKCgQGlpaYqK\nigrq69+/vz799FNFR/NRAgCgO2EmGmjGa6+9piFDhujxxx9v8hwCNAAA3Q8hGmjE0aNHdeutt+rq\nq6/W119/rZkzZ2rdunVelwUAAMIEIRo4zrZt23T22WfrpZdeqm+rq6vTtddeq+LiYg8rAwAA4YIQ\nDfg55zR37lydddZZ+vjjjwP6YmJidN999+nkk0/2qDoAABBOWMwJyHfN5ylTpugPf/hDUN8ZZ5yh\nnJwcDR8+3IPKAABAOGImGt3ehg0blJmZ2WiAvu6667RlyxYCNAAACECIRrdVW1urWbNm6cILL9QX\nX3wR0NezZ08tXrxYixYtUlJSkkcVAgCAcMVyDnRL+/bt06RJk7R27dqgvuHDh2vZsmUaNGiQB5UB\nAIBIwEw0up26ujqNHz++0QA9bdo0/eUvfyFAAwCAZhGi0e306NFDc+bMkZnVt6WmpmrFihWaM2eO\nYmNjPawOAABEgk4L0WY238yKzGxbg7YUM8s1sx3+r6d01vsDzRkzZox+/etfS5JGjRql/Px8XXbZ\nZR5XBQAAIkVnzkQvkDT+uLYHJK1xzg2StMZ/DHSowuJyrdxWqKUffK6V2wpVWFze6HkzZ87UK6+8\nonfeeUd9+/bt4ioBAEAk67QQ7Zx7X9Lh45p/Immh//FCST/trPdH91RYXK7c7QdUXlWrk6xaLzz2\noJa//1GjQTomJkY33XSToqKiPKgUAABEsq6+Okcf51yhJDnnCs3sW02daGa3SLpFkgYMGNBF5SHS\n5RcUKyk+WoW7tmvWr27Tvi/26B97d+mM03KUltzP6/IAAMAJImw3FjrnXnLOjXDOjejdu7fX5SBC\nHCqt0Mqcl/XLn1+hfV/skST97cP1Wjj3eY8rAwAAJ5Kunok+YGZp/lnoNElFXfz+OIEVFRXphV9P\n0ab/XhPUl7f2LVVXz1JMTIwHlQEAgBNNV89E/1HSZP/jyZLe7OL3xwnq3XffVWZmZqMB+uKfXqvc\nd98nQAMAgA7TmZe4WybpfyR918wKzOwXkv5d0lgz2yFprP8YaLPq6mo99NBDGjNmjAoLCwP6Tko6\nWb+ZM0/Z/zlPA9NSPKoQAACciDptOYdzbmITXaM76z3Rvezdu1cTJ07Uxo0bg/pGjhyppUuX6tRT\nT/WgMgAAcKIL242FQHOWL1+uzMzMoABtZvrNb36jdevWEaABAECn6eqNhUC7VFZW6uZbp2rJwvlB\nfWlpaVqyZIlGjRrlQWUAAKA7YSYaEeXgkWpt2fb3oPYxl4xXfn4+ARoAAHQJQjQiyrbCUt0763n9\nr1N8GwWjo2N0432P6N5nXhHXEwcAAF2FEI2IcrisSv3T0/V/nnhB6aedrheWrVDWjbfpq6PVXpcG\nAAC6EdZEI2wVFBQoPT09oC0lMVZllTX60QVjNPzcCxUdE6PSimqlJMZ6VCUAAOiOmIlG2KmtrdWs\nWbM0cOBArVkTePOUjPRklVbUqLSiWj2io1VaUa3SihplpCd7VC0AAOiOCNEIK/v27dPYsWP18MMP\nq7q6WpMmTVJR0Td3h09LTtDYwX2UEBulQ0cqlRAbpbGD+ygtOcHDqgEAQHfDcg6EjRUrVuiGG27Q\noUOH6tv279+vG264QStWrJCZSfIFaUIzAADwEjPR8FxlZaWmTZumK664IiBAS1JqaqruvPPO+gAN\nAAAQDpiJhqc+++wzZWVlaevWrUF9o0eP1uLFi5WWluZBZQAAAE1jJhqeWbRokYYNGxYUoKOiovTE\nE09o1apVBGgAABCWmIlGlystLdXUqVO1ZMmSoL5TTz1Vy5Yt07nnnutBZQAAAK1DiEaX2rx5s7Ky\nsrRz586gvquvvlovvfSSkpO5XB0AAAhvhGh0mf379+vHP/6xKioqAtrj4+P1/PPPa8qUKWwgBAAA\nEYE10egy3/72t3X//fcHtJ155pnKy8vTLbfcUh+gC4vLtXJboZZ+8LlWbitUYXG5F+UCAAA0iRCN\nLjV9+nSdf/75kqTbbrtNmzZt0plnnlnfX1hcrtztB1ReVavUnnEqr6pV7vYDBGkAABBWWM6BLhUd\nHa3s7Gxt2rRJP/vZz4L68wuKlRQfraT4GEmq/5pfUMwNVgAAQNhgJhodbu/evbrnnntUW1vbaH//\n/v0bDdCSdLisSolxgf+3S4yL1uGyqg6vEwAAoK2YiUaHWr58uaZMmaKvv/5ap5xyih555JGQnp+S\nGKuyypr6GWhJKqusUUpibEeXCgAA0GbMRKNDHD16VLfccosmTJigr7/+WpI0c+ZMvf/++0HnNrdx\nMCM9WaUVNSqtqFadcyqtqFZpRY0y0rnsHQAACB+EaLTbRx99pLPOOkvz5s0LaK+rq1N2dnZAW0sb\nB9OSEzR2cB8lxEbp0JFKJcRGaezgPqyHBgAAYYXlHGgz55zmzp2re++9N+jaz7GxsXr66ad19913\nB7S3ZuNgWnICoRkAAIQ1QjTa5KuvvtLNN9+s119/Pahv0KBBysnJ0bBhw4L6DpdVKbVnXEBbYly0\nDh2p7LRaAQAAOhrLORCyDRs2KDMzs9EAPXnyZG3ZsqXRAC19s3GwITYOAgCASEOIRqvV1tbq8ccf\n1wUXXKAvvvgioK9nz55avHixFixYoNKaKDYOAgCAExohGq1SWFioMWPGaPr06aqrqwvoO+27P9Cq\ndf+jSZMmsXEQAAB0C6yJRqv06NFDn3zySVD7VZNv1YTb71dJrC8Es3EQAAB0B8xEo1X69OmjxYsX\n1x8np/TSrLnZuu3+R5Xc86T6Owpyx0EAANAdMBONVhs7dqyuvukOfbotXw89/aJ69e4jKXBjIHcc\nBAAA3QEhGgGccyosLFTfvn0b7X/2qSe15u8HFXtSrOqcU1lljUoranTOwF6SfBsHc7cfkOSbgT6+\nHwAA4ETAcg7UKykp0aRJk5SZmanCwsJGz+mfmqRxQ9Ka3BjIxkEAANAdMBMNSVJeXp6ysrK0a9cu\nSdL111+vVatWqUeP4P9ntbQxkI2DAADgRMdMdDdXV1enZ599ViNHjqwP0JK0evVqzZ4928PKAAAA\nwhcz0d1YUVGRJk+erJUrVwb1DRkyRFdeeaUHVQEAAIQ/ZqK7qdWrVysjI6PRAD116lR9+OGHGjx4\nsAeVAQAAhD9CdDdTXV2tBx98UJdccon2798f0JecnKzXX39dL774ohISWNMMAADQFJZzdCN79+7V\nxIkTtXHjxqC+8847T0uXLtWAAQM8qAwAACCyMBPdTSxfvlyZmZlBAdrMNH36dL333nsEaAAAgFZi\nJrobeOuttzRhwoSg9r59+yo7O1sXXXRR1xcFAAAQwZiJ7gbGjRuniy++OKDtiiuuUH5+PgEaAACg\nDZiJ7gaioqK0ZMkSZWRkqKSkRLNnz9Zdd90lM2v0/MLicuUXFOtwWZVSEmOVkZ7MzVMAAAAaIER3\nE3379lVOTo5SUlI0dOjQJs8rLC5X7vYDSoqPVmrPOJVV1ih3+wFu3Q0AANAAyzlOIOvXr9dzzz3X\nZP/o0aObDdCSlF9QrKT4aCXFx6iHmZLiY5QUH638guKOLhcAACBiMRN9AqitrdUTTzyh3/72t3LO\nKSMjQ6NGjWry/OaWaxwuq1Jqz7iA8xPjonXoSGWnfg8AAACRhJnoCPePf/xDY8aM0YwZM1RXVyfn\nnCZNmqSDBw82ev6x5RrlVbVK7Rmn8qpa5W4/oMLicklSSmKsyiprAp5TVlmjlMTYTv9eAAAAIgUh\nOoL9+c9/VkZGht57772A9sLCQq1YsaLR57S0XCMjPVmlFTUqrahWnXMqrahWaUWNMtKTO/vbAQAA\niBiE6AhUWVmpe+65R1deeaX++c9/BvT17t1bb731lm644YZGn3u4rEqJcYGreBLjonW4rEqSlJac\noLGD+yghNkqHjlQqITaKTYUAAADHYU10hPnss8+UlZWlrVu3BvWNGTNGixYtUlpaWpPPP7ZcIyk+\npr7t+OUaackJhGYAAIBmMBMdIZxzWrhwoYYNGxYUoKOiovTkk09q1apVzQZoieUaAAAAHYGZ6AhQ\nWlqq22+/XdnZ2UF9p512mpYtW6ZzzjmnVa91bLlGfkGxDh2pVEpirM4Z2IuZZwAAgBAQosPc5s2b\ndc0112jXrl1BfRMmTNDvfvc7JSeHNovMcg0AAID2YTlHmCstLdXu3bsD2hISEjRv3jzl5OSEHKAB\nAADQfoToMHfRRRfp4Ycfrj/+wQ9+oLy8PN18880yMw8rAwAA6L5YzhEBZsyYobVr1+qHP/yhnnnm\nGSUksBQDAADAS4ToMFFdXa2SkhL16tUrqC86OlqrV69WXFxcI88EAABAV2M5RxjYs2ePLrjgAl11\n1VWqra1t9BwCNAAAQPggRHvs1VdfVWZmpjZu3Kh169Zp1qxZXpcEAACAFhCiPXL06FFNmTJF11xz\njUpKSurbH330Ua1fv75dr11YXK6V2wq19IPPtXJboQqLy9tbLgAAABogRHvgo48+0ogRI/Tyyy8H\n9V1++eX63ve+1+bXLiwuV+72AyqvqlVqzziVV9Uqd/sBgjQAAEAHYmNhF3LOae7cuZo2bZoqKysD\n+mJjYzV79mzdddddLV66rrC4XPkFxTpcVqWUxFhlpCfX3zwlv6BYSfHRSoqPkaT6r/kFxdxgBQAA\noIMwE91FDh8+rKuuukpTp04NCtDf+c53tHHjRt19992tCtDNzTQfLqtSYlzg/40S46J1uKyqY78h\nAACAbowQ3QXWr1+vzMxMvfHGG0F9kydP1ubNmzV06NBWvVbDmeYeZkqKj1FSfLTyC4olSSmJsSqr\nrAl4TllljVISY9v/jQAAAEASIbpRHbUxr7a2Vo899pguvPBCffnllwF9SUlJWrJkiRYsWKCePXu2\n+jVbmmnOSE9WaUWNSiuqVeecSiuqVVpRo4x0bg8OAADQUQjRx+nIjXkzZszQjBkzVFdXF9A+YsQI\nbd26Vddee23Ir9nSTHNacoLGDu6jhNgoHTpSqYTYKI0d3If10AAAAB2IEH2clpZLhOLuu+9Wnz59\nAtp+9atfacOGDTr99NPbVF9rZprTkhM0fkiafv6jUzV+SBoBGgAAoIMRoo/TkRvz+vTpo0WLFkmS\nevfurbfffluzZ89WbGzb1ycz0wwAAOA9LnF3nGPLJY5dGk5q38a8Sy65RPPnz9f48eOVlpbWquc0\ndwk7yRekCc0AAADeIUQfJyM9WbnbD0jyzUCXVdaotKJG5wzs1ej5zjktXLhQaWlpGjdunKTgEDz+\n37ICQm9zIfnYmuyk+Gil9oxTWWWNcrcfYLYZAAAgjLCc4zihLJcoKSnRpEmTdOONN+r666/X/v37\nW9yY2FJ/R67JBgAAQOdgJroRrVkusWnTJmVlZWn37t2SpKKiIl133XWa9uyCZu8Y2NIdBQ+XVSm1\nZ1zAeyXGRevQkcAbtAAAAMA7zESHqK6uTjMee1LnjhxZH6CP2bBhg/L/9rdmNya2tHGRm6UAAACE\nP0J0CA4cOKDRl4zTYzMeUm1NYND9/uAhysvL09DMzGZDcEshmZulAAAAhD9CdCvl5uYqIyND761Z\nHdQ3/urr9eTCNzV48OAWQ3BL/VzCDgAAIPyxJroF1dXVmj59up566qmgvqSTk3XvY3M0cvSl9WuW\nj4Xg/IJiHTpSqZTEWJ0zsFd9CG6p/9g5hGYAAIDwRYhuxp49ezRx4kR98MEHQX1Dhv1IDz71or7V\nN12lFdUBa5ZbCsGEZAAAgMhGiG7Cq6++qilTpqikpCSgvUePHrpy8p2adNs0nZwYX78co6nrSAMA\nAODEQ4huwo4dO4ICdL9+/ZSdna3vZJzd7HIMAAAAnNgI0U144IEHtGbNGq1du1aSdOWVV2r+/PlK\nTU2VJEIzAABAN8bVOZoQFRWlJUuWqF+/fnrhhRf05ptv1gdoAAAAdG/MRDejb9++2rFjhxISmHUG\nAADAN5iJbgEBGgAAAMcjRAMAAAAhIkQDAAAAISJEAwAAACEiRAMAAAAhIkQDAAAAISJEAwAAACEi\nRAMAAAAhIkQDAAAAIfIkRJvZeDP71Mx2mtkDXtQAAAAAtFWXh2gzi5L0oqRLJQ2WNNHMBnd1HQAA\nAEBbeTETfbaknc653c65Kkk5kn7iQR0AAABAm3gRovtJ+rLBcYG/LYCZ3WJmeWaWd/DgwS4rDgAA\nAGiJFyHaGmlzQQ3OveScG+GcG9G7d+8uKAsAAABoHS9CdIGk/g2O0yXt86AOAAAAoE28CNGbJA0y\ns38xs1hJWZL+6EEdAAAAQJtEd/UbOudqzOxOSaskRUma75z7uKvrAAAAANqqy0O0JDnn3pL0lhfv\nDQAAALQXdywEAAAAQkSIBgAAAEJkzgVdXS7smNlBSZ978Napkg558L4nCsavfRi/9mMM24fxax/G\nr30Yv/Zh/NrmVOdcq66tHBEh2itmluecG+F1HZGK8Wsfxq/9GMP2Yfzah/FrH8avfRi/zsdyDgAA\nACBEhGgAAAAgRITo5r3kdQERjvFrH8av/RjD9mH82ofxax/Gr30Yv07GmmgAAAAgRMxEAwAAACEi\nRDfBzMab2admttPMHvC6nnBnZvPNrMjMtjVoSzGzXDPb4f96ipc1hjMz629ma83sEzP72Mx+6W9n\nDFvBzOLN7EMzy/eP36P+9n8xsw/84/d7M4v1utZwZmZRZrbVzP7sP2b8WsnM9prZR2b2VzPL87fx\n+W0lM0s2s9fM7O/+vwfPZfxax8y+6/+5O/anxMzuYfw6HyG6EWYWJelFSZdKGixpopkN9raqsLdA\n0vjj2h6QtMY5N0jSGv8xGlcj6T7n3PclnSPpDv/PHGPYOpW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c18043c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#resetting the index starting from 0 so our data is easier to follow\n",
    "League_avg2 = League_avg2.reset_index()\n",
    "\n",
    "#Our goal is to try and predict when the number of 3 point attempts will surpass the number of 2 point attempts. We will use\n",
    "#OLS method that we did in the homeworks. This graph will give us the predicted number of 3 pt attempts per year, if the\n",
    "#trend we see now were to continue.\n",
    "\n",
    "#First we need to create a dataframe consisting of only year and 3PA data\n",
    "df3p = pd.concat([League_avg2['Year'],League_avg2['3PA']], axis = 1)\n",
    "\n",
    "#We have to rename 3PA to ThreePA, because patsy does not accept 3PA as a valid name\n",
    "df3p.columns = ['Year', 'ThreePA']\n",
    "\n",
    "#The years have to be indexes to 0, or else the graph will not show up properly\n",
    "df3p['Year'] = df3p['Year'] - 1979\n",
    "\n",
    "#Next, we will use patsy and the OLS method to figure out the slope of the line we will create to predict our data\n",
    "outcome, predictors = patsy.dmatrices('ThreePA ~ Year', df3p)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "print(res.summary())\n",
    "\n",
    "#We will form a scatter plot of our existing data, then overlay our predicted line on top of it.\n",
    "plt.scatter(df3p['Year'], df3p['ThreePA'], alpha=0.3, label='Data');\n",
    "\n",
    "#the year max is increased by so our line goes out past existing data points\n",
    "xs = np.arange(df3p['Year'].min(), df3p['Year'].max()+40)\n",
    "\n",
    "#This part is creating the graph\n",
    "ys = 0.5889 * xs + 1.5028\n",
    "plt.plot(xs, ys, '--k', linewidth=4, label='Model')\n",
    "plt.title('3PA vs Year')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('3PA')\n",
    "plt.legend();\n",
    "\n",
    "#We have to create a separate dataframe to hold the data we collected\n",
    "predict = pd.DataFrame(xs, columns = ['Year'])\n",
    "predict['3PA'] = ys\n",
    "predict['Year'] = predict['Year']+1979 #the years have to be reset to their original dates\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  TwoPA   R-squared:                       0.866\n",
      "Model:                            OLS   Adj. R-squared:                  0.863\n",
      "Method:                 Least Squares   F-statistic:                     233.6\n",
      "Date:                Thu, 22 Mar 2018   Prob (F-statistic):           2.58e-17\n",
      "Time:                        21:52:38   Log-Likelihood:                -102.73\n",
      "No. Observations:                  38   AIC:                             209.5\n",
      "Df Residuals:                      36   BIC:                             212.7\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     86.9393      1.181     73.630      0.000      84.545      89.334\n",
      "Year          -0.8391      0.055    -15.283      0.000      -0.950      -0.728\n",
      "==============================================================================\n",
      "Omnibus:                       12.823   Durbin-Watson:                   0.262\n",
      "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               12.850\n",
      "Skew:                          -1.321   Prob(JB):                      0.00162\n",
      "Kurtosis:                       4.063   Cond. No.                         42.2\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": 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ejBCNWiWyVZgycwqUmZOvImuVmZOvzJwCRbYKczlu3bp1euKJJ0re5+bmavz4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\nu580JRoAAAClZma69957tWHDBjVv3vz0eo0aNfTnP/+52A9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c0f992b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We have to do the same thing we did above, but for two point attempts. This graph will give us the predicted number of \n",
    "#2 pt attempts per year, if the trend we see now were to continue.\n",
    "\n",
    "df2p = pd.concat([League_avg2['Year'],League_avg2['2PA']], axis = 1)\n",
    "df2p.columns = ['Year', 'TwoPA']\n",
    "df2p['Year'] = df2p['Year'] - 1979\n",
    "\n",
    "outcome, predictors = patsy.dmatrices('TwoPA ~ Year', df2p)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "print(res.summary())\n",
    "\n",
    "plt.scatter(df2p['Year'], df2p['TwoPA'], alpha=0.3, label='Data');\n",
    "xs = np.arange(df2p['Year'].min(), df2p['Year'].max()+40)\n",
    "ys = -0.8504 * xs + 87.0748\n",
    "plt.plot(xs, ys, '--k', linewidth=4, label='Model')\n",
    "plt.title('2PA vs Year')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('2PA')\n",
    "plt.legend();\n",
    "\n",
    "#Saving the predicted two point attempts data \n",
    "predict['2PA'] = ys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the year where three point attempts should surpass two point attempts: 2039\n"
     ]
    },
    {
     "data": {
      "image/png": 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eeIP69evnGcfevXuZNGkSPXv2pG3btpQpU4Zp06aF1sfa19SpUwH46quvyMzM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Upfa+3o5sV1TdRHotRHXPpa085O3/0Y4J7PHRF4HKWviYiIDHIFxTDrJPf34h3+z2leC68+Avsd\nC4Ulua3fIJfqLm+NQDnwF9zi2xtT7cha+1a/1y4FzVASERGRbAWlr71n33E0tXXx8vpmumJubLT/\nxDHUzAjx2KpNvkElpa6JiIgMI7fMD1jM2wAWisbCrJNh7mmw//G9waVhuO5Sf9zlrcL77xLgxAyO\nmZ9JxUREREQGQnfM8s1HXkm681pHt+Wvr2/lkH0q+fT79qUmHGLxjBCh0UUAvOeFcbrzmoiIyHDn\nt5h3YSmccguMmeAW8371IXjpLigaAwec5B6v+1/o8so0r3X7gCEfVMpEqoDShTmrhYiIiEgfpEpf\n297exfI1Ln2tLtLI8jVNtLR3+e7HAHf9x3t8tyl1TUREZAToCQAFzTba/3j4wC3Q8Pfe4NLOd5P3\n09nq9jECAkoZ3+VtsFHKm4iIyMjml75WmG84bJ9KGnd28srGbcQsGAOzJo2lJhzikZc2ElX6moiI\niOyp7i74+nh8F/LGwHVNua5Rv+mPlDcRERGRQamrO8aNPulrnd2WZ954l8P3H8d/HjuTmnCIRTMq\nKCspBODg6kqlr4mIiMieyy9ws5j81l0qn577+gwABZRERERkwKVKXQNobu1k2ZooyyJR6hqiLF/b\nlBRMinf7Jw/zfVzpayIiItJvgtZdOu6agatTDimgJCIiIgMqMXVtfVMrV9y9gufffheLYVkkymvv\ntGAt5OcZ5kwZy0cPruL+5et909emVpSmPN7SRdMUQBIREZE9l27dpWFOASUREREZUN959NWk2Ubt\nXTF+//xaxhYXsCgc4pSDplAbDrGgqoLRxW74srCqQulrIiIiMrAOOnPEBJASKaAkIiIi/Spd+lrj\njg7qI1Hvr5ENzW2++zHA8mtPJD/P+G5X+pqIiIjIwFFASURERPpNUPras29tJRaD+jVR3tqyA3B3\nZJs3tZwxxflsb09eD2lqRWlgMKmH0tdEREREBoYCSiIiItJvgtLX7vz3OipGFVIzI8SHa6ZTG67k\noOnllBTmJwWhQKlrIiIiIoOdAkoiIiISKF362jstbdQ3uPS1ukg0ZfraC1efgDHJM46UuiYiIiIy\n9OQ0oGSMqQJ+A0wGYsBt1tofGmMqgTuBaqABONNaG81l3URERGR3Qelrz7yxle6YpT4SZU3jTgCK\nCvJYML2cMcUFbG/vStrX1IpS32BSD6WuiYiIiAwtuZ6h1AVcaq1dZowZC9QbYx4HLgCetNbeZIy5\nErgSuCLHdRMREZE43w5IX/tT/TrGjymiJhzivMPC1FSHmDe1jOICpa+JiIiIjBQ5DShZazcCG71/\ntxhjXgGmAacBR3tP+zXwfyigJCIi0m/Spa4BbGhqpS4Spb6hkfo1UTamSF/799eOV/qaiIiIyAg2\nYGsoGWOqgUXAc8AkL9iEtXajMWbiQNVLRERkuPFLXbvynhWsje5kbHEBdZEoy+LWPyotzGdhVQVj\niwtoUfqaiIiIiPgYkICSMWYMcDfwJWvttlSD0oRynwY+DTBjxoy9V0EREZFh5ObHVielrrV1xvje\nX14DYHJZCTXVIT4VDlEbrmT2lLEU5ucpfU1EREREAuU8oGSMKcQFk2631t7jPbzZGDPFm500BXjH\nr6y19jbgNoDa2lqbkwqLiIgMMunS16y1rG1spX5NI3UNUdY3tQbu6x9XHsu0ilLfbUpfExEREZEg\nub7LmwF+Abxirf1+3KYHgI8DN3n/vT+X9RIRERkqgtLXGt7dwZjiAuojUeoiUba0tAMwtriA4oI8\n2rtiSfuaVlEaGEzqofQ1EREREfGT6xlKRwDnAS8ZY5Z7j30VF0i6yxhzEbAG+EiO6yUiIjIkBKWv\n/eCJ1wGoqizlyP3HszgcojYc4oBJY3nwxQ1KXRMRERGRfpXru7w9g7s5jJ/jclkXERGRwSCT9LW3\nt+7w7r6WOn3t+a8ex8SykqTHlbomIiIiIv1twO7yJiIiMtIFpa+9tXU7o4oKqGuIsmxNlMYdHQCU\nlxamTF/zCyb1UOqaiIiIiPQnBZREREQGSFD62q1PvgHAvuNHc9zsidSEQ9RWh9h3/BgeUPqaiIiI\niAwCCiiJiIj0g3SpawCxmOWNLdupa4hSF2lMmb5Wf9XxjBtTnPS40tdEREREZDBQQElERGQP+aWu\nfeWel+jo6qaqcjT1kUbqIlGWRaJsa+sCYNzoIkoK8mgLSF/zCyb1UPqaiIiIiAw0BZRERET2kF/q\nWmtnN5ff/dKu/585cQynHDSFmnAlteEQ4XGjuH+50tdEREREZGhSQElERKQPumOWVzdtoz6S+s5r\nv7rgYBbPCFE+qjBpm9LXRERERGSoUkBJREQkgd96SMfNmcgLa5qoj0Spj0R5YU2UHR1uZlGegZhN\n3s+0ilKOmT0x5bGUviYiIiIiQ5ECSiIiInHcekgraO10axutb2rlkjuX0xMvyjMwa3IZH1o8ndrq\nEItnhKhraOSr965U6pqIiIiIjBgKKImIyIjX2R3j5Q0ufe3mx16lrXP3hbItMLakgP/62GIWVlUw\ntmT39LWqylEYY5S6JiIiIiIjhgJKIiIyrPmlrx09awLL1rjUtbqGKC+ua0oKIiXa3tbFe2dOCNyu\n1DURERERGUkUUBIRkWErXfpafp5h3tQyzj5kBjXhELXhSs7473/6LrI9taI0hzUXERERERncFFAS\nEZFhpb2rm5Xrm6lriHLL46/R1pWcvlZWUsBPz6tlQVU5o4p27wovWzKLr9zzktZDEhERERFJQQEl\nEREZEvxS15YumsbW7e3UR6Isi0Spi0R5aV0zHd2p09da2rp4z37jfLf1pK1pPSQRERERkWDGWp/7\nHA8BtbW1tq6ubqCrISIiOeBS13afNZRvDKHRhWzd3gFAUX4e86eVUVtdSU3Y3X1t6U/+4Zu+Nq2i\nlH9ceWzO6i8iIiIiMlQYY+qttbXpnqcZSiIiMmi1dnTz4romrrl/5W7BJIBua9ne1sWVJ8+mNhxi\n/rRySgrzd3uO0tdERERERPYOBZRERCTngtLXNm9ro67B3X2tPtLIyxu20RULnknb3hXjM0ftF7hd\n6WsiIiIiInuHUt5ERCSngtLXykoLiO7sBKC4II8FVRXUhkPUhENcdd9KNja3Je1LqWsiIiIiIv1L\nKW8iIjKobG/vYvmaJq4OSF9r7ezmqlPmUFtdydwpZRQV5O3a3tLWpdQ1EREREZFBRAElERHZI37p\na6ctnMqG5jbqGhqpj0Spa4jy6qZtpMheo70zxiffu6/vNqWuiYiIiIgMLkp5ExGRPvNPX4PRxQVs\na+sCYFRRPotmVFATrqQ2HOKKu1cofU1EREREZJBSypuIiOw1za2dLFsT5er7/NLXoLPbcv0H51ET\nDjF78lgK8nvT1644abbS10REREREhjgFlEREBAi+85q1ljWNO6lriFIXibIsEuW1d1pINcG1rbOb\njx9e7btN6WsiIiIiIkOfUt5ERMQ3da0gzzBnylg2NrezdXs7AGNLClg8I7Tr7muX/vFFpa+JiIiI\niAwjSnkTEZGMNO7o4IaHViWlrnXFLKs2tnDawqnUhEPUhiuZOXEMeXlm13OUviYiIiIiMjIpoCQi\nMgylSl97c8v2XXdeq49EeWvrjsD9xGKW75+5MHC70tdEREREREYmpbyJiAwzQelrB0waw4bmNpp2\ndgIQGlVITThETbiSXzzzFlu3dyTtS6lrIiIiIiIji1LeRERGoHda2rjhwZd909de27ydDy2eRm24\nkprqEPuOH40xLn1tSnmJUtdERERERCRjCiiJiAxyQelr3THLa5tbqI+41LW6SCNrG1sD99Mds3zn\nwwt8tyl1TUREREREsqGUNxGRQSwofW2/iWPYEG2lpb0LgPFjinfdee22v73FFu+ubPGUviYiIiIi\nIuko5U1EZIjb0NTK9QHpa2++s50zD67aFUSaUTlqV/rahLHFSl8TEREREZG9SgElEZEcCUpdA+jq\njvHqphbqGhqp81LYNja3Be6rO2b55ukH+m5T+pqIiIiIiOxtCiiJiORAYura+qZWLv/TCv780gZa\n2rtZvraJnR1u2+SyEmqqQ9SGQ/zX/73Jlpbk9LWpFaUpj7d00TQFkEREREREZK9RQElEZC+z1vKt\nP7+SlLrW0R3jsVXvMG9qGR+pmU5NdSU14RDT4oJFoVFFSl8TEREREZFBRwElEZE+SJW+1tEVY+WG\nZpZFotQ1RKlfE/WdZQRggIcvfm/gcZS+JiIiIiIig5ECSiIiWQpKX3vwxQ20tHXx4rom2rtiAFRV\nlnLk/uN56tV3aG7tTNpXutQ1UPqaiIiIiIgMPgooiYhkwVrLNx/xT1978tV3WFBVwbmHhXfdfW1i\nWQmQHIQCpa6JiIiIiMjQpYCSiIxoqVLXANo6u1mxrpn6SJT6SCP1kSjRnckzjcClr93/+SN8tyl1\nTUREREREhhMFlERkxPJLXbvy7hW8uC5KQV4edZEoK9c309ltAdh3/GiOnzOJx1dtpqkP6WtKXRMR\nERERkeFCASURGbG+89irSalrbV0xfvWPCEUFeRw0rZxPHLkPteFKFs+oYNyYYkDpayIiIiIiIgoo\niciwkS59bWdHF8vXNlHv3XltQ1Ob734M8NJ1J1JckO+7XelrIiIiIiIy0imgJCLDQlD62gtrohhj\nqI9EWbVxG90xl752wKQxjCrKZ2dHd9K+plaUBgaTeih9TURERERERjIFlERkWAhKX/v1sxFKCvNY\nWFXBZ4/aj5rqEIurQpSPKlTqmoiIiIiISB8poCQig1K69LWWtk5eWNPk3X0tXfraEgrz85K2KXVN\nRERERESkbxRQEpFBxy997Yq7V1AXaQSgPtLE6k3biFnIMzB7chmji/LZEZC+5hdM6qHUNRERERER\nkewpoCQig45f+lp7V4zf/WsNY4oLWDSjgiXHzaQ2XMmCqnLGlih9TUREREREJJcUUBKRvSpd6hpA\n084Olq2JUteQPn3txWtPJD/PJG1T+pqIiIiIiEjuKKAkInuNX+raV+5ZwZaWdipGFVIfiVIXifLG\nO9sBKMgzzJtaxujifHa0+6ev+QWTeih9TUREREREJDcUUBKRvebmx1Ynpa61dsa48ZFXACgrKaAm\nHOL0RdOoCYdYML2C0qJ8pa+JiIiIiIgMcgooiUjGMklf27q9fded19Y3tQbu6/FL3sd+E8aQp/Q1\nERERERGRIUcBJRHJSFD62qbmVspHFXnrHzXS8O5OAIry8yjKz6OjO5a0r2kVpcycNDbl8ZS+JiIi\nIiIiMngpoCQiGQlKX7vp0dUAjBtdxOJwiLMPmUFtdYh5U8t5dOUmpa6JiIiIiIgMQwooiYxQmaSv\nbd7WRl1DlLpIY8r0taf/39FUjxuFMbunryl1TUREREREZHhSQElkBApKX1vf1EpZSQF1kSh1Db1r\nIJUUpk5f22f86MBjKXVNRERERERk+FFASWQECkpfu/kxl742cWwxtdUhPnHkPtSGQ8ydWsbDKzYq\nfU1EREREREQABZRERgRrLeubWjO6+9rfLz+G6aFSpa+JiIiIiIhIIAWURIY4v7WQTjloCq9s3Obu\nvLYmSn1DlE3b2gAYXZRPcUEe7V3+6WtVlaMCj6X0NREREREREQEFlESGNL+1kL5813Iu++OLdMYs\n4IJEh+xTSW11iMUzQsyePJaHlL4mIiIiIiIie0ABJZEhxlpL5N2d1EeiXH3/yqS1kGIWSovyuOWM\ng6gJh5hSXpq0D6WviYiIiIiIyJ5QQElkEPFLXzv5wMmsXL+N+kijtwZSE1u3t6fcz872bj5w0NSU\nz1H6moiIiIiIiPSVAkoig0RQ+tqld0G3y14jPG4U7ztgPDXhELXhSi783+fZ0NSWtK+pFcmzkkRE\nRERERET6iwJKIgPIWsubW7ZTH4ly/YOrfNPXxhTn892PLGRxuIKJY0t22375ktlaC0lERERERERy\nTgElkb3EL33tpPmTeXFtE3WRKMsi7g5sTTs7U+5nR3s3J82f7LtNayGJiIiIiIjIQDDW2oGuQ5/U\n1tbaurq6ga6GiK/E9DUAY8DgZh0B7DdhNLXhSmqqQ9SEQ5z/i+dY75O+Nq2ilH9ceWyOai4iIiIi\nIhnNRPoAACAASURBVCIjmTGm3lpbm+55mqEk0k+6Y5bXNrdQH4ly48OvJKWvWQujiwv44VkLWTwj\nRGh00W7bL1P6moiIiIiIiAwRCiiJpOGXurZ00TR2tHexfG0TdQ0ude2FSJSW9q6U+9rR3sVxcyb5\nblP6moiIiIiIiAwVSnkTScEvdS0/zzC5rJiNzW3ErEtlmzVpLIvDIWq9u6+d/bNnlb4mIiIiIiIi\nQ45S3kT2QFd3jFc2tnDtAyuTUte6Y5Yt2zv4/DH7UxMOsWhGiPLSwt2eo/Q1ERERERERGc4UUJIR\nJSh9bVtbp7vrmve3fG0TOzu6A/fT2RXj0hODg0NKXxMREREREZHhTClvMmIEpa9NGFPE5pZ2rIU8\nA3OnllEzI0RNdSU3PryKzdvak/al1DUREREREREZjpTyJuLp6IqxckNzYPpadGcnlxx/ADXhEAur\nKhhd3PuxiMWsUtdEREREREREEiigJENWUPpadEcH9ZEodZEoyyJRXlzXRHtXLHA/HV0xLj5upu82\npa6JiIiIiIiIJFPKmwxJQelrlaML2dLSAUBhvmHe1HJqvLuvXf/gKjZt053XRERERERERIIo5U2G\npbbOblasa+aa+/3T11pau7hsySxqwyEWVFVQUpi/a3t7V0zpayIiIiIiIiL9QAElGXBBqWsAW1ra\nqY80UtcQpX5NlJXrm+nsDp5V194V4/PH7O+7TelrIiIiIiIiIv1DKW8yoPxS1wrzDQuml7NleweR\nd3cCUFSQx0HTyqmpDlEbruTq+1eyqVnpayIiIiIiIiL9SSlvMujt7OjiGw+vSkpd6+y2LFvTxAlz\nJ3HuoWEWh0PMn1ZGcUFv+tqO9i6lr4mIiIiIiIgMEAWUpF+lSl/b2Nzq7r7WEKU+EmXVxm10x/xn\nyFkLPz0vOCCq9DURERERERGRgaOUN+k3QelrB04tZ3NLO+ubWgE3k2hhVQU14RB/eH4N7+7oSNqX\nUtdEREREREREck8pb5JTLW2dfP0h//S15euaOPnAKVx05D7UVoeYM6WMwvw8APafOEapayIiIiIi\nIiJDjAJKEigofc1ay7qol77m3YFt9eYWgia7WQs/OWex7zalromIiIiIiIgMPUp5E19B6Wtzpoxl\nU3M777S0AzCmuIBFM1z62m+fjSh9TURERERERGQIU8qb9FnTzo7A9LWXN7Rw6kFTqKmupGZGiFmT\nx5KfZwCoHjda6WsiIiIiIiIiI4ACSiNAqjuvWWt5e+sO6iJRlkWi1EWivPHO9sB9xWKWH5y1yHeb\n0tdERERERERERgYFlIa5xNS19U2tXH73Cp58ZTNtXTGWRaK70tTKSwupCYc4fdE0fvWPt9m6PTl9\nbWpFacrjLV00TQEkERERERERkWFOAaVh7qZHX01KXevoivHgio3sM340x8yeSE04RG04xH4TxpDn\npa9NqyhV+pqIiIiIiIiI+FJAaYhJlb4Wi1ne2LLd3X2tIUp9pJFNzW2++zHA0//v6MDjKH1NRERE\nRERERIIooDSEBKWv/eXlTbR2drNsTRPNrZ0AjBtdxOJwiMYdHWxr60raV7rUNVD6moiIiIiIiIj4\nU0BpCLnpz/7pa4+s3MT+E8dw8vzJLn2tupLqcaMwxiQFoUCpayIiIiIiIiKyZxRQGkCp0te6Y5ZX\nN22jPhLdlcK2aVtw+toTXz7Kd5tS10RERERERESkvymgNEB809f+tII/v7SRnZ3dvLCmie3tLlVt\n4thiaqtDbG/vpLk1+/Q1pa6JiIiIiIiISH9SQGmA3PzY6uT0te4Yj63azJwpZSxdNJXacCU14RDT\nQ6VKXxMRERERERGRQUMBpQGyoanV93ED/PmL7/XdpvQ1ERERERERERkMFFAaIFMrSlnvE1RS+pqI\niIiIiIiIDHZ5A12BkeqyJbMoLczf7TGlr4mIiIiIiIjIUKAZSgNE6WsiIiIiIiIiMlQNmoCSMeYk\n4IdAPvBza+1NA1ylvU7payIiIiIiIiIyFA2KlDdjTD7wE+BkYC5wtjFm7sDWSkRERERERERE/AyK\ngBJwCPCGtfYta20HcAdw2gDXSUREREREREREfAyWgNI0YG3c/6/zHhMRERERERERkUFmsASUjM9j\nNulJxnzaGFNnjKnbsmVLDqolIiIiIiIiIiKJBktAaR1QFff/04ENiU+y1t5mra211tZOmDAhZ5UT\nEREREREREZFegyWg9G9gpjFmH2NMEXAW8MAA10lERERERERERHwUDHQFAKy1XcaY/wQeA/KBX1pr\nXx7gaomIiIiIiIiIiI9BEVACsNY+Ajwy0PUQEREREREREZHUBkvKm4iIiIiIiIiIDBEKKImIiIiI\niIiISFYUUBIRERERERERkawooCQiIiIiIiIiIllRQElERERERERERLKigJKIiIiIiIiIiGRFASUR\nEREREREREcmKAkoiIiIiIiIiIpIVBZRERERERERERCQrxlo70HXoE2PMFiAy0PXoJ+OBrYO0TC6P\npfrlvkwuj6X6DZ1jqX65L5PLY6l+uS8zXI+l+uW+TC6PpfrlvsxwPZbql/syuTyW6rfnxxqMwtba\nCWmfZa3V3wD/AXWDtYzqp/qpfiPvWKqf6qf6Dd366Vyofqrf4DjWYK+fzoXqp/oNnmMN5T+lvImI\niIiIiIiISFYUUBIRERERERERkawooDQ43DaIy+TyWKpf7svk8liq39A5luqX+zK5PJbql/syw/VY\nql/uy+TyWKpf7ssM12Opfrkvk8tjqX57fqwha8guyi0iIiIiIiIiIgNDM5RERERERERERCQrCiiJ\niIiIiIiIiEhWFFASEZFhzxhjBroOMrioTYgftQtJpDYhftQuJNFIbRMKKA0SfW2Aw6nhGmOybo/D\n6fX76cvrG07npC9tYrjb2+/vUDrn2ZwLm6MFA40xZcaYYu/f+izm2N5uE8aYYmNMUTbHNcZMNMZM\n9P49JM5jJobSa9mb7aIvbcLbrnYxgNR/5M5QaROgdpFLQ6VdDMY2MdhoUe5ByBhj+jjQDSznfWjt\nQDb0TF9XzwXGWhvry7lIV8YYUwbstNZ2xT83zfk7CHjFWttpjMmz1sYyrEuxtbY9m/r1pwzaBJm+\nln6oS4G1tivT+iU8Lx/XfrNqE1m8v1XAu9banQllAt9rY8xRwEpr7bvGmHxrbXcmdfLKVlhrm4Lq\nmsV+Ur2mccBMoA0oA96w1m7IYt8ZXzOMMeXAPOBta+3GDPc/BZgNrLLWbs6iXofh3qvXsyhzDvC4\ntXZLFmWuAP4CrMjyvf020Git/XYWZWqAA4C3rLXPZVgmBOwDdAKluHOf9vUZY/bFtYsSYDRQZ619\nLYu65gOxdO3C+0zNB7Z5x2hP9fy4cnOAWcAWa+0/MixzCrDeWrs8k+d7ZT4H3Jtpe40rdy3wFPBP\na223MabQWtuZpswtuHN2aRbHOQ53HrZZa3+XRblqIAyMAYqBemttJE2ZnLQJ77lZt4u+tAmvXE7a\nRV/ahFcuJ+2iL23CK5eTPqQv/YdXLus+JFf9h1cu6z4kV/2HVy7rPkTjit3KaFzRW07jCvo+rhgO\nFFAaIN6H50RgIu5D+CbwtLW2IU25POBwYApQATThBjHrMzyuAfLSXci85x2Mu7CssdY+m+H+84Bj\ngCrcoOWlDMoUAucDf7XWvuFXl8SLjDGmBLgEuCmbL+FeuVuBr/VcJI0xY621LWnK/BX4URYDuALv\nNU3FvT9P+QVUfMoZ4BBgAq5tbAResNZuSlEmHzgFmI4775uAh/3OZYpj5gPdGVzM84ETcG22Ebgj\ng7ZUACzFtaU3rLX3ZlCnIuAK3IV5pV+d/erqvVfXAFf31CvdeTfGlAK/BL7c0wkYY8Zba7emKFMC\n1AM/t9beku71xJUrBL6Ie69WWWtvM8aMstbuTFEm62uFV7/bgWnA27g2sQMXALsjRd3Ox7XXV+Ie\nz/MCeb5BM+9YvwTm4NrRM8CXgfYUwa4S4E6gZ3D6AdwAdTTwamKwLaHcW8DfgDXAC8ADwIeAB6y1\nzT5lSoHngcOttS3egGk+EANestZuCyjzOrDQWrvVGHMwUIO77v7dWvtEivqtxV2XnwdusNauThP4\nKwWewH3W9wcuxQ3KxgHLAtp/zzkPAVtwA6Wnvbo97HecuGPd3/PaAYvrR97GteWkgaP3Wfx/wGPW\n2vq4x/O9L89+QfMS4I/AWGA77lpxMdBsrbVp2tLDuPM3yzvuNtx1baW19mWfMsVAg/ca1gPPeefm\nQuDXfp9j7zy8DCzw2kQVsNirb5219tUU5+91YJa1dof35f5wr66PA7cnXmu8Mm8Am71zcZ3XHxjw\n/zXTK/N33GfpAOA3QBGwL+7z+ZeA+vWc9+24z8kJwKtAHfCTgHOekzaRUL+M20Vf2oRXLiftoi9t\nIq7cXm8XfWkTceX2eh/Sl/4jrlxWfUiu+o+4cln1IbnqP+KOlVUfonHFbmU0rtj9WBpX9HFcMVwM\nialmw9Q5wOdwgYOduODIrcaYS9KUOxf4GvBR3IXhcOBqY8wFQQWMMScYY64yxsy0Ts8XbWOMyfMu\n+InOxn0x/zRwnTHmahM3NdEET1P8GO6ifxbwTWPMEcaYU40xHzfGjA8osxT4CfBDY8wfvOceYIy5\nAQKnD54NHOFdREYbY95jjLnAGHO+MaYy6FzgOrZp1totxpjJxpiPA98zxvzaGHN0QJmzcF/mv2iM\n+Y0xZp805wDc+/sx3Hv0GWPMUuByY8z1xph5acpdgesIFwI34N7f01OUOQ/4AjADN1Cs9l7TDcaY\nCX4FjDGnG2N+bIyZ77WJLu9c5nl/pQHHOhvXVg8CTgJuMsYU9QyCA9rSx4BP4YIo53jt4ZPGmOuM\n+9XAz3HA9cAfjDH/MsZcboxZYIz5GaScUno2MN/rkELGmJOAK40x1xpjZgWUOR8YY63daIypMsZc\nDPyvMeZvxpgzA8qcBXQDJxljnjXu1y28c5dqauw5wBJcp3OEMeYi4GavXR2doky214oLgSJr7WG4\nDvce3BeXk4wx3/I6S78y1wI3GGN+a4y5yBgzxfbO0Ppwivc331q7CDgNNxg5puc9MsYc7fNZOQs3\nS/BI4HLg67hA7+eBG/3an3deO4F7vdfyKi74+j/ATcDhxpgxPvX7CPA3r4M/ELgF+G/gM0HHwn3+\nlnmDvhrgZmAubkBytnG/gvo5ExdknYkbbF9ujNkv1Rcj3DU9Yq39sPc6rgU+ixucXmyMqfApcx7u\nB6GTcQPFf+GC1+cbYy5LcazzgQ5r7UnAjcAvcAPBCuAqY8xonzKfwF2TfmCMecYY81VjTDhu4PZ5\nb8CW+JparLVH4z77AEvjzsOZAW3pLGCjtfYM3LXvWu/11QJfNm526S7egLod+BHwf8BtuMH5HcBV\nwCneQD/RR4CnvDZxEPADXKB3IXCpMWasTxlw16UVXuDgYOAbuIHtw7gfUvYLONbd1trFwH3AucaY\nWu+6G9QuzgVWW2u/BPwUd/4Pww2CzzbGTAsodx6w3Vp7Nq4t3Q80475QXGNccDpRrtpEz+vKtl1k\n1SYg5+2iL22i51i5aBd9aROQuz6kL/0HZNmH5Lj/gL71IbnqP6BvfYjGFb00ruilcYXT13HFsKCA\n0sC5CLjRWvtd4PvAk7gByDxjzPkpyn0O+I53kfgZ7oP7JLDEGPOZgDI34maw/N4Y8xdjzGeNm4Fh\ncb9ynR1Qv29ba48DLgMOBQ4EMMYsxv3K5eds4GbvwtLplf0AbmB1YeKTvYv5k7jB0T3A3biI/f3A\nGcaY44wxo3yOsxT4rvfvL+MizicAR+MuVEEOAf7s/ftK4Hjcr4h1wKkBHeLpuIvWwcC7wCXGmEk2\ndarYRcBXrbWX4KL1HwcMUIB3vgMCD/8BfNNa+xlcZ7gS9wvDuSY4qHQh8ANr7ZXAn3AD2m/gLuYX\nBJS5GneuHjPGvGZcwLHnNc3DDRb8fAL3S+qncBflWcD7vGDU4fi3pbNwM8m+iLvm/AcuODLK71x4\n/34GFzi9HvfeVuPet/cbY840xowJOH8fAX7l/ftLuPeh2DvWOYnH8uyP6zjBdXAHesf+CXBsQMd7\nOvAJa+0SXFu9wBgzy1qbbsruBbhfl37m1esUXIdVDywNGIT05VrxDrDVGFNqrd1irf27tfZHwDdx\n7+/7fMoswf3i/VXcjLxDgV8YY75jjPkhcKn1T+M4HfeFCGvt27hfCC8HMMacDHzF57NyLu6zDnAs\n0GCtPQYXxJ6Mu17sxvue1Y17X6bhBmFX4gJ7W3Hn8vM+9bsU9x6D+8VxtbV2Pu4zMD3gXPwL2Oi9\n94cDd1lrL8YNGHfgBo1+LsC9P+AGsu3APSY4MAnus9AzNf+DwPPW2g8A38G124/7lBkHbACw7te/\nN4BXcOejNkXA4TUgaowZZ61tstautu6Xx+/jBo5+15gFuDZxDG4APBN40BjzJ2PML4GPWmvbEsq8\nH3ddxbpZf/8DfNoYU2LcDI6PB7Slc4EH4/ZR5w0Cv42biXFG/JPjPmu/xF2L9rEufegl77W+DzcA\nTHQ+MNW42ZPnAc9Za48FvoU750nnwbtu/BN40xizn3cefmutvcVa+wfcL9t+7e8TuM84wO+AdcCf\njDFfSfFlNIYLSoD7YvWEtfZzuPbXivti4KcLaDbGFFk363YH7nNyLe6LxGyfMrlqE9C3dpFVm/D2\nnZN2sQdtAnLXLvrSJiB3fUhf+g/Isg/Jcf8BfetDLiA3/Qf0rQ/RuKKXxhW9NK5wsh5XDCvWWv3l\n+A83ffMbuC+vJd5j/8J9kZ2Li4JO8ClnvDLX95SL23YQ8BhwQMLjIdyFaF9gPO6D9UfcB+MXuAvG\n5xLKTAReBMbFPXY18DPv33cCF/rUbwqwGijw/n8rMMP79xzgEWBOwDk5FnehmOv9/8u4C/y9wAk+\n9evABRomA88Ck7xtc73jzAo4zntwF+RR3usPeY8X4KaHnpjw/GnA63H/PxP4Ay4AdUbAMYpxAbIv\neHVtizvORFyn+l6fckW4juXTuF+BwE1vnwsc6dV3tE+5T+AujKMTHp+KG7DWJjxehuvge9re+3EB\nim24dSDeTmwT3vPGe697etxjF+GmBIMbFF/g8179Gyj0/n8VMNn792TvPT844DzO987Vkd7/L/Ne\n55PA6T7Pr/TqfhVu0LKrDXtt8/HEcxHXZm7z6noTXpv1tj0GnOZzXt9KaPffByJemyxM8bm/0avf\n/rgvAD3ttth7r5b4lPk62V8rKoG7cB3ied657HkPHgQ+5VNmJr2fvyLvdb0XN/DYBlwU8Jo+ipsV\nFv/4g7hg4S9IuFbgrmNL4l7PScDEuO2/T2xH8WW9/x7gvfbxuKnIZ3jnZKZPmffign5R3BeyA+O2\n3eF3LK+O3wT+4b2Wm+m9rt2OCyb6fX79zusZuGD5F+JfQ9z2alwg+AWvnhfHbfttwHnvKXMd8HPc\ntPTF3rb7gbMCzl/Pde9xXND0yLjX9QDwaZ8yM4DZCY9V4Qap7Ynnwjt3x/q0iZ/iZtf92u/8ec9Z\nRO+1bxFxbds7F0n9Ttz2cu+1zfHaxPtxqQ5TfJ5b4z33ddysv3lx234XVD9v+2dw/eY/ccHr0ri2\nlNjW84FTffZRi/tB6OqANjER98Vonfe8y+O2/SbF+ZvktYtf4vrBh3HpFT3t4nyfMqOB/8X1f5m2\niXD8OUvXJuLaxXE99cm0XeBSBnrOccZtgt5rRTbtotarR1btAvhPXCpy2jYR1y4+2Id2MRn3JXt9\npu3CK3NvNm3C29bTh/wKN27MtA+Z5/270Dt2YB9Cb/9xYMLjgf1HXFuK70OWkEEfwu79xx9wX6Az\n6T8eoLf/mB+3zbf/iKtjTx/yAO6LfGAfwp71H3fj+o/7ep4X9/lI6j/iyvX0IT/Djf1S9iFem7jD\na0uZtokD8PqPTNpEQruYm2m78M73SfT2H0vY/VqR6biip108n0G7uDebduHV8RuZtol+aBd3ZdMu\n4spk3Ca8bWPIflxRTRbjCm/7CfRtXFFLluMK3Ow4yK7/OIQ+jiuGw9+AV2Ck/nmN+incl8MngR/H\nbWvAG5D4lJsJPIoL6lwM1MRtewcYFff/8RfJSfGPex/cr3iNvjThGKOAk9k9oFTk1fMcXABnVEIZ\n4/0t8P4/Dzg04Tmrgl6Xt30JLmBwCi7fGdyXmTyf13QqLjC2BXg2YT+vpDh/Y3FBm1dwAYorvMeL\ncVNdE8/FOOBo79/5cY9/zDv+RwOOczjuQv4X3EDv+Lhtq32O0/O63gc8hLuoP4L7tRNcB/um3+vy\nztF93uv5blx9y3Czm0b5lNkXl/qX+PgXccE6v+Pk4QJy0xIe/wNuJtpzAe1vVlwbSfwy8XLAsXou\n5gd5r+mcnjYRv2+fcsfgph4/B/w5w2MV436t3YgLCv0w7hwllcEFHxfH19P793twndpnUrTxBbhA\n1yO46bAfStducV+onsBdK57CrXuR9lrhbf8ULkj2Xa9uD3j7SGoTKfYxgYRrSwZlwrhB0xoSgt9p\nylUEvU8+zz0D91lemcX+j6J3oFNB+mvSobhg+mrvvN2JG0SmrV/Cfk732qTvucAtfjnTa3O/wg3K\nvoebueZ73nG/nF2Mm7V6qPfYFK9NpHyvcDMzr8ENfp7G9SkPZ/keF+CuSX7Xl7yex+m9DlTigqab\nUrUJ4j7bcWUnpvh85NH7peb9uIF5Nm3iMKA4rq0n9QM+ZSbhrpWrccGl33l/fvXb7fXg9We4a9Wz\nadpfCBfAvt17r27Hp/9NKDMN1z+ej/cDE66/X52m3PG4LxJZtQl2758D24Rf+8mmXWTTJnzKnNqH\ndnFENu3CaxOXJLSJ32ZYv4I+tIuqTNsF7ov8qbjZzBm3Ce95n8L1Hz9gL/ch9Pb7M8iy/4g7fxn1\nIbiAxW1Ztonj4tpE2v7De96huDSbN8miD8GN+Xraerr+owDXf8zy2lza/sMrtx+uD/kibgkJyKAP\n8drE93A/pO21NoH3Iym914pcjCvOws18yaZdHE9vH1Serl14beLruMBDVuOKnnORYbuY4LWL2d77\ndFW6doH7seVi3Az/92TaJuLOw3W4Hyiy6UMMvZ/9lH0IvcH6nudn1X8ktKeUfUjc8z6ACxJm0yYO\nj6trRuOKof434BUYiX/0fsEuwE0TPZze6OkX8IIIafbxEdw0ul/j0qLuxaWapSsXP7g9H7gngzI9\ndTsFl3v/v314zV8C/pDB8z6D+2XgxsT6Bjx/FLtHgS/J8DgH4mYdrPYuKP8NXJNBuZ4LTIH3Xv1X\nmuePwf2S8XvcrwkPAT9NU2YmLrh2MlDmPXY5cGeacjXe8x7FLTD4W+D6DN+fnovzEtw03Gze20Nx\nvzT9Jstyl6V6r+gdTJ2Jm1H39fj3IF2bBaoSjnVHmjKVuI70Da9dfBu3UHemr8fgfj37TibnG/eL\nx33Aclw6360pnj/We35NXBv0vVbgvlBejvsCMQv3xegDuIW9zyFuBlZCmctwU7f3AyoS2uOVAfWa\n5n22T/fKlce9bz8GfhlQ5tLEY9E79fiWFMfqKbeP9/9fAT4Z1C6851yB+/JQTe+gLw8XfPh+ivN3\nhvfa9/f+DsT9Ajo2oH5TE15XecL2oPp9Gfer3L6468rBuFmoV5MwYy2uzCW4z+oB9A66DS6oeWOG\nbXUaLmB7IO7aXp5Jubh/h/H55TTgWD1t9qvArzL8PMUPME/CpWCnK5PntfEzvP/3nS2YovxRmRwn\nrr0Wee/7e4gLrGRy/rKsVxXuF/2LCZjRmab8ArwfTxIen4ybVXmi9//jE9pERUCZLwe0zcA24ZX7\nYny5uHbxNXzGFV6ZSxKP5bWJk4PeK6/clxKOlbJdxJ2LD/hsO9rvWHHn4pSEdjEFN67zbRMB9Us3\n1uk51kk+7eKLfu0i7vwdk2mb8LbthwuCHE/vD5DH4fqQj+Hfh/SUOdlnm28f4pW5Ov41xW3z7T/i\nyl2XWM479x/Gpw+JK3Ny3GOX0Nt/+LWJ/XAzSk5NeDyw/4gr93Xg/T37xl1vA/sQ3PX/Wr/zF/9Z\n8TnOtcTN4sfNGroe94NB0mc0rtw1uOUKdrs+EdCH4M06xY2583BBvzO8dhE0rjgW9x2nPPF14PrU\noHHFsbixS1ncYz2z0X7k1y68MnMSjtWzzESqccWxuNne8eWuw5u1EtAujsVdJ8fHPdbTX70/oP0d\n673/8eOded6xl/i1iRSvq8Dv3wllZhOXtYCbNXQNweOKnnPut7/D/NpE3GdofNz5LsKNzxbgxqBl\nAWUqA/a3D95n0qeM7xgFFygLulZcgpchkvB4ofde3RRQpiLhsVOBD6c455fg+s+8hMcN7keCpOMM\nt78Br8BI+8NNvYvhpjqO89l+At4sn4THp+I66/vp/SVhIm6xr9nehzc/oMy9xHUc9F6Y/aafT8V1\nRn9KKDMad2G+g4S0sIRj3ZNQrgjXAV0BHBVwrF1lcF+cL6M3LSrxw9lznAeAY+MeL8Z1cl/Cm6GT\n4vwdnfC6puPfGSbVz3s81S8P8ef8qLjHj8Eb0OKfotRT7k/EzeyiN5h3JnBYQpkyXKDgR7hB5VFe\nWyjEXZSrfY7TU+YHuIDi+3qe5134DiYhbTKh3C1x5cLeOS/y9peYLphY5qi4Y43CBQ+P9ClzSlyZ\nI3CDnw/Smx6WNEhPOBcX4gbz071tBd5jfsf6AC4F8hPesSri3g+/TsjvPCSd5wzq917vsam4z+Fx\nuIXB/cr8EDeIPxyY2nMOcIOQBQllDsb9gvddXLrgY7hrTVKKpU+Zm3G/Kj2BSzc9Ps1rOhiX4vFd\nv3K4z9akgDK+x8J9/v0GIIl1fNI7bvxnJXHKt1+Z+GMVkpwmGl/m/3BTuG/B5wtZNq8rzXn/rnes\nnvodkUGZ73nv7+O4YPgJaeo3A5eW8z7iBsFZlKlM2BY0Q7CnzFGJZeKe4zejKf5Y4xK29QzUdh4v\nGQAAEL9JREFUC7OoX34fXlPPDIek15aqfhmev8TXlMn58z0PGZTL9P1diPt1/Me4WZO+X45TlFlO\nwJe0FOV+ElQusV2kOlbce+U3sE8s94PEclmWMfH/7cdz4fe6gtpF/Pl7McvX9BNgRRb1q8Fdw27G\npavPxKVNBwYy48p8BzdjYjauf0/V79Tgrns3+5XBjREmpSjneyxcv1OWpn5zcGOmBXHPSXx//cp8\nNu44+fgvQRBf7nlc0P8i3N3A0p2LbM/f07j+4/m4Mklp/SnK/RsXRPhcmmP13L3sJtwPWcfiZv18\nguBrbU+Zb+HWIDoBF1T7HKlnqMYfK7FcHm4278Qsyozy/vyCeIl1PBH3w+K5BM+Q8SvTc6xSr10k\nXsviy/wnLlA7EbeeaGAguS/nMK7Mt71j9dTvQoIDMvFlLvbqV+UdpzhF/Rbj7s6N97p7fphM1dbj\ny+Thvn9dlab9pS0T8P5mUq44TZkq3PfXmizqV4UL3MUfJ+0P4UP9r6ejlBwxxtyE+/WqEdhgrb05\nw3I/xa3F8y4uqv0ELtr9Eu62jElvZEKZubgOayEuNep2a+2OLMq8gMubjln/2zKmOtad1v+2m35l\nZuEGWX+0PrdS9ynzV1wwbRlutktLYpmAck/j0g6XA7dZn0Uf07wm32PFlWnEDUB66vecdx58bw+f\nUK6nfotxaxb91lrb6lOmZyHODbggzFRcwO8xa+0/A46TWGa6V+Zha+2//MqkKfeItfbZ/jpWQJnf\nesepy6J+VbhUvGyP9UfvWM94d3qwGZTZ9ZqMMQXW/xbRfuXuwq0/9Vya+m3E/ZJS5R3roRSv6XtA\n1Fr7De//98HNZvw4bvD4ycT6+ZSpxv3Ke4FX5jPW/xbgqcrVWWsvyLLMc9baizJ8XdW4WUcfx322\nLkr8DKc51vPW2qTF+wPO367X5B3H7/1Ndw4/mXjdDCjzEdzA71ngs9bajgzqd0bccf4jsYz3vN/g\nrkMP4RbpfQt4xVr7vDFmHHC2tfbHGZR52VpbZ9ydNM+11t6aRZlxwP9v7+xjJavrO/x870KzpUgW\nrAtlsdhGoNZUXRWVJkuhpC1qYunKps0qttE2rY1Es4baNJESStLUROqiIAphXXBdX8BFU9sai3SB\nVguaGF7WbHdFBYEuhbWFFpRFfv3je6YMc2fundczc888T3Jy78yZz36fc+7ZuXN/5/eyqZRyZZ9+\nrdzR5NwNHx3gmI6pjunyAets7swskdtTSrmjOq7fG7DWIH6tOkdX5/zDA56/Y4DzSilbOzJbgUdK\nKX9V/dvbgF2llO0RcTx5Q2TnMplryF7O10XEccBZpZQdXfyWyv0c2dj/uQEyx1Z+n+2jVvtxHVfl\nPjPgcfWT2VZlrq3O3+mly1LqY6p1TXVM11bn4qxSyqf68Luhj5/V3wIHq9xfkzcy7iZvYHyHXKDk\nyWUyp5GfTc+uMm/u/Ly5TOZesvdRt8+1S+X2kb2JOt9ru2X2VMe0j5wnsXOJ8qXq7K/qdPtd0O38\ntR/Xm0rHZMN9nL9z+szcTfbc20v2puj2Gbqb351ttTb2yG0ne7ztJXsiryHnjHkD8HAp5YFlMkdV\nuddWmUdLKfd3ZpbJvRG4r5Ty6ACZs8kVvhYtXd8jd0x1XGcDB0pO/txvrdcD/1FKObBMpv38vbHK\nPDjguWid90W5Lpk1ZC+j3yR/9j8YsE7Xn1X1HnNfKeWDEbGRvPF9GPm36b3kddv5/6ozs6kt8x3y\nuu18f1kq0/o/1e3/4lK5/eRcrIf6yPxUldlXZTo/ly1XZ2O3z2WNo8xAq9Y8bWRjxHFki/Bt5F3t\nM6nGkNK7tf8Onp2TYh85ue+fkeNTr6T7Xd/OzCVkl+4vkXe1jxwi06tVfKnc5XSfW6LbMb2PnF/m\no31m2utc0e08LFHrghHOX9daPTIXtB3TonPeR60re/ysbm93IBsbLiM/HJzbo85Sma6TjE+o1sYB\nMh8hP1wN69dr8vRxZ7oe0zK5u4Y8f70yW6rXncTiHh3X0qX3yzCZPnLX0dEjbNjMBI9rUL9hz8XY\nzvsIfjeQjW+/Tr4fXUr2fHsneXNi0XDaCWRuosdw2mFyM+RX5/nrOux5yFq3AC9ue3w22eAA2TP3\nqnFk+sxdPY7MBI9rGL9hz8VYzvuIfq3FVL5INmK29m2j+/vSuDOfoPfvgoFzM+RX5/nr9btg4FrV\nvtaQ7l+oHn+N/BtmJ9lwMJZMH7lFC7JMqNanxnlcEzoXw9T67THW2UOO4NhA9ob8nbZ92+nSU2kC\nmaU+9wycq9OvadvUBeZpI4fU3Njx3HvJWep/eYncC8hJ295P3ol+oGP/btpW3ppg5pbOzLC5uo5p\nxs7fOP1+luzh8sdkb5e1wNerfS8nG7w6J5MeONNnblHj34Qyw/qN5VzMul+173CysfQSsqH6hVSr\nUZB3nl4zjkxTazXRj+yC/SLahnBWj88lG63/m8VDn2vJ6Dc1vyPJu8m/xLPDxw4nb2CcQQ6V7RxO\nO3CmqbUa7PczVEP1qRZnqb5vDTv9GrB+Ghn9puPXkX9rde0cRU4y/Hyy1/7acWaaWqtJfuT7ya+S\nQ/E+R/YUWuDZ95uvd7lua8msBL8mblMXmKeNHM71kur7Vm+Uo8m5cx4B3rtEtrUc4YfIu5GbyC6J\n64FvTTOj39T8NpB3lD5GflC8uHr+NOBfxpWps5Z+w2V47mTJR5CTtP49udLQVrJ3w45RM02tNQ9+\nS1xjB6aR0W92/FrPkcMifkwOPx0p09Ra8+RXPd/6w+hUcqhw7Rn9ZsePXIDjHuDmHv/ewJmm1poX\nP3LuqJfxbMPkqeS0B7VnVoJfU7fDkNoopext+/6Jao6WHwIXRsQ/k+M0e2VvJ4fAEBEvI5ftPIuc\np+fj08zoNzW/WyPifnLS6v3kXE+QLeW7x5Wps5Z+Q2cWIuJ15KS8zyPn+NoaEb9C9oC7AuicQ2CY\nTFNrNd3vDHLuvs+XUr7atv9ecmXCaWT0m57faeRk4ce3Z0rOA3cFOex61ExTazXd79fIa2lXKeWm\nUsozEbFANkZ1zi9WV0a/6fm13l/WkdfSroh4EjgAEBGHl+fOQzNMpqm1muzXeo85tsq1fyZ9KXlz\nfBqZleDXSJyUuyYi4mSy+/HrgSfIpZPvbtsfAKXjB1LlTiHHv/8PeRf6zoh4FTl5878B3yttE37V\nldFv6n6/Rf4RubOU8u/VvlVkb6dvl1L+a5RMnbX0G9nv7eSKKzvIPyI2k/OyfYSc/+MxOhgm09Ra\nc+D3SXLuvs3kXbRrgMtK98lNa8noN3N+28g52p4GflzaJkYdJtPUWnPodw250mUrd6jujH4z4/cW\ncq7XS8nPJf28vyybaWqtOfE7nrxpsZq8lj5E9oI8tMS1NLHMSvBrLGUGuknNw0YuV38JOWnmP5Gr\nN90BbKRLt8MuubPIYQ1PkuOcey5jXVdGv5nwu5lcHe4b9JiscNhMnbX0Gznz5c795PLA24Hzx5Vp\naq0583sl+YHnXdPM6DeTfudXjxdGzTS11pz7dQ7xqCWj30z5vZr8vTPI+8uSmabWmjO/Jl7rE/Fr\n6jZ1gXnYyEl197Q9fhH5B+NbgKuAFw6YO6+6YBfl6sroN3N+Pa+lCVx/Y62l38iZIFdHvKjLvmOB\nW4FXj5ppai399NPvOZlTR800tdac+zXxWtdvjvw8F/pN26/J2wJSBwvA7RFxQvX4RHLJ0R3A/cA7\nW0Pe+sxdB3yvR66ujH6z5bfUtTTu62/ctfQbIVPyN9jHgZdGxFcj4o+qoXGQEzivJSdZHCnT1Fr6\n6affczJ3j5ppaq0592vita7fHPl5LvSbtl+TcVLuergf+C5wT0R8q/p+R7XvIXJ4UxlTrq6Mfvrp\nNwN+EbEeeDH5y+044A+AiyPiNnII5RfL4vkyBs40tZZ++um3cv08F/rpp9+s1dJPv25+jabMQDep\nedmANeTQlXVtz30BeNO4c3Vl9NNPv+n5keO1vwL8I7AL+Jvq+RcAv0FOFNg57nvgTFNr6aeffivX\nz3Ohn376zVot/fTr5tf0beoCTd+AdwMfAF7eZd864Lxx5erK6KeffjPjdzmwpfp+LXA9sKl6/Dzg\n98eRaWot/fTTb+X6eS7000+/Wauln37d/Jq+OYfS5PkL4OeBqyNid0RsiYh11b71wCNjzNWV0U8/\n/WbDbz3wrwCllIfJ4XHvqPadT95JGUemqbX000+/letXZy399NNv5frVWUs//eaPabdoNXkDTiGH\nqRwJPB/YRF54dwGfIcdZvmYcuboy+umn38z4rQJOp2PlN+AG4E+Am4D1o2aaWks//fRbuX6eC/30\n02/WaumnXze/edimLtD0DVgNrO547ihgK/Dtcebqyuinn36z4Ve9ZlX1daH6ehKwH/jmODNNraWf\nfvqtXD/PhX766TdrtfTTb942V3mbMKWUH7U/jogopTwWEU8DO8eZqyujn376zYZflftJ9fWZiFhV\nStkXEZ8GDowz09Ra+umn38r1q7OWfvrpt3L96qyln37zRpRsWZOaiYgTgYOllMcnnasro59++s1G\nrYhYgPxlN8lMU2vpp59+K9evzlr66affyvWrs5Z++jUZG5RERERERERERGQgXOVNREREREREREQG\nwgYlEREREREREREZCBuURERERERERERkIGxQEhEREelBRFwfEQcj4tgu+86IiGci4t3TcBMRERGZ\nJk7KLSIiItKDqiHpHuDmUsqmtud/GrgTeBjYMK+ru4iIiMj8Yg8lERERkR6UUg4A7wHOjYhz2nZd\nBJwAvL2uxqSIWBURh9VRS0RERGQ5bFASERERWYJSyieBvwOuiIg1EfFKYAtwUSllb/trI+J3I+L2\niHgiIn4YEZ+OiHUdr3lbROyOiP+MiMcj4psRsbnjNasjokTEhRHx/oj4PvAUcNJkj1ZERESkPxzy\nJiIiIrIMVaPQPcAu4BXA08DrSik/aXvNe4BLgauAG4E1wMVAAV5RSnmiet1fkkPl9lfRM4H3Ae8o\npXyies1q4EngQWAv8GHgR8DtpZRHJ3msIiIiIv1gg5KIiIhIH0TEH5KNRYeAV5VS7mrbtwZ4ANhe\nSvnTtudPBvYA7yqlXNnl31wge4xvA04upby2er7VoHQfcFIp5amJHZiIiIjIEDjkTURERKQPSilX\nAw8BN7Y3JlVsAI4AdkTEYa0NuLfaTm+9MCJeEhGfjYgHyZ5Oh4C3Aqd0KfslG5NERERkFnFiRxER\nEZH+earaOllbfb2tR+678P89mb4CHAQuqJ5/imri7y65h0aRFREREZkUNiiJiIiIjE5rXqPNwL4u\n+x+rvm4A1gHnlFK+0doZEYf3+Hedm0BERERmEhuUREREREbnFnLOo18spexc4nVHVF8PtZ6IiLXA\nGyboJiIiIjJ2bFASERERGZFSysGI+HPggxFxPPBl4HGyN9KZwD+UUq4HbgX+F/hYRFwMHAVcCBwA\nTpiKvIiIiMgQ2KAkIiIiMgZKKZdFxPeBLcDbgFXkym+7gbuq1zwYEW8GPgDcAPwAuBQ4kZxHSURE\nRGRFEKU4NF9ERERERERERPpnYdoCIiIiIiIiIiKysrBBSUREREREREREBsIGJRERERERERERGQgb\nlEREREREREREZCBsUBIRERERERERkYGwQUlERERERERERAbCBiURERERERERERkIG5RERERERERE\nRGQgbFASEREREREREZGB+D8A8Q9QANGySQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c17d1eef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, we will add all the data we found into one graph, so we can see when the amount of \n",
    "#3 point shot attempts will surpass the amount of 2 point shot attempts. Our code will print the year that occurs.\n",
    "\n",
    "#Change the year back to the index\n",
    "predict.index = predict['Year']\n",
    "\n",
    "#Print out the year in which 3PA are predicted to surpass 2PA\n",
    "for index, row in predict.iterrows():\n",
    "    if(row['2PA'] <= row['3PA']):\n",
    "        print(\"This is the year where three point attempts should surpass two point attempts: \" + str(int(row['Year'])))\n",
    "        break\n",
    "#plot columns that contain 3 point shots attempted and 2 point shots attempted\n",
    "plt.plot(predict['3PA'], marker='o')\n",
    "plt.plot(predict['2PA'], marker='o')\n",
    "\n",
    "#Labels for the line graph\n",
    "plt.legend(['3pt Shots Attempted Predicted', '2pt Shots Attempted Predicted'], loc='upper right', fontsize=14)\n",
    "plt.ylabel('Number of Shots Attempted', fontsize=16)\n",
    "plt.xlabel('Year', fontsize=16)\n",
    "plt.title('3pt Shots Attempted vs. 2pt Shots Attempted Predictive', fontsize=18)\n",
    "\n",
    "#set the tickmarks of the x-axis\n",
    "plt.xticks(np.arange(1979, 2056))\n",
    "plt.xticks(rotation=70, stretch=5)\n",
    "\n",
    "#Change dimensions of the plot\n",
    "fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "fig_size[0] = 20\n",
    "fig_size[1] = 9\n",
    "plt.rcParams[\"figure.figsize\"] = fig_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Weighted 3pt Effectivness Over Time')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cpQ07gxoYHNq/bdnjW1VckKeLF8/IYmUTz6gDJTM7zswuMrOGVBYE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jZuBGPjIgf9vkX8kRpKIsPAoYZWvvr4OsbGRfKby2cTGmQv2GXoXD5vIlmjY7j7pS20u9xOl+NX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\n9UA8BAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c0fa1aeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graphing the effectiveness of the 3 point shots attempted per year\n",
    "aggEffectiveness = pd.DataFrame()\n",
    "myArray = np.arange(0)\n",
    "weightArray = 16 - np.arange(16)\n",
    "weightedEffectiveness = 0\n",
    "for index, row in effectiveness.iterrows():\n",
    "    weightedEffectiveness = 0\n",
    "    for x in np.arange(16):\n",
    "        weightedEffectiveness = weightedEffectiveness + row[str(x)] * weightArray[x]\n",
    "    myArray = np.append(myArray, weightedEffectiveness / 16)\n",
    "aggEffectiveness['Weighted'] = myArray\n",
    "aggEffectiveness['Year'] = League_avg.index\n",
    "\n",
    "\n",
    "\n",
    "plt.plot(aggEffectiveness['Weighted'], marker='o')\n",
    "\n",
    "plt.ylabel('Weighted Effectivness', fontsize=16)\n",
    "plt.xlabel('Years since 1979', fontsize=16)\n",
    "plt.title('Weighted 3pt Effectivness Over Time', fontsize=18)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 5 - Data Analysis \n",
    "\n",
    "When starting out with this project, our group noticed that there seems to be a recent trend in the NBA of focusing more on the three-point shot than in years past. We also suspected that the two-pointer was losing its flare and that there might be a correlation between the attempted two- and three-pointers. In order to analyze this, we accrued data about the league average from every NBA season since 1979. By plotting the average number of three-point attempts and average number of two-point attempts for a team in each season, we were able to corroborate that the number of three pointers attempted by a team has steadily increased since 1979 while the number of two-point attempts has steadily decreased. \n",
    "\n",
    "Afraid that the league average for three-point attempts or two-point attempts in any particular season might be skewed by a team taking many more of a particular shot than the average team, or an outlier season in which teams took more shots than normal, we decided to also plot the ratio of three-point and two-point attempts for every season. We did this by dividing the average number of three-point attempts and two-point attempts by the total number of average field goal attempts for each season, so as to remedy these fears. However, when we plotted our results we arrived with the same result that the number of 3PA and 2PA are inversely correlated, with three-pointers increasing and two-pointers decreasing over time.\n",
    "\n",
    "Given this new information, we decided to predict when the avarage number of three-point attempts would surpass the average number of two-point attempts in the NBA. In order to do this, we performed OLS regressions on both the 3PA over time and 2PA over time. Plotting the results of our regressions, we were able to predict that 2039 is the year when there will be more 3PA than 2PA in the NBA. \n",
    "\n",
    "With the increase in the number of 3PA since 1979, we wanted to see whether there was also a change in the field goal percentage of each shot over time. However, by plotting the average field goal percentage of each shot for every season, we were unable to find any significiant trend. While the two-point FG% hasn't changed too much overall with slight variation from year-to-year, there seems to be a prominent increase in three-point FG% from 1979-1995. However, since 1995 there doesn't seem to be any drastic change in the three-point FG% and the pattern is similar to two-point FG%. This is probably attributed to the fact that the three-pointer was originally introduced in 1979 and it took players some time in order to acquaint themselves with the shot. \n",
    "\n",
    "The primary question we were trying to address with our project was whether or not focusing heavily on the three-point shot was an effective strategy or not, given the trend towards taking more three-pointers. In order to gauge the \"effectiveness\" of relying heavily on the three-pointer as a succesful strategy, we decided to analyze data from 1979, the season the three-pointer was introduced in the NBA. Our procedure consisted of aggregating the top 16 teams in the league for each season, and comparing the average number of three-point attempts(3PA) of each team to the league average for that season. Then, we calculated the weighted average for each season by subtracting the league average 3PA from the 3PA of each of the top 16 teams, and multiplying each entry by a weight based off of the team's rank during the season. We finally average the 16 results we get from this procedure to generate a weighted average number of 3PA for the top 16 teams compared to the league average for a given season. By plotting this weighted average across all NBA seasons from 1979-2017, we were able to visualize the weighted three-point effectiveness over time. Our graph reveals that there are many more seasons with a positive effectiveness than a negative effectiveness, and the general trend shows an increase in the \"effectiveness\" of employing a three-point driven strategy. However, given the drastic variations from year to year, we cannot conclusively decide that the effectiveness of the strategy has gone up, but rather only note that there is an upward trend in the general pattern of the effectiveness.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 6 - Privacy and Ethics Considerations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to the overtly public nature of this data we do not have sizeable concerns regarding privacy. The data sets we utilized only included team names and are otherwise free of any potentially vulnerable or personal information. If we wanted to we could have anonimized the data by changing the team labels to arbitrary names. One main reason for doing this might be to ensure that the anaylsis of any trends or patterns would not be influenced by outside knowledge of the teams. However, we decided that because of the objectivity of our research question this step was unnecessary. \n",
    "\n",
    "The website we sourced our data from, Basketball-Reference, was very conducive to data sharing. We assured that the Basketball-Reference permitted the use of their tables and data. Basketball-Reference also provided custom data sets if the data researchers sought was not already listed on their site, though these specialized sets came at a price. Luckily we were able to find the appropriate data for our question in their already publically listed tables. \n",
    "\n",
    "\n",
    "As a result of the objective quality of sports statistics, we are confident that the data sets we used are unbiased.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 7 - Conclusion and Discussion\n",
    "\n",
    "Through our project, we were able to reach some interesting conclusions. First off, we were able to determine that the number of three-point attempts in the NBA is inversely correlated with the number of two-point attempts in the NBA, with the number of 3PA steadily rising and number of 2PA steadily decreasing since 1979. In addition, we were able to determine that this doesn't necessarily mean that NBA players have become better three-point shooters, as the average two-point field goal percentage has remained relatively consistent since 1979 and the average three-point field goal percentage has remained relatively consistent since 1995, with a spike from 1979 to 1995 that we suspect might be due to the novelty of the three-point shot but have not done research on. Finally, arguably the most intriguing finding of our project is that the overall effectiveness of the three-point shot in the game seems to be increasing since 1979. Hence, our data analysis supports our hypothesis that a three-point focused game strategy is indeed recommended over a two-point strategy and is effective in winning games. \n",
    "\n",
    "Our findings suggest that the trend of recent NBA teams such as the Warriors prioritizing the three-pointer is warranted, as our analysis shows that the benefit of the extra point from a three-pointer outweight its increased difficulty compared to a regular field goal. Based off of our findings, we could also debilitate arguments around the NBA from commentators, playors, and others that the game has deteriorated in quality and that the new strategies of three pointers have made it an easier league, since our evidence shows that the strategy of taking more threes is actually more effective. \n",
    "\n",
    "While our findings do support the claim that taking more three-pointers is an effective strategy, our analysis is rudimentary and not definite. The likelihood of exclusively taking three-pointers as an effective strategy is probably not true, and while our analysis compares the number of three pointers taken by top teams compared to average teams, it does not take the field goal percentage of a three pointer into account. Therefore, there has not yet been a thorough analysis done that contrasts the three-point field goal percentage to normal field goal percentage, and results from this analysis might question our results. In addition, because of this additional variable, skeptics might question the validity of our claims, but by historically comparing the success of teams we have been able to reach the insight that the more succesful teams have taken more three pointers than average and this effectiveness has shown an increasing trend. In addition, other pitfalls include the way we performed our OLS regression. There is a good posibility that we were too generous in our assumptions during extrapolation and the current trend will not continue in the same way until 2039. Hence, there is a good possibility that the number of three-point attempts will not surpass the number number of two-point attempts in exactly 2039 unless the trends stay exactly the same."
   ]
  },
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