NBA Data Notebook/NBA Data Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NBA Analysis: A Comparison of Michael Jordan, Kobe Bryant and LeBron James at Their Prime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "![LebronKobeMJ](./images/KobeMJLebron.png)\n",
    "\n",
    "Our project focuses on the age old discussion: Who was the best basketball player at their best? Michael Jordan, Kobe Bryant and LeBron James are widely considered the best basketball players of all time and are usually ranked on preference, but by examining the statistics at their arguable prime years, we seek to gain insights and compare their achievements on the basketball court. The results will be based purely on statistics and official achievements. Furthermore, we orchestrated this data science project in a way which focuses on the use of standardised tools and multiple data sources in regards to Big Data and Big Data Engineering requirements. The challenge is to combine all these and create a valuable insight with the collected and processed data.\n",
    "\n",
    "Our team consists of following members:\n",
    "\n",
    "- Maria Mirnic (wi21b105)\n",
    "- Kevin Xhunga (wi21b025)\n",
    "- Safwan Zullash (wi21b030)\n",
    "\n",
    "You can find the whole project in this [GitHub Repo](https://github.com/wi21b030/BDENG-NBA-Data)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project Requirements Overview\n",
    "\n",
    "| Requirement                                               | Description                                                                                                                                                           |\n",
    "|-----------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
    "| At least 3 different data sources of different data types | The project should utilize a minimum of three data sources that offer diverse data types, such as APIs, web scraping, and CSV files.                                  |\n",
    "| Use Kafka to collect the data                             | Kafka should be employed as a central message broker to collect data from various sources. It acts as a scalable and fault-tolerant system for data ingestion.        |\n",
    "| Use Spark to read data from Kafka and analyze it          | Spark, as the core processing framework, should be used to read data from Kafka and perform data manipulation and analysis tasks efficiently.                         |\n",
    "| Store analyzed/transformed data/results                   | The project should store the analyzed and transformed data in a suitable database or file format to ensure data persistence and enable further analysis or retrieval. |\n",
    "| Show results and tell a story                             | The project should present the analysis results through visualizations and storytelling to effectively communicate insights derived from the data.                    |\n",
    "| Visualize the architecture of the system                  | The project should provide a graphical representation of the architecture, illustrating the flow of data and the components involved in data processing.              |\n",
    "| Document each step in a Jupyter notebook                  | Each step of the project should be thoroughly documented in Jupyter notebooks, providing transparency, reproducibility, and clear explanations of the process.        |\n",
    "| Infrastructure must be multiuser capable                  | The project infrastructure should support multiple users, allowing for concurrent collaboration and efficient development and maintenance of the project.             |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About Big Data Infrastructure & Engineering\n",
    "\n",
    "Our project incorporates several aspects of Big Data Infrastructure and Engineering, addressing the criteria of Volume, Velocity, Variety, Veracity, and Value. Additionally, it aligns with the four levels of data handling in Data Science, including Data Source, Data Storage, Data Analysis, and Data Output.\n",
    "\n",
    "### Big Data Vs:\n",
    "\n",
    "- **Volume**: Although the size of the dataset used in our project may not be massive, we have designed our architecture and utilized technologies such as Spark and Kafka to handle large volumes of data efficiently. This scalability allows us to process and analyze increasing amounts of data as needed.\n",
    "\n",
    "- **Velocity**: We have implemented Kafka as a central message broker in our architecture, enabling real-time data ingestion and processing. This allows us to handle streaming data and maintain a continuous flow of information throughout the project.\n",
    "\n",
    "- **Variety**: Our project leverages multiple data sources, including REST API, web data, and CSV files. By incorporating diverse data types, such as JSON, HTML, and structured CSV, we are able to analyze and derive insights from various perspectives, enhancing the overall comprehensiveness of our analysis.\n",
    "\n",
    "- **Veracity**: We have implemented data cleaning and validation processes to ensure the accuracy and reliability of the data. By performing data cleaning and verification steps, we address potential quality issues and increase the veracity of the data used in our analysis.\n",
    "\n",
    "- **Value**: Our project focuses on comparing the performance of basketball players based on statistical data. By analyzing and visualizing this data, we aim to derive valuable insights that contribute to the ongoing debate about the best basketball player. The findings and conclusions drawn from our analysis provide tangible value to basketball enthusiasts, fans, and analysts.\n",
    "\n",
    "### Big Data Engineering:\n",
    "\n",
    "Our project also incorporates key principles of Big Data Engineering, including data handling at different levels:\n",
    "\n",
    "- **Data Source**: We have utilized multiple data sources, including REST API, web scraping, and CSV files, to collect relevant data for our analysis. By leveraging diverse data sources, we ensure a comprehensive and well-rounded analysis.\n",
    "\n",
    "- **Data Storage**: We have employed MongoDB, a NoSQL and document-based database, to store the analyzed and transformed data. MongoDB's scalability and flexibility enable us to handle and manage large volumes of data efficiently.\n",
    "\n",
    "- **Data Analysis**: We have utilized Spark as the core processing framework in our architecture, leveraging its distributed computing capabilities. Spark allows us to perform various data manipulation and analysis tasks on large datasets, facilitating efficient and scalable data processing.\n",
    "\n",
    "- **Data Output**: Our project presents the results of our analysis through visualizations and storytelling. By effectively communicating the insights derived from the data, we provide a coherent and understandable narrative that enhances the overall value of our project.\n",
    "\n",
    "By considering these aspects of Big Data Infrastructure and Engineering, our project demonstrates the ability to handle large volumes of data, process data in real-time, work with diverse data types, ensure data quality and accuracy, and derive valuable insights from the analyzed data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project Setup and Solution Architecture\n",
    "\n",
    "![Architecture](./images/Architecture.jpg)\n",
    "\n",
    "### Architecture:\n",
    "\n",
    "Our project architecture is designed to handle the data processing and analysis tasks in a distributed and scalable manner. The architecture consists of several key components that work together to ensure efficient data ingestion, processing, and storage.\n",
    "\n",
    "- **Data Sources**: We leverage multiple data sources to gather a comprehensive dataset for analysis. These sources include REST-APIs, web data obtained through web scraping, and structured data from CSV files. By incorporating different data types and sources, we enrich our analysis and gain a holistic view of the basketball players' performances.\n",
    "\n",
    "- **Kafka**: Kafka plays a central role in our architecture as a message broker. It acts as a data pipeline, collecting data from various sources using Kafka producers and storing it in Kafka topics. This decouples the data producers from the consumers, enabling efficient data ingestion and processing. With Kafka, we ensure fault tolerance, scalability, and real-time data streaming capabilities.\n",
    "\n",
    "- **Spark**: Spark serves as the core processing framework in our architecture. It is responsible for reading data from Kafka using Kafka consumers and performing various data manipulation and analysis tasks using Spark DataFrames. Spark's distributed computing capabilities allow us to handle large-scale data processing, ensuring high-performance analytics.\n",
    "\n",
    "- **Pandas**: We incorporate the use of Pandas, because it offers extensive data processing capabilities and interoperability with Spark, making it a valuable tool for tasks such as data cleaning and creating visualizations. Its familiar syntax and versatility allow our team to work efficiently with the data.\n",
    "\n",
    "- **Database (MongoDB)**: To store the analyzed and transformed data, we utilize a MongoDB database which was provided by the FH. MongoDB is a NoSQL document-based database that offers flexibility and scalability, making it suitable for handling large volumes of data. Storing the data in MongoDB allows for easy retrieval, querying, and further analysis.\n",
    "\n",
    "- **Jupyter Lab**: Our project environment revolves around Jupyter Lab, which provides an interactive development environment for executing code and maintaining the project. The environment for Jupyter Lab was provided by the FH.\n",
    "\n",
    "- **GitHub**: We adopt GitHub as our version control system to manage code, track changes, and facilitate collaborative development. GitHub ensures that all project members have access to the latest code and can work concurrently on different project components.\n",
    "   \n",
    "### Software Components and Versions\n",
    "\n",
    "Here are the Python Packages we used that helped us during the project:\n",
    "\n",
    "| Packages/Software   | Version       |\n",
    "|---------------------|---------------|\n",
    "| PySpark             | 3.3.2         |\n",
    "| Kafka-Python        | 2.0.2         |\n",
    "| Selenium            | 4.10.0        |\n",
    "| Webdriver-Manager   | 3.8.6         |\n",
    "| PyMongo             | 4.3.3         |\n",
    "| Pandas              | 1.5.3         |\n",
    "| Tabulate            | 0.9.0         |\n",
    "| NumPy               | 1.23.5        |\n",
    "| Matplotlib          | 3.7.1         |\n",
    "| Seaborn             | 0.12.2        |\n",
    "| Python              | 3.10.10       |\n",
    "\n",
    "If you need to install the packages by yourself here are the commands that you can copy and run in your JupyterLab environment:\n",
    "```python\n",
    "!pip install pyspark\n",
    "!pip install kafka-python\n",
    "!pip install selenium\n",
    "!pip install webdriver_manager\n",
    "!pip install pymongo\n",
    "!pip install tabulate\n",
    "!pip install pandas\n",
    "!pip install numpy\n",
    "!pip install matplotlib\n",
    "!pip install seaborn\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Sources\n",
    "\n",
    "We used three different data sources:\n",
    "\n",
    "- \"Ball Don't Lie\" API - [Link](https://www.balldontlie.io/home.html#introduction)\n",
    "- CSV-Data from Kaggle - [Link](https://www.kaggle.com/datasets/drgilermo/nba-players-stats?select=Seasons_Stats.csv)\n",
    "- NBA Stats Website - [Link](https://www.nba.com/stats/players/traditional?PerMode=PerGame&SeasonType=Regular+Season)\n",
    "\n",
    "### API Data:\n",
    "\n",
    "The data of the API was structured as JSON responses and contained data like this i.e.:\n",
    "\n",
    "![API Data](./images/APIData.png)\n",
    "\n",
    "This was our main data source, since the API tracked and updated the data very regularly and had most of the data we wanted in an already well-formatted structure, which was easy to use and clean. We simply sent requests for all the players that played in the specific prime years of the three players that are in focus and processed the responses.\n",
    "\n",
    "### WebScraping:\n",
    "\n",
    "Our second data source was the official website nba.com/stats. To gather the data from this website we used WebScraping. The website looked like this:\n",
    "\n",
    "![Website Data](./images/nbaStatsWebsite.png)\n",
    "\n",
    "\n",
    "### CSV Dataset:\n",
    "\n",
    "Our third and last data source was a CSV-file which was available on the popular Data Science website Kaggle. Here we simply picked a CSV-dataset which had the most complete amount of data and some additional stats that were missing from the other two sources. The data inside the file looked like this:\n",
    "\n",
    "![CSV Data](./images/CSVData.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Kafka Integration\n",
    "\n",
    "![Kafka](./images/Kafka.jpg)\n",
    "\n",
    "![Kafka Workflow](./images/KafkaWorkflow.png)\n",
    "\n",
    "In our project, we leveraged Kafka as a central data broker to streamline the handling of data from various sources. Specifically, we utilized Kafka to efficiently manage the API data and the data obtained through web scraping. By employing Kafka as the backbone of our data pipeline, we were able to establish a scalable and fault-tolerant architecture that facilitated the seamless flow of data throughout our project.\n",
    "\n",
    "To organize the data flow, we created two Kafka topics: one for the API data and another for the web scraping data. The topics served as logical channels where the data producers could publish the relevant information. This allowed us to decouple the data sources from the consumers, enabling asynchronous processing and ensuring data availability for downstream tasks.\n",
    "\n",
    "For pushing data to the Kafka topics, we implemented Kafka producers. These producers were responsible for fetching data from the respective sources and publishing it to the relevant topics in JSON format. In the case of the API data, we utilized the Kafka producer to consume the data from the API endpoints and push it to the corresponding Kafka topic. Similarly, for web scraping, we employed a Kafka producer to retrieve the scraped data, save them in CSV files, read them in and then publish it to the designated topic. This approach enabled us to collect and consolidate the data from multiple sources in a standardized and efficient manner.\n",
    "\n",
    "Here you can see the two Kafka Producers and the WebScraper that collects the data that we wrote:\n",
    "\n",
    "- API Data Producer - [Notebook Path](../Kafka%20API%20PubSub/NBA%20API%20Producer.ipynb)\n",
    "- WebScraper - [Notebook Path](../WebScraper/NBA-Data-WebScraping.ipynb)\n",
    "- WebScraping Data Producer - [Notebook Path](../WebScraper/NBA%20WEB%20Producer.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "\n",
    "Since we took the ETL approach, we consumed or read the data, cleaned our data and then uploaded it to our database.\n",
    "\n",
    "To view the individual Data Cleaning processes we linked the notebooks responsible for that here:\n",
    "\n",
    "- API Data Consuming & Cleaning - [Notebook Path](../Kafka%20API%20PubSub/API%20Data%20Consumer%20with%20Spark.ipynb)\n",
    "- Web Data Consuming & Cleaning - [Notebook Path](../WebScraper/NBA%20WEB%20Consumer.ipynb)\n",
    "- CSV Data Reading & Cleaning - [Notebook Path](../CSV%20Data/CSV-Script.ipynb)\n",
    "\n",
    "![PySpark](./images/PySpark.jpg)\n",
    "\n",
    "In the case of the API Data and the Web Scraping data we consumed the data via Spark directly from Kafka and read it into a Spark Dataframe so that we can support streaming in the future and are open for scalability and speed. For the CSV Data from Kaggle we did not use Kafka, because we did not deem it necessary and because it was static, but we read the data into a Spark Dataframe and cleaned it. In general we used Spark Dataframes for it's versatile capabilities. Most of the work we had to do was changing types of the CSV Data, or certain values that had to be converted into numeric values specifically such as the statistic \"Minutes Played\" which was mostly in a string form like this \"30:26\". We also checked for Null values and duplicate data, but all in all this was very comfortable to do with the power and flexibility of Spark. One of the challenges we faced was figuring out which Year or Season was actually contained in the data, because some datasets had different meanings for a value like \"1995\", i.e. in one dataset that meant the season 1994-1995 and in the other it meant 1995-1996. In these instances we had to do some research and use our own domain knowledge to correctly address these issues."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ETL Output\n",
    "\n",
    "![ETL](./images/ETL.png)\n",
    "\n",
    "After the data was extracted and transformed, we decided to store the analyzed and transformed data in MongoDB, a popular NoSQL database. MongoDB provided us with flexibility and scalability, allowing us to easily handle and query large volumes of data. We created three separate collections within MongoDB to store the different types of data: one for the API data, one for the web scraping data, and another for the CSV data obtained from Kaggle.\n",
    "\n",
    "![MongoDB](./images/mongoDB.png)\n",
    "![MongoCollections](./images/MongoCollections.png)\n",
    "![CleanData](./images/CleanData.png)\n",
    "\n",
    "To interact with MongoDB, we utilized the PyMongo library, which provided us with a Python interface to connect to the MongoDB database and perform operations. PyMongo allowed us to seamlessly insert the transformed data into the respective collections.\n",
    "\n",
    "![PyMongo](./images/PyMongo.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis with Spark"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql.functions import col, sum, mean, min, max, count, expr\n",
    "from pyspark.sql import functions as F\n",
    "from pyspark import SparkContext\n",
    "from tabulate import tabulate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Spark Session using Mongo Spark Connector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "spark = SparkSession.builder \\\n",
    "    .appName(\"NBA Analysis\") \\\n",
    "    .config(\"spark.mongodb.input.uri\", \"mongodb://pt-n20.p4001.w3.cs.technikum-wien.at:4001/nba_data.season_stats_web\") \\\n",
    "    .config(\"spark.mongodb.input.partitioner\", \"MongoSinglePartitioner\") \\\n",
    "    .config(\"spark.mongodb.input.partitionerOptions.partitionKey\", \"_id\") \\\n",
    "    .config(\"spark.jars.packages\", \"org.mongodb.spark:mongo-spark-connector_2.12:3.0.1\") \\\n",
    "    .getOrCreate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data from MongoDB Collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_web = spark.read.format(\"com.mongodb.spark.sql.DefaultSource\").load()\n",
    "df_api = spark.read.format(\"com.mongodb.spark.sql.DefaultSource\").option(\"uri\", \"mongodb://pt-n20.p4001.w3.cs.technikum-wien.at:4001/nba_data.season_stats_api\").load()\n",
    "df_csv = spark.read.format(\"com.mongodb.spark.sql.DefaultSource\").option(\"uri\", \"mongodb://pt-n20.p4001.w3.cs.technikum-wien.at:4001/nba_data.season_stats_csv\").load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join Collections based on Player Name and Season Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_web = df_web.withColumnRenamed('SEASON', 'web_season')\n",
    "df_api = df_api.withColumnRenamed('season', 'api_season')\n",
    "joined_df = df_web.join(df_api, (df_web[\"PLAYER_NAME\"] == df_api[\"full_name\"]) & (df_web[\"web_season\"] == df_api[\"api_season\"]))\n",
    "joined_df = joined_df.join(df_csv, (joined_df[\"PLAYER_NAME\"] == df_csv[\"Full_Name\"]) & (joined_df[\"web_season\"] == df_csv[\"Season\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "columns_to_exclude = [\"_id\",\"web_season\",\"Full_Name\",\"csv_season\",\"api_season\"]\n",
    "filtered_df = joined_df.drop(*columns_to_exclude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- AGE: integer (nullable = true)\n",
      " |-- DD2: double (nullable = true)\n",
      " |-- L: integer (nullable = true)\n",
      " |-- PLAYER_NAME: string (nullable = true)\n",
      " |-- PLUS_MINUS: double (nullable = true)\n",
      " |-- TD3: double (nullable = true)\n",
      " |-- W: integer (nullable = true)\n",
      " |-- ast: double (nullable = true)\n",
      " |-- blk: double (nullable = true)\n",
      " |-- dreb: double (nullable = true)\n",
      " |-- fg3_pct: double (nullable = true)\n",
      " |-- fg3a: double (nullable = true)\n",
      " |-- fg3m: double (nullable = true)\n",
      " |-- fg_pct: double (nullable = true)\n",
      " |-- fga: double (nullable = true)\n",
      " |-- fgm: double (nullable = true)\n",
      " |-- ft_pct: double (nullable = true)\n",
      " |-- fta: double (nullable = true)\n",
      " |-- ftm: double (nullable = true)\n",
      " |-- games_played: integer (nullable = true)\n",
      " |-- min: double (nullable = true)\n",
      " |-- oreb: double (nullable = true)\n",
      " |-- pf: double (nullable = true)\n",
      " |-- pts: double (nullable = true)\n",
      " |-- reb: double (nullable = true)\n",
      " |-- stl: double (nullable = true)\n",
      " |-- turnover: double (nullable = true)\n",
      " |-- 2P: double (nullable = true)\n",
      " |-- 2P%: double (nullable = true)\n",
      " |-- 2PA: double (nullable = true)\n",
      " |-- DBPM: double (nullable = true)\n",
      " |-- DWS: double (nullable = true)\n",
      " |-- FTr: double (nullable = true)\n",
      " |-- GS: double (nullable = true)\n",
      " |-- OBPM: double (nullable = true)\n",
      " |-- OWS: double (nullable = true)\n",
      " |-- PER: double (nullable = true)\n",
      " |-- Pos: string (nullable = true)\n",
      " |-- Season: integer (nullable = true)\n",
      " |-- TS%: double (nullable = true)\n",
      " |-- USG%: double (nullable = true)\n",
      " |-- WS: double (nullable = true)\n",
      " |-- eFG%: double (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "filtered_df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3357"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "intervals = [(1996, 1997), (2005, 2007), (2011, 2013)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are the ratios of the number of players for the three periods?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+---------+-------------+\n",
      "| Interval   |   Count | Share (%)   |\n",
      "|------------+---------+-------------|\n",
      "| 1996-1997  |     763 | 22.73%      |\n",
      "| 2005-2007  |    1287 | 38.34%      |\n",
      "| 2011-2013  |    1307 | 38.93%      |\n",
      "| Total      |    3357 | 100.00%     |\n",
      "+------------+---------+-------------+\n"
     ]
    }
   ],
   "source": [
    "table_data = []\n",
    "total_count = filtered_df.count() \n",
    "total_percentage = 0  \n",
    "for start_year, end_year in intervals:\n",
    "    interval_df = filtered_df.filter((col(\"SEASON\") >= start_year) & (col(\"SEASON\") <= end_year))\n",
    "    count = interval_df.count()\n",
    "    percentage = (count / total_count) * 100  \n",
    "    formatted_percentage = \"{:.2f}%\".format(percentage)  \n",
    "    table_data.append([f\"{start_year}-{end_year}\", count, formatted_percentage])\n",
    "    total_percentage += percentage \n",
    "table_data.append([\"Total\", total_count, \"{:.2f}%\".format(total_percentage)])  \n",
    "print(tabulate(table_data, headers=[\"Interval\", \"Count\", \"Share (%)\"], tablefmt=\"psql\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In total, we have 3,357 records across all intervals. From this total we see that we have the least data from the first Interval and nearly the same percentage by the other two."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Player Demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+--------------+-----------+-----------+\n",
      "| Interval   |   Median_Age |   Min_Age |   Max_Age |\n",
      "|------------+--------------+-----------+-----------|\n",
      "| 1996-1997  |           27 |        18 |        41 |\n",
      "| 2005-2007  |           26 |        18 |        44 |\n",
      "| 2011-2013  |           26 |        19 |        40 |\n",
      "+------------+--------------+-----------+-----------+\n"
     ]
    }
   ],
   "source": [
    "table_data_age = []\n",
    "\n",
    "for interval in intervals:\n",
    "    start_year, end_year = interval\n",
    "    interval_df = filtered_df.filter((col(\"SEASON\") >= start_year) & (col(\"SEASON\") <= end_year))\n",
    "    age_median = interval_df.selectExpr(\"percentile_approx(AGE, 0.5)\").first()[0]\n",
    "#   age_mean = interval_df.select(mean(\"AGE\")).first()[0]\n",
    "    age_min = interval_df.select(min(\"AGE\")).first()[0]\n",
    "    age_max = interval_df.select(max(\"AGE\")).first()[0]\n",
    "    table_data_age.append([f\"{start_year}-{end_year}\", age_median, age_min, age_max])\n",
    "#   table_data_age.append([f\"{start_year}-{end_year}\", age_mean,age_min,age_max])\n",
    "\n",
    "headers = [\"Interval\", \"Median_Age\", \"Min_Age\", \"Max_Age\"]\n",
    "# headers = [\"Interval\", \"Mean_Age\",\"Min_Age\",\"Max_Age\"]\n",
    "print(tabulate(table_data_age, headers=headers, tablefmt=\"psql\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the interval 1996-1997, the median age is 27 years. The youngest age is 18 years, and the oldest player is 41 years. In the interval 2005-2007, the median age is 26 years. The youngest age is 18 years, and the oldest player is 44 years. In the interval 2011-2013, the median age is also 26 years. The youngest age is 19 years, and the oldest player is 40 years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Which Player Positions are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n",
      "['SF-PF', 'SG-PF', 'PF', 'SF-SG', 'PF-C', 'PG', 'SG-PG', 'C-PF', 'SF', 'C', 'SG-SF', 'SG', 'PG-SG', 'PF-SF', 'C-SF']\n"
     ]
    }
   ],
   "source": [
    "filtered_df.createOrReplaceTempView(\"temp_table\")\n",
    "count_query = \"\"\"SELECT COUNT(DISTINCT Pos) AS count FROM temp_table \"\"\"\n",
    "count_result = spark.sql(count_query).collect()[0][\"count\"]\n",
    "print(count_result)\n",
    "distinct_query = \"\"\"SELECT DISTINCT Pos FROM temp_table\"\"\"\n",
    "distinct_result = spark.sql(distinct_query).rdd.flatMap(lambda x: x).collect()\n",
    "print(distinct_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to determine the total number of distinct positions held by the players in our dataset, without any limitations based on years. Upon analyzing the data, we observed that the positions recorded for the players can be single positions or combinations of multiple positions. In total, we found that there are 15 different types of positions that the players have in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+------------------+------------------+------------------+------------------+------------------+\n",
      "| Interval   | Position Top 1   | Position Top 2   | Position Top 3   | Position Top 4   | Position Top 5   |\n",
      "|------------+------------------+------------------+------------------+------------------+------------------|\n",
      "| 1996-1997  | PF - 23.07%      | SG - 19.53%      | PG - 19.40%      | SF - 18.74%      | C - 18.61%       |\n",
      "| 2005-2007  | PF - 22.07%      | C - 20.28%       | SG - 19.43%      | PG - 19.11%      | SF - 16.47%      |\n",
      "| 2011-2013  | PF - 20.28%      | C - 20.28%       | SF - 20.12%      | SG - 20.05%      | PG - 18.06%      |\n",
      "+------------+------------------+------------------+------------------+------------------+------------------+\n"
     ]
    }
   ],
   "source": [
    "table_data_pos = []\n",
    "\n",
    "for interval in intervals:\n",
    "    start_year, end_year = interval\n",
    "\n",
    "    interval_df = filtered_df.filter((col(\"SEASON\") >= start_year) & (col(\"SEASON\") <= end_year))\n",
    "    pos_counts = interval_df.groupBy(\"Pos\").agg(count(\"*\").alias(\"Count\")).orderBy(col(\"Count\").desc())\n",
    "\n",
    "    total_players = interval_df.count()\n",
    "    top_5_positions = pos_counts.limit(5).collect()\n",
    "\n",
    "    top_5_names = [row[\"Pos\"] for row in top_5_positions]\n",
    "    top_5_percentages = [(row[\"Count\"] / total_players) * 100 for row in top_5_positions]\n",
    "\n",
    "    row_data = [f\"{start_year}-{end_year}\"] + [\"{} - {:.2f}%\".format(name, percentage) for name, percentage in zip(top_5_names, top_5_percentages)]\n",
    "    table_data_pos.append(row_data)\n",
    "\n",
    "headers = [\"Interval\"] + [\"Position Top {}\".format(i) for i in range(1, 6)]\n",
    "print(tabulate(table_data_pos, headers=headers, tablefmt=\"psql\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, the analysis highlights a positive trend for Power Forward (PF) and Center (C) positions, with PF consistently being the most popular position in all intervals. Shooting Guards (SG) show a declining trend in popularity, particularly in the second interval, where it is overtaken by Centers (C). Small Forwards (SF) maintain a relatively stable position throughout the intervals. The Point Guard (PG) position remains in the top five but experiences slight fluctuations in popularity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Winners vs Losers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+-------------------------+---------------------------+\n",
      "| Interval   | Player with Most Wins   | Player with Most Losses   |\n",
      "|------------+-------------------------+---------------------------|\n",
      "| 1996-1997  | Michael Jordan - 131    | Shareef Abdur-Rahim - 130 |\n",
      "| 2005-2007  | Bruce Bowen - 177       | Ricky Davis - 165         |\n",
      "| 2011-2013  | Kevin Durant - 165      | Bismack Biyombo - 151     |\n",
      "+------------+-------------------------+---------------------------+\n"
     ]
    }
   ],
   "source": [
    "table_data_wl = []\n",
    "for interval in intervals:\n",
    "    start_year, end_year = interval\n",
    "\n",
    "    interval_df = filtered_df.filter((col(\"SEASON\") >= start_year) & (col(\"SEASON\") <= end_year))\n",
    "    player_stats = interval_df.groupBy(\"PLAYER_NAME\").agg(\n",
    "        F.sum(\"W\").alias(\"TotalWins\"), F.sum(\"L\").alias(\"TotalLosses\"))\n",
    "\n",
    "    top_player_wins = player_stats.orderBy(F.desc(\"TotalWins\")).limit(1).select(\"PLAYER_NAME\", \"TotalWins\").collect()\n",
    "    top_player_losses = player_stats.orderBy(F.desc(\"TotalLosses\")).limit(1).select(\"PLAYER_NAME\", \"TotalLosses\").collect()\n",
    "\n",
    "    row_data = [f\"{start_year}-{end_year}\"] + [\"{} - {}\".format(row.PLAYER_NAME, row.TotalWins) for row in top_player_wins]\n",
    "    row_data += [\"{} - {}\".format(row.PLAYER_NAME, row.TotalLosses) for row in top_player_losses]\n",
    "    table_data_wl.append(row_data)\n",
    "\n",
    "headers = [\"Interval\", \"Player with Most Wins\", \"Player with Most Losses\"]\n",
    "print(tabulate(table_data_wl, headers=headers, tablefmt=\"psql\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 1996-1997 Interval: The player with the most wins during this interval is Michael Jordan, with 131 wins. The player with the most losses during this interval is Shareef Abdur-Rahim, with 130 losses.\n",
    "\n",
    "- 2005-2007 Interval: Bruce Bowen has the highest number of wins during this interval, with 177 wins. Ricky Davis has the highest number of losses, with 165 losses.\n",
    "\n",
    "- 2011-2013 Interval: Kevin Durant achieved the most wins during this interval, with 165 wins. Bismack Biyombo has the highest number of losses, with 151 losses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Five Main Stats (Points, Rebounds, Assists, Steals and Blocks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+--------------+--------------+--------------+--------------+--------------+\n",
      "| Interval   |   Median_pts |   Median_reb |   Median_ast |   Median_stl |   Median_blk |\n",
      "|------------+--------------+--------------+--------------+--------------+--------------|\n",
      "| 1996-1997  |         6.03 |         2.66 |         1.13 |         0.6  |         0.24 |\n",
      "| 2005-2007  |         6.48 |         2.89 |         1.13 |         0.53 |         0.24 |\n",
      "| 2011-2013  |         6.58 |         2.93 |         1.12 |         0.57 |         0.27 |\n",
      "+------------+--------------+--------------+--------------+--------------+--------------+\n"
     ]
    }
   ],
   "source": [
    "table_data_stats = []\n",
    "\n",
    "for interval in intervals:\n",
    "    start_year, end_year = interval\n",
    "    interval_df = filtered_df.filter((col(\"SEASON\") >= start_year) & (col(\"SEASON\") <= end_year))\n",
    "    pts = interval_df.select(expr(\"percentile_approx(pts, 0.5)\").alias(\"Median_pts\")).first()[\"Median_pts\"]\n",
    "    reb = interval_df.select(expr(\"percentile_approx(reb, 0.5)\").alias(\"Median_reb\")).first()[\"Median_reb\"]\n",
    "    ast = interval_df.select(expr(\"percentile_approx(ast, 0.5)\").alias(\"Median_ast\")).first()[\"Median_ast\"]\n",
    "    stl = interval_df.select(expr(\"percentile_approx(stl, 0.5)\").alias(\"Median_stl\")).first()[\"Median_stl\"]\n",
    "    blk = interval_df.select(expr(\"percentile_approx(blk, 0.5)\").alias(\"Median_blk\")).first()[\"Median_blk\"]\n",
    "    table_data_stats.append([f\"{start_year}-{end_year}\", pts, reb, ast, stl, blk])\n",
    "\n",
    "headers = [\"Interval\", \"Median_pts\", \"Median_reb\", \"Median_ast\", \"Median_stl\", \"Median_blk\"]\n",
    "print(tabulate(table_data_stats, headers=headers, tablefmt=\"psql\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Points: The median points scored by players during the intervals range from approximately 6.03 to 6.58. \n",
    "- Rebounds: The median rebounds for players range from approximately 2.66 to 2.93. \n",
    "- Assists: The median assists for players range from approximately 1.12 to 1.13. \n",
    "- Steals: The median steals achieved by players range from approximately 0.53 to 0.6. \n",
    "- Blocks: The median blocks for players range from approximately 0.24 to 0.27. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most Prolific 2-Point Percentages and 3-Point Percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+---------------------------+----------------------------+\n",
      "| Interval   | Player with Highest 2P%   | Player with Highest 3FG%   |\n",
      "|------------+---------------------------+----------------------------|\n",
      "| 1996-1997  | Michael Jordan - 49.45%   | Michael Jordan - 30.60%    |\n",
      "| 2005-2007  | Kevin Garnett - 52.13%    | Richard Hamilton - 41.30%  |\n",
      "| 2011-2013  | LeBron James - 59.33%     | Kevin Durant - 39.80%      |\n",
      "+------------+---------------------------+----------------------------+\n"
     ]
    }
   ],
   "source": [
    "table_data_pct = []\n",
    "\n",
    "for interval in intervals:\n",
    "    start_year, end_year = interval\n",
    "    \n",
    "    interval_df = filtered_df.filter((col(\"SEASON\") >= start_year) & (col(\"SEASON\") <= end_year))\n",
    "    player_stats = interval_df.groupBy(\"PLAYER_NAME\").agg(\n",
    "        F.avg(\"2P%\").alias(\"2P%\"), F.avg(\"fg3_pct\").alias(\"3FG%\"), F.sum(\"2PA\").alias(\"2PA\"))\n",
    "\n",
    "    filtered_player_stats = player_stats.filter(player_stats[\"2PA\"] >= 3000)\n",
    "    top_player_2p_pct = filtered_player_stats.orderBy(F.desc(\"2P%\")).limit(1).select(\"PLAYER_NAME\", \"2P%\").collect()\n",
    "    top_player_fg_pct = filtered_player_stats.orderBy(F.desc(\"3FG%\")).limit(1).select(\"PLAYER_NAME\", \"3FG%\").collect()\n",
    "\n",
    "    row_data = [f\"{start_year}-{end_year}\"] + [\"{} - {:.2f}%\".format(row.PLAYER_NAME, row[\"2P%\"]*100) for row in top_player_2p_pct]\n",
    "    row_data += [\"{} - {:.2f}%\".format(row.PLAYER_NAME, row[\"3FG%\"]*100) for row in top_player_fg_pct]\n",
    "    table_data_pct.append(row_data)\n",
    "\n",
    "headers = [\"Interval\", \"Player with Highest 2P%\", \"Player with Highest 3FG%\"]\n",
    "print(tabulate(table_data_pct, headers=headers, tablefmt=\"psql\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1995-1997 Interval:\n",
    "Michael Jordan had the highest 2-point shooting percentage during this interval, with an accuracy of 49.45%.\n",
    "Michael Jordan also had the highest 3-point shooting percentage, with a success rate of 30.60%.\n",
    "\n",
    "2005-2007 Interval:\n",
    "Kevin Garnett achieved the highest 2-point shooting percentage during this interval, with an accuracy of 52.13%.\n",
    "Richard Hamilton had the highest 3-point shooting percentage, with a success rate of 41.30%.\n",
    "\n",
    "2011-2013 Interval:\n",
    "LeBron James had the highest 2-point shooting percentage during this interval, with an exceptional accuracy of 59.33%.\n",
    "Kevin Durant achieved the highest 3-point shooting percentage, with a success rate of 39.80%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation and Analysis\n",
    "\n",
    "Now that we have collected and processed the data, it's time to derive insights and draw meaningful conclusions. By leveraging visualizations and analysis techniques, we can uncover patterns, trends, and comparisons to gain a deeper understanding of the players' performances during the specified periods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert Spark Dataframe to Pandas Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "nba_stats_df = filtered_df.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AGE               int32\n",
       "DD2             float64\n",
       "L                 int32\n",
       "PLAYER_NAME      object\n",
       "PLUS_MINUS      float64\n",
       "TD3             float64\n",
       "W                 int32\n",
       "ast             float64\n",
       "blk             float64\n",
       "dreb            float64\n",
       "fg3_pct         float64\n",
       "fg3a            float64\n",
       "fg3m            float64\n",
       "fg_pct          float64\n",
       "fga             float64\n",
       "fgm             float64\n",
       "ft_pct          float64\n",
       "fta             float64\n",
       "ftm             float64\n",
       "games_played      int32\n",
       "min             float64\n",
       "oreb            float64\n",
       "pf              float64\n",
       "pts             float64\n",
       "reb             float64\n",
       "stl             float64\n",
       "turnover        float64\n",
       "2P              float64\n",
       "2P%             float64\n",
       "2PA             float64\n",
       "DBPM            float64\n",
       "DWS             float64\n",
       "FTr             float64\n",
       "GS              float64\n",
       "OBPM            float64\n",
       "OWS             float64\n",
       "PER             float64\n",
       "Pos              object\n",
       "Season            int32\n",
       "TS%             float64\n",
       "USG%            float64\n",
       "WS              float64\n",
       "eFG%            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba_stats_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Domain Context\n",
    "\n",
    "To truly analyze the impact and value of a player, it is extremely challenging to rely solely on numbers. Basketball, like any sport, involves intangible factors that can greatly contribute to a player's performance and overall impact. However, certain statistical categories can provide valuable insights into a player's skills and effectiveness on the court. In this analysis, we will focus on key categories that can help us understand why the three players excelled during these specific years. We will first look at how they performed individually compared to the league and in the second part compare the three players specifically and measure their overall impact."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Individual Impact\n",
    "\n",
    "For the first part of the analysis the selected key categories are as follows:\n",
    "\n",
    "- Points (PTS)\n",
    "- Assists (AST)\n",
    "- Rebounds (REB)\n",
    "- Steals (STL)\n",
    "- Blocks (BLK)\n",
    "- Effective Field Goal Percentage (eFG%)\n",
    "- True Shooting Percentage (TS%)\n",
    "\n",
    "By examining these categories, we aim to gain insights into the exceptional individual performances during the players' prime periods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Air Jordan: Michael Jordan\n",
    "Michael Jordan is widely regarded as one of the greatest basketball players of all time. He spent the majority of his career with the Chicago Bulls in the NBA (National Basketball Association). Known for his incredible athleticism, competitiveness, and clutch performances, Jordan revolutionized the game and became a global icon.\n",
    "\n",
    "During the seasons we take a look it he made the following achievement:\n",
    "\n",
    "- NBA champion (1996–1998)\n",
    "- NBA Most Valuable (1996, 1998)\n",
    "- NBA All-Defensive First Team (1996–1998)\n",
    "- NBA scoring champion (1996–1998)\n",
    "\n",
    "**Note: The dataset from the official NBA website only provides data that goes up until 1996. However, 1995 is also considered one of Michael Jordan's prime years. For this reason, only two seasons are available for Michael Jordan.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Preparation\n",
    "\n",
    "Before we start to analyse we need to define the dataframes containing the season stats of those individual years in which Michael Jordan was at his peak. To do to that we use following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "prime_1996_1997_df = nba_stats_df[nba_stats_df['Season'].isin([1996,1997])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we save Michael Jordan's stats in a dataframe of his own for easier analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>DD2</th>\n",
       "      <th>L</th>\n",
       "      <th>PLAYER_NAME</th>\n",
       "      <th>PLUS_MINUS</th>\n",
       "      <th>TD3</th>\n",
       "      <th>W</th>\n",
       "      <th>ast</th>\n",
       "      <th>blk</th>\n",
       "      <th>dreb</th>\n",
       "      <th>...</th>\n",
       "      <th>GS</th>\n",
       "      <th>OBPM</th>\n",
       "      <th>OWS</th>\n",
       "      <th>PER</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Season</th>\n",
       "      <th>TS%</th>\n",
       "      <th>USG%</th>\n",
       "      <th>WS</th>\n",
       "      <th>eFG%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1369</th>\n",
       "      <td>35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20</td>\n",
       "      <td>Michael Jordan</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0.55</td>\n",
       "      <td>4.21</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>10.4</td>\n",
       "      <td>25.2</td>\n",
       "      <td>SG</td>\n",
       "      <td>1997</td>\n",
       "      <td>0.533</td>\n",
       "      <td>33.7</td>\n",
       "      <td>15.8</td>\n",
       "      <td>0.473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1759</th>\n",
       "      <td>34</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13</td>\n",
       "      <td>Michael Jordan</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>69</td>\n",
       "      <td>4.29</td>\n",
       "      <td>0.54</td>\n",
       "      <td>4.50</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>13.3</td>\n",
       "      <td>27.8</td>\n",
       "      <td>SG</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.567</td>\n",
       "      <td>33.2</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0.516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      AGE  DD2   L     PLAYER_NAME  PLUS_MINUS  TD3   W   ast   blk  dreb  \\\n",
       "1369   35  5.0  20  Michael Jordan         7.3  0.0  62  3.45  0.55  4.21   \n",
       "1759   34  9.0  13  Michael Jordan        10.0  1.0  69  4.29  0.54  4.50   \n",
       "\n",
       "      ...    GS  OBPM   OWS   PER  Pos  Season    TS%  USG%    WS   eFG%  \n",
       "1369  ...  82.0   4.6  10.4  25.2   SG    1997  0.533  33.7  15.8  0.473  \n",
       "1759  ...  82.0   6.5  13.3  27.8   SG    1996  0.567  33.2  18.3  0.516  \n",
       "\n",
       "[2 rows x 43 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mj_prime_stats_df = prime_1996_1997_df[prime_1996_1997_df['PLAYER_NAME'] == 'Michael Jordan']\n",
    "mj_prime_stats_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Points\n",
    "\n",
    "In this section, we will analyze the points scored by players during the 1996-1998 NBA seasons. Specifically, we will focus on comparing Michael Jordan's scoring performance to that of the league as a whole:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(prime_1996_1997_df['pts'], vert=False)\n",
    "\n",
    "# Plot MJ's stats as red markers\n",
    "mj_points =mj_prime_stats_df['pts']\n",
    "for point in mj_points:\n",
    "    plt.plot(point, 1, 'ro')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title('Boxplot of Points in the 1996-1997 NBA Seasons')\n",
    "plt.xlabel('Points')\n",
    "plt.ylabel('Season')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           League  Michael Jordan\n",
      "count  763.000000        2.000000\n",
      "mean     7.523224       29.195000\n",
      "std      5.511085        0.643467\n",
      "min      0.000000       28.740000\n",
      "25%      3.110000       28.967500\n",
      "50%      6.030000       29.195000\n",
      "75%     10.820000       29.422500\n",
      "max     29.650000       29.650000\n"
     ]
    }
   ],
   "source": [
    "# Create summaries for 'pts' column in both DataFrames\n",
    "summary_league = prime_1996_1997_df['pts'].describe()\n",
    "summary_mj = mj_prime_stats_df['pts'].describe()\n",
    "\n",
    "# Concatenate the summaries horizontally\n",
    "summary_df = pd.concat([summary_league, summary_mj], axis=1)\n",
    "summary_df.columns = ['League', 'Michael Jordan']\n",
    "\n",
    "# Print the summary DataFrame\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be observed that Michael Jordan's average score of 29.19 is significantly higher than the league average of 7.52 during the years under consideration. The Boxplot not only highlights the difference between Michael Jordan and his colleagues but also emphasizes his outstanding performance when comparing the median (yellow line) and Jordan's point scores (red dots). Additionally, it is evident that Jordan achieved the maximum point scores in the seasons, further underlining his exceptional achievements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assists\n",
    "Michael Jordan was not only known for his scoring ability but also for his ability to make assists. He was a talented playmaker who could both set up his teammates and score himself. He had a career average of 5.3 assists per game, showcasing his versatility and ability to be both a scorer and a team player."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(prime_1996_1997_df['ast'], vert=False)\n",
    "\n",
    "# Plot Michael Jordan's assists as red markers\n",
    "mj_assists = mj_prime_stats_df['ast']\n",
    "\n",
    "for assist in mj_assists:\n",
    "    plt.plot(assist, 1, 'ro')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title('Boxplot of Assists in the 2005-2007 NBA Seasons (Shooting Guards)')\n",
    "plt.xlabel('Assists')\n",
    "plt.ylabel('Season')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           League  Michael Jordan\n",
      "count  763.000000         2.00000\n",
      "mean     1.725374         3.87000\n",
      "std      1.769907         0.59397\n",
      "min      0.000000         3.45000\n",
      "25%      0.510000         3.66000\n",
      "50%      1.130000         3.87000\n",
      "75%      2.445000         4.08000\n",
      "max     11.400000         4.29000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_1996_1997_df['ast'].describe()\n",
    "summary_mj = mj_prime_stats_df['ast'].describe()\n",
    "\n",
    "# Concatenate the summaries horizontally\n",
    "summary_df = pd.concat([summary_league, summary_mj], axis=1)\n",
    "summary_df.columns = ['League', 'Michael Jordan']\n",
    "\n",
    "# Print the summary DataFrame\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also see in terms of assists that Michael Jordan surpasses the average of his colleagues. He ranks in the upper quartile of the league in terms of assists, although not to the same extent as when comparing his scoring. Nevertheless, he averaged more than double the number of assists compared to the league average."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rebounds\n",
    "\n",
    "Now let's take a look at the rebounds. So far, Michael Jordan's numbers have been impressive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter the DataFrame for shooting guards\n",
    "shooting_guards_df = prime_1996_1997_df[prime_1996_1997_df['Pos'] == 'SG']\n",
    "\n",
    "# Calculate the league average rebounds for shooting guards\n",
    "league_avg_rebounds_sg = shooting_guards_df['reb'].mean()\n",
    "\n",
    "# Calculate the league average rebounds for each season\n",
    "league_avg_rebounds_1996 = prime_1996_1997_df[prime_1996_1997_df['Season'] == 1996]['reb'].mean()\n",
    "league_avg_rebounds_1997 = prime_1996_1997_df[prime_1996_1997_df['Season'] == 1997]['reb'].mean()\n",
    "\n",
    "# Plot the league average rebounds for shooting guards as three distinct points\n",
    "plt.plot([1996, 1997], [league_avg_rebounds_1996, league_avg_rebounds_1997],\n",
    "         color='blue', marker='o', linestyle='--', label='League Average (SG)')\n",
    "\n",
    "# Plot Kobe Bryant's rebounds as a line graph\n",
    "mj_rebounds = mj_prime_stats_df['reb']\n",
    "seasons = mj_rebounds.index\n",
    "\n",
    "plt.plot([1996, 1997], mj_rebounds, color='red', marker='o', label='Michael Jordan (SG)')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title('Rebounds in the 1996-1997 NBA Seasons (Shooting Guards)')\n",
    "plt.xlabel('Season')\n",
    "plt.ylabel('Rebounds')\n",
    "plt.legend()\n",
    "\n",
    "# Set the x-axis tick positions and labels\n",
    "plt.xticks([1996, 1997], ['1996','1997'])\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           League  Michael Jordan\n",
      "count  763.000000         2.00000\n",
      "mean     3.364993         5.83500\n",
      "std      2.438323         0.06364\n",
      "min      0.000000         5.79000\n",
      "25%      1.530000         5.81250\n",
      "50%      2.660000         5.83500\n",
      "75%      4.555000         5.85750\n",
      "max     13.580000         5.88000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_1996_1997_df['reb'].describe()\n",
    "summary_mj = mj_prime_stats_df['reb'].describe()\n",
    "\n",
    "# Concatenate the summaries horizontally\n",
    "summary_df = pd.concat([summary_league, summary_mj], axis=1)\n",
    "summary_df.columns = ['League', 'Michael Jordan']\n",
    "\n",
    "# Print the summary DataFrame\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, Michael Jordan excels with nearly double the number of rebounds. We can observe that his rebound count increased from the 1996 season to the season 1997."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Steals\n",
    "\n",
    "So far, we have looked at statistics that considered Michael Jordan independent of his opponents. But basketball is a team sport, and the question arises: Can Michael Jordan prevail against his opponents? To investigate this, let's take a closer look at steals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the steals per game for all players\n",
    "steals_per_game = prime_1996_1997_df.groupby('PLAYER_NAME')['stl'].mean()\n",
    "\n",
    "# Sort the data by steals per game in descending order\n",
    "sorted_steals = steals_per_game.sort_values(ascending=False)\n",
    "\n",
    "# Determine the position of Michael Jordan in the sorted list\n",
    "jordan_position = sorted_steals.index.get_loc('Michael Jordan')\n",
    "\n",
    "# Create the bar chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(range(len(sorted_steals)), sorted_steals, color='blue')\n",
    "plt.axhline(y=sorted_steals.iloc[jordan_position], color='red', linestyle='--', label='Michael Jordan')\n",
    "\n",
    "# Set the title and axis labels\n",
    "plt.title('Steals per Game')\n",
    "plt.xlabel('Player')\n",
    "plt.ylabel('Steals per Game')\n",
    "\n",
    "# Label Michael Jordan's position\n",
    "plt.text(jordan_position, sorted_steals.iloc[jordan_position], 'Michael Jordan', ha='center', va='bottom')\n",
    "\n",
    "# Remove the player labels at the bottom\n",
    "plt.xticks([])\n",
    "\n",
    "# Show the chart\n",
    "plt.tight_layout()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           League  Michael Jordan\n",
      "count  763.000000        2.000000\n",
      "mean     0.691913        1.715000\n",
      "std      0.489242        0.007071\n",
      "min      0.000000        1.710000\n",
      "25%      0.330000        1.712500\n",
      "50%      0.600000        1.715000\n",
      "75%      0.955000        1.717500\n",
      "max      2.720000        1.720000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_1996_1997_df['stl'].describe()\n",
    "summary_mj = mj_prime_stats_df['stl'].describe()\n",
    "\n",
    "# Concatenate the summaries horizontally\n",
    "summary_df = pd.concat([summary_league, summary_mj], axis=1)\n",
    "summary_df.columns = ['League', 'Michael Jordan']\n",
    "\n",
    "# Print the summary DataFrame\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The answer to the question is clearly: Yes, he can. We can see that the red line representing Michael Jordan's steal value is situated in the significantly sparser, higher range. The arithmetic mean of steals for Michael Jordan is again more than double that of the rest of the league members. In terms of the median, we have a value that is even three times higher than the league's average."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Blocks\n",
    "\n",
    "It is evident that Michael Jordan was a master at scoring. However, when it comes to defending against the opposing team, shooting guards, in general, are not typically focused on blocking. Therefore, it would be appropriate to compare Jordan only with other shooting guards in the league."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter the data for sf in the league\n",
    "sg_df = prime_1996_1997_df[prime_1996_1997_df['Pos'] == 'SG']\n",
    "\n",
    "# Calculate the blocks per game for sf\n",
    "sg_blocks = sg_df.groupby('PLAYER_NAME')['blk'].mean()\n",
    "\n",
    "# Sort the data by blocks per game in descending order\n",
    "sorted_blocks = sg_blocks.sort_values(ascending=False)\n",
    "\n",
    "# Determine the position of Michael Jordan in the sorted list\n",
    "jordan_position = sorted_blocks.index.get_loc('Michael Jordan')\n",
    "\n",
    "# Create the bar chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(range(len(sorted_blocks)), sorted_blocks, color='blue')\n",
    "plt.axhline(y=sorted_blocks.iloc[jordan_position], color='red', linestyle='--', label='Michael Jordan')\n",
    "\n",
    "# Set the title and axis labels\n",
    "plt.title('Blocks per Game - Shooting Guard')\n",
    "plt.xlabel('Player')\n",
    "plt.ylabel('Blocks per Game')\n",
    "\n",
    "# Label Michael Jordan's position\n",
    "plt.text(jordan_position, sorted_blocks.iloc[jordan_position], 'Michael Jordan', ha='center', va='bottom')\n",
    "\n",
    "# Remove the player labels at the bottom\n",
    "plt.xticks([])\n",
    "\n",
    "# Show the chart\n",
    "plt.tight_layout()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           League  Michael Jordan\n",
      "count  763.000000        2.000000\n",
      "mean     0.396553        0.545000\n",
      "std      0.479174        0.007071\n",
      "min      0.000000        0.540000\n",
      "25%      0.100000        0.542500\n",
      "50%      0.240000        0.545000\n",
      "75%      0.510000        0.547500\n",
      "max      3.650000        0.550000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_1996_1997_df['blk'].describe()\n",
    "summary_mj = mj_prime_stats_df['blk'].describe()\n",
    "\n",
    "# Concatenate the summaries horizontally\n",
    "summary_df = pd.concat([summary_league, summary_mj], axis=1)\n",
    "summary_df.columns = ['League', 'Michael Jordan']\n",
    "\n",
    "# Print the summary DataFrame\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see here that Michael Jordan, compared to other shooting guards, still comes out on top, but the differences are not as pronounced. While other shooting guards averaged 0.4 blocks per game, Jordan averaged 0.54 blocks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Effective Field Goal Percentage\n",
    "\n",
    "Now, let's delve into Michael Jordan's eFG% (Effective Field Goal Percentage) to examine his shooting accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mj_efg_avg = mj_prime_stats_df.groupby('Season')['eFG%'].mean()\n",
    "league_avg_efg = prime_1996_1997_df['eFG%'].mean()\n",
    "plt.bar(mj_efg_avg.index, mj_efg_avg, color='red', label='Michael Jordan')\n",
    "plt.axhline(league_avg_efg, color='blue', linestyle='--', label='League Average')\n",
    "plt.title('Effective Field Goal% Michael Jordan compared to League Average')\n",
    "plt.xlabel('Season')\n",
    "plt.ylabel('(eFG%)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           League  Michael Jordan\n",
      "count  763.000000        2.000000\n",
      "mean     0.456705        0.494500\n",
      "std      0.091666        0.030406\n",
      "min      0.000000        0.473000\n",
      "25%      0.427500        0.483750\n",
      "50%      0.469000        0.494500\n",
      "75%      0.502500        0.505250\n",
      "max      1.000000        0.516000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_1996_1997_df['eFG%'].describe()\n",
    "summary_mj = mj_prime_stats_df['eFG%'].describe()\n",
    "\n",
    "# Concatenate the summaries horizontally\n",
    "summary_df = pd.concat([summary_league, summary_mj], axis=1)\n",
    "summary_df.columns = ['League', 'Michael Jordan']\n",
    "\n",
    "# Print the summary DataFrame\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Michael Jordan's eFG% is significantly higher compared to his counterparts in the league."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### True Shooting Percentage\n",
    "\n",
    "Now that we have examined the Effective Field Goal Percentage, let's shift our attention to an advanced metric - the True Shooting Percentage (TS%). This expanded statistic provides us with an even more comprehensive insight into players' scoring efficiency by taking into account not only field goals but also three-pointers and free throws."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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48KAAIFauXClXf+7cuQrHBGV/p5Sh8aE0ypUrh+bNm5d4/gMHDqBZs2ZwdXVFbm6u7NW2bVsAwMmTJ4udv127doiOjsbevXsxbtw4vPfee9i3bx86deqEkSNHKtTv1KmT3Ps6deoAAB48eADgZUeIjIwMhUtWbm5uaN68OY4fPw4AuHnzJp48eYJ+/frJXT61tLREt27d8NdffyE9PV21jfGK9u3bw9DQsMg4b968idjYWPTs2VNuvkqVKqFRo0ZKr+fq1aswNjaGsbExXF1dsXjxYrl7m1T9fDp16iR3n9qtW7dw9+5dDB48GFKptNAYMjMzcfz4cXTt2hXm5uZy62nXrh0yMzMVLum+6XN8kxMnTqBly5awsbGBoaEhjI2NMW3aNMTHxyMuLk6uba9v4+7du8PISP52ztJ+j181aNAgnD9/HleuXMG6detQtWpVNGnSRKl5z5w5g+TkZAwfPvyNPSIlEgk6duwoV1anTh2FbajMtjp8+DByc3PRv39/ufZLpVIEBgaqpVe3svvmq7p16yb3vuBS2scffyxX3rdvX4V5c3Nz8d1338Hb2xsmJiYwMjKCiYkJbt++XegtDiX5Tqanp+PkyZPo2bNnsfcOnjhxAi1atICbm5tceXBwMNLT0+VupQCg0FOuZs2aAF4eV14vT0hIULi02aJFCzg5OcneGxoaolevXrhz5w4ePXokK1+1ahV8fHwglUphZGQEY2NjHD9+vMjt8+qx4c6dO7hx44bss3h9v4+JiZFdFg4LCysyptIo6Hzz+neqR48esLCwKPQ7pQpV2ggotz1PnjwJKysrtGnTRm5dffr0KXGcBw4cgK2tLTp27CgXY7169eDs7PzG/VcIIbuU2apVKwAvb+Np2rQpdu/ejeTkZAAv72l2dnbGhg0bZPMePnwYT548kd0SVRBPaX53Cvz6669o1KgRLC0tZdtz3bp1CtsTUDzOv749S/I7VRyNJ2el7SX29OlT/P7777IEoeD13nvvAYBSXZTNzMzQpUsXLFy4ECdPnsSdO3fg7e2N5cuX4+rVq3J17e3t5d4X3DSYkZEBoPj72FxdXWXT31QvPz8fL168eGPsRVE2zlcPVgUKKytK1apVERERgfPnz+Pff/9FYmIitmzZAhsbGwCqfz6vb4+C+xUqVqxYZAzx8fHIzc3Fjz/+qLCedu3aFbqeN22f4pw7dw6tW7cG8PKevdOnTyMiIgKTJ0+WW0ZR29jIyEhh/er4Hhdo0qQJqlevjtWrV+OXX37BoEGDlBp6AlBuexcwNzdXSJhNTU2RmZkpe6/stnr69CkA4P3331fYBiEhIWoZakDZffPV9r3ewzU+Pr7Qz8/Z2VlhmWPGjMHUqVPRpUsX/P777/j7778RERGBunXrFvo9K8l38sWLF8jLy3vj5xUfH19kuwumv8rOzk7uvYmJSbHlr37mQOHbo6CsYF1LlizBsGHD4O/vj927d+Ovv/5CREQE2rRpU2ibX4+/4Dszbtw4he/M8OHDAfy338THxxcbU0kVfB9eT4wlEgmcnZ0VtquqVGmjstszPj6+1Mf9wuJMTEyEiYmJQpyxsbFv3H9PnDiBqKgo9OjRA8nJyUhMTERiYiJ69uyJ9PR0bN++HcDLY2e/fv2wd+9e2T19GzduhIuLC4KCguTiKc3vDgDs2bMHPXv2RIUKFbBlyxacPXsWERERGDRokNz3veA78Pq+8fr2LMnvVHFK3FtTWUX9aJiamircWAwoHkQcHBxQp04dzJkzp9DlFBx8VFGpUiV8/vnnGD16NK5evSr7QJVRcICNiYlRmPbkyRM4ODgoVc/AwADlypVTOXZV4yzY+V8VGxur9HKkUin8/PyKnK7q5/P696HgoPfqf9uvK1euHAwNDdGvXz+MGDGi0DoeHh5Fzq+qHTt2wNjYGAcOHJBLTvbt2ydX79VtXKFCBVl5bm6uxr/HAwcOxJQpUyCRSDBgwACl51Nme6tC2W1VsF/s2rUL7u7ualn365TdNwsUdmyyt7eXfX6vJlOF7TNbtmxB//798d1338mVP3/+HLa2tiVpggI7OzsYGhq+8fOyt7cvst0AFNpeWoVtj4Kygu22ZcsWNG3aFCtXrpSrl5KSUugyX/88CmKeNGkSPvroo0Ln8fT0lK2zuJhKquD78OzZM7kETQiB2NhYvP/++6VaviptVHZ72tvb49y5cwrLKWxbSKVSJCUlKZS/nkQ4ODjA3t4ehw4dKjRGKyurQssLrFu3DsDLBHPJkiWFTh8yZAiAl8e2hQsXYseOHejVqxf279+P0aNHy10pKu3vDvBye3p4eCAkJERu+ut5ScF3ICEhQS5Be317qvt3SuPJWVEqV66My5cvy5WdOHFC4fR5hw4dEBoaiqpVq6qczKSkpEAikRQ6mGbBaUtVfxQbNmwIMzMzbNmyBT169JCVP3r0CCdOnED37t0BvNyhKlSogG3btmHcuHGyDz8tLQ27d++W9eAEVDuroyxPT084Oztj586dGDNmjKw8OjoaZ86cKVFSW5jSfD4AUKNGDVStWhXr16/HmDFjCu3ebG5ujmbNmiEyMhJ16tSR/TdfWqampoVuc4lEAiMjI7mDQUZGBn755Re5egWXEkNCQuTG89u1a5dCD8zSbqfXDRgwAH///Tdq1qwplxi+SUBAAGxsbLBq1Sr07t1b6TNuRVF2WwUFBcHIyAh3795VuJSoLsrum8Vp1qwZFixYgK1bt2LUqFGy8m3btinUlUgkCt/XgwcP4vHjx6hWrVopWvIfMzMzBAYG4tdff8WcOXOKTLJatGiBvXv34smTJ3L79ubNm2Fubv7GIY5Udfz4cTx9+lR29iAvLw8hISGoWrWq7CxfYdvn8uXLOHv2rMLl18J4enqievXq+OeffxQS4Nc1a9YM+/fvLzQmZbx6DDYzM5OVt2jRAgsWLMCWLVvw1Vdfycp3796NtLQ0uSFtSkKVNiq7PQMDA7Fz50788ccfsst8ABR60wIvf4d//fVXZGVlyZYdHx+PM2fOyJ1V7tChA3bs2IG8vDz4+/ur1MYXL15g7969aNSoEWbPnq0w/eeff8bWrVvx77//olatWqhZsyb8/f2xYcMG5OXlISsrCwMHDpSbRx3HU4lEAhMTE7ljYGxsrEJvzcDAQCxYsAAhISEYNmyYrPz17anu3ymtJWf9+vXD1KlTMW3aNAQGBuLatWv46aefZJfLCsyaNQtHjx5FQEAARo0aBU9PT2RmZuL+/fsIDQ3FqlWrijzlf/PmTQQFBaF3794IDAyEi4sLXrx4gYMHD2LNmjVo2rQpAgICVIrb1tYWU6dOxTfffIP+/fujT58+iI+Px8yZMyGVSjF9+nQAL4fpWLBgAT7++GN06NABQ4YMQVZWFhYuXIjExETMmzdPtszatWsDAL7//nsMGDAAxsbG8PT0fON/I8UxMDDAzJkzMWTIEHTv3h2DBg1CYmIiZs6cCRcXl0KHESmJ0nw+BZYvX46OHTuiQYMG+Oqrr1CpUiVER0fj8OHD2Lp1K4CX2+bDDz9E48aNMWzYMFSuXBkpKSm4c+cOfv/990IHZn2T2rVrY8+ePVi5ciV8fX1hYGAAPz8/tG/fHkuWLEHfvn3x+eefIz4+HosWLVI4ML733nvo06cPFi9eDENDQzRv3hxXr17F4sWLYWNjI7eN1bGdXuXq6qpwdkoZlpaWWLx4MT799FO0bNkSn332GZycnHDnzh38888/+Omnn1RanrLbqnLlypg1axYmT56Me/fuoU2bNihXrhyePn2Kc+fOwcLCosSDgBYcXJXdN4vTunVrNGnSBF9//TXS0tLg5+eH06dPKySbwMsfiI0bN8LLywt16tTBhQsXsHDhQpU+R2UsWbIEH374Ifz9/TFx4kRUq1YNT58+xf79+7F69WpYWVlh+vTpsvtwpk2bBjs7O2zduhUHDx7EggULFI6rpeXg4IDmzZtj6tSpsLCwwIoVK3Djxg25H6wOHTrg22+/xfTp0xEYGIibN29i1qxZ8PDwUHr4mNWrV6Nt27YICgpCcHAwKlSogISEBFy/fh0XL17Er7/+CgCYMmUK9u/fj+bNm2PatGkwNzfH8uXLkZaWptR6Co7B8+fPR9u2bWFoaIg6deqgVatWCAoKwoQJE5CcnIxGjRrh8uXLmD59OurXr49+/foptfzff/+90ON59+7dlW6jsttzwIABWLp0KT755BPMnj0b1apVwx9//CEbdP3V41K/fv2wevVqfPLJJ/jss88QHx+PBQsWKFzu7927N7Zu3Yp27drhyy+/xAcffABjY2M8evQIYWFh6Ny5M7p27Vpo27du3YrMzEyMGjWq0GE77O3tsXXrVqxbtw5Lly4F8PK+2iFDhuDJkycICAiQnT0soI7jaYcOHbBnzx4MHz4c3bt3x8OHD/Htt9/CxcUFt2/fltVr06YNGjVqhLFjxyI5ORm+vr44e/YsNm/erLA91fo7pXTXgTcoqrdmYUMxCCFEVlaW+Prrr4Wbm5swMzMTgYGB4tKlSwq9NYUQ4tmzZ2LUqFHCw8NDGBsbCzs7O+Hr6ysmT54sUlNTi4zpxYsXYvbs2aJ58+aiQoUKwsTERFhYWIh69eqJ2bNny3X/LejF9GpXcCH+6+3xag8XIV4OR1CnTh1hYmIibGxsROfOnQvtcbdv3z7h7+8vpFKpsLCwEC1atBCnT59WqDdp0iTh6uoqDAwMBABZ1+iiemsuXLhQYRkopDfZmjVrRLVq1YSJiYmoUaOGWL9+vejcubNcb5SiFPf5vUqZz6e4uIUQ4uzZs6Jt27bCxsZGmJqaiqpVq8r1eCtYxqBBg0SFChWEsbGxKF++vAgICBCzZ8+W1VHlc0xISBDdu3cXtra2QiKRyPXWWr9+vfD09BSmpqaiSpUqYu7cuWLdunUKPb8yMzPFmDFjhKOjo5BKpaJBgwbi7NmzwsbGRiH+kn6PhVDus1B2KA0hhAgNDRWBgYHCwsJCNgTBq0MuFLY/C1F4T0plt5UQL/eHZs2aCWtra2Fqairc3d1F9+7dxbFjx4pdR2HGjx8vAIiUlBS5cmX2zaLaJ4QQiYmJYtCgQcLW1laYm5uLVq1aiRs3bijsXy9evBCDBw8Wjo6OwtzcXHz44Yfi1KlTCvusqseWwly7dk306NFD2NvbCxMTE1GpUiURHBwsN5TDlStXRMeOHYWNjY0wMTERdevWVVh2UbEUfE8iIiLkygvrbQhAjBgxQqxYsUJUrVpVGBsbCy8vL7F161a5ebOyssS4ceNEhQoVhFQqFT4+PmLfvn0KPQHfdGz4559/RM+ePYWjo6MwNjYWzs7Oonnz5mLVqlVy9U6fPi0aNGggTE1NhbOzsxg/frxYs2aNUr01s7KyxKeffirKly8vOxYUzJORkSEmTJgg3N3dhbGxsXBxcRHDhg0TL168KHaZr26/ol6qtFHZ7SnEyx7KH330kbC0tBRWVlaiW7duIjQ0VAAQv/32m1zdTZs2iZo1awqpVCq8vb1FSEhIocvMyckRixYtEnXr1hVSqVRYWloKLy8vMWTIEHH79u0it0G9evWEo6OjyMrKKrJOgwYNhIODg6xOUlKSMDMzEwDE2rVrC51HHb878+bNE5UrVxampqaiZs2aYu3atYUefxISEsTAgQPljgl//fVXoT27lfmdUoZEiEJGiqQyKzExETVq1ECXLl2wZs0abYdTJp05cwaNGjXC1q1bC+3lR+rRuXNnnD17VtYjlN4OiUSCESNGqHyWlbTru+++w5QpUxAdHa32s7v6aNu2bfj4449x+vRpla/AKUNrlzVJ82JjYzFnzhw0a9YM9vb2ePDgAZYuXYqUlBR8+eWX2g6vTDh69CjOnj0LX19fmJmZ4Z9//sG8efNQvXr1Im/wpdL5888/cebMGYSGhhY6HA6RvitInL28vJCTk4MTJ07ghx9+wCeffMLErAS2b9+Ox48fo3bt2jAwMMBff/2FhQsXokmTJhpJzAAmZ2Waqakp7t+/j+HDhyMhIUF2Y/CqVatU6qFKRbO2tsaRI0ewbNkypKSkwMHBAW3btsXcuXOLHLeNSqdJkyYoX748Pv/88zfeRE2kj8zNzbF06VLcv38fWVlZqFSpEiZMmIApU6ZoO7R3kpWVFXbs2IHZs2cjLS0NLi4uCA4OLrSDg7rwsiYRERGRDtH4ILREREREpDwmZ0REREQ6hMkZERERkQ7Ruw4B+fn5ePLkCaysrEo9OjoRERG9HUIIpKSkwNXVVW0DqesqvUvOnjx5otSjQ4iIiEj3PHz4sMwPCaJ3yVnBIzQePnyo8IgKIiIi0k3Jyclwc3Mr1aMN3xV6l5wVXMq0trZmckZERPSO0Ydbksr2RVsiIiKidwyTMyIiIiIdwuSMiIiISIfo3T1nRERUvLy8POTk5Gg7DNJDJiYmZX6YDGUwOSMiIgAvx5GKjY1FYmKitkMhPWVgYAAPDw+YmJhoOxStYnJGREQAIEvMHB0dYW5urhe94kh3FAwSHxMTg0qVKun194/JGRERIS8vT5aY2dvbazsc0lPly5fHkydPkJubC2NjY22HozW8sEtERLJ7zMzNzbUcCemzgsuZeXl5Wo5Eu5icERGRjD5fSiLt4/fvJSZnRERERDqEyRkRERGRDmFyRkRE77Tg4GB06dJF22Fo1KNHj2BiYgIvLy9th0JvAZMzIiIiHbdx40b07NkT6enpOH36tEbXJYRAbm6uRtdBxWNyRkREZdq1a9fQrl07WFpawsnJCf369cPz589l0w8dOoQPP/wQtra2sLe3R4cOHXD37l25ZZw5cwb16tWDVCqFn58f9u3bB4lEgkuXLgF4mTzZ2trKzVNQ51W///47fH19IZVKUaVKFcycOfONiZAQAhs2bEC/fv3Qt29frFu3TjatT58+6N27t1z9nJwcODg4YMOGDbL5FyxYgCpVqsDMzAx169bFrl27ZPXDw8MhkUhw+PBh+Pn5wdTUFKdOncLdu3fRuXNnODk5wdLSEu+//z6OHTsmt66YmBi0b98eZmZm8PDwwLZt21C5cmUsW7ZMVicpKQmff/45HB0dYW1tjebNm+Off/4pts36jskZEREVKy2t6FdmpvJ1MzKUq6tOMTExCAwMRL169XD+/HkcOnQIT58+Rc+ePV+JIw1jxoxBREQEjh8/DgMDA3Tt2hX5+fkAgJSUFHTs2BG1a9fGxYsX8e2332LChAkqx3L48GF88sknGDVqFK5du4bVq1dj48aNmDNnTrHzhYWFIT09HS1btkS/fv2wc+dOpKSkAAA+/vhj7N+/H6mpqXLrSUtLQ7du3QAAU6ZMwYYNG7By5UpcvXoVX331FT755BOcPHlSbj1ff/015s6di+vXr6NOnTpITU1Fu3btcOzYMURGRiIoKAgdO3ZEdHS0bJ7+/fvjyZMnCA8Px+7du7FmzRrExcXJpgsh0L59e8TGxiI0NBQXLlyAj48PWrRogYSEBJW3od4QeiYpKUkAEElJSZpZAcAXX3wV9SKdlZGRIa5duyYyMjIUphX3kbZrJ1/X3LzouoGB8nUdHNTzNRkwYIDo3LlzodOmTp0qWrduLVf28OFDAUDcvHmz0Hni4uIEAHHlyhUhhBArV64U9vb2cttm7dq1AoCIjIwUQgixYcMGYWNjI7ecvXv3ild/Zhs3biy+++47uTq//PKLcHFxKbZ9ffv2FaNHj5a9r1u3rli7dq0QQojs7Gzh4OAgNm/eLJvep08f0aNHDyGEEKmpqUIqlYozZ87ILXPw4MGiT58+QgghwsLCBACxb9++YuMQQghvb2/x448/CiGEuH79ugAgIiIiZNNv374tAIilS5cKIYQ4fvy4sLa2FpmZmXLLqVq1qli9erXC8ov7Hmr891uH8AkBRERUZl24cAFhYWGwtLRUmHb37l3UqFEDd+/exdSpU/HXX3/h+fPnsjNm0dHRqFWrFm7evIk6depAKpXK5v3ggw9KFEtERITcmbK8vDxkZmYiPT290AGAExMTsWfPHvz555+ysk8++QTr16/Hp59+CmNjY/To0QNbt25Fv379kJaWht9++w3btm0D8PKSbmZmJlq1aiW33OzsbNSvX1+uzM/PT+59WloaZs6ciQMHDshG7c/IyJCdObt58yaMjIzg4+Mjm6datWooV66cXJtTU1MVnjqRkZGhcOmY/sPkjIiIivXKFTMFhoby71+5oqXA4LUbae7fL3FISsvPz0fHjh0xf/58hWkuLi4AgI4dO8LNzQ1r166Fq6sr8vPzUatWLWRnZwMAhBAK944JIeTeGxgYKJQVPHXh1VhmzpyJjz76SCGWVxO/V23btg2ZmZnw9/eXW3d+fj6uXbsGb29vfPzxxwgMDERcXByOHj0KqVSKtm3bytYJAAcPHkSFChXklm1qair33sLCQu79+PHjcfjwYSxatAjVqlWDmZkZunfvLrddCvNqeX5+PlxcXBAeHq5Q7/V79Og/TM6IiKhYr/1ma6VuSfn4+GD37t2oXLkyjIwUf/Li4+Nx/fp1rF69Go0bNwYAubNUAODl5YWtW7ciKytLltCcP39erk758uWRkpKCtLQ0WZJT0Fng1Vhu3ryJatWqKR3/unXrMHbsWAQHB8uVjxo1CuvXr8eiRYsQEBAANzc3hISE4I8//kCPHj1kj0Hy9vaGqakpoqOjERgYqPR6AeDUqVMIDg5G165dAQCpqam4/0pG7eXlhdzcXERGRsLX1xcAcOfOHSQmJsq1OTY2FkZGRqhcubJK69dnTM6IiOidl5SUpJAM2dnZYcSIEVi7di369OmD8ePHw8HBAXfu3MGOHTuwdu1alCtXDvb29lizZg1cXFwQHR2NiRMnyi2nb9++mDx5Mj7//HNMnDgR0dHRWLRoEYD/Hjfk7+8Pc3NzfPPNN/jiiy9w7tw5bNy4UW4506ZNQ4cOHeDm5oYePXrAwMAAly9fxpUrVzB79myFNl26dAkXL17E1q1bFcY369OnDyZPnoy5c+fC2NgYffv2xapVq3Dr1i2EhYXJ6llZWWHcuHH46quvkJ+fjw8//BDJyck4c+YMLC0tMWDAgCK3abVq1bBnzx507NgREokEU6dOlZ2JA14mZy1btsTnn3+OlStXwtjYGGPHjoWZmZlsu7Rs2RINGzZEly5dMH/+fHh6euLJkycIDQ1Fly5dFC6l0v/T5g1v2sAOAXzxpcUX6azibsTWdQMGDBAAFF4DBgwQQghx69Yt0bVrV2FrayvMzMyEl5eXGD16tMjPzxdCCHH06FFRs2ZNYWpqKurUqSPCw8MFALF3717ZOk6fPi3q1KkjTExMhK+vr9i2bZsAIG7cuCGrs3fvXlGtWjUhlUpFhw4dxJo1a8TrP7OHDh0SAQEBwszMTFhbW4sPPvhArFmzptB2jRw5Unh7exc6LS4uThgaGordu3cLIYS4evWqACDc3d1l7SqQn58vvv/+e+Hp6SmMjY1F+fLlRVBQkDh58qQQ4r8OAS9evJCbLyoqSjRr1kyYmZkJNzc38dNPP4nAwEDx5Zdfyuo8efJEtG3bVpiamgp3d3exbds24ejoKFatWiWrk5ycLL744gvh6uoqjI2NhZubm/j4449FdHS0QrvYIeAliRBCaCct1I7k5GTY2NggKSkJ1tbW6l8BH9pKVDT9Oty8UzIzMxEVFQUPD48i73+i/2zduhUDBw5EUlISzMzMtB2Oznj06BHc3Nxw7NgxtGjRQuX5i/seavz3W4fwsiYREdEbbN68GVWqVEGFChXwzz//YMKECejZs6feJ2YnTpxAamoqateujZiYGHz99deoXLkymjRpou3Q3mlMzoiIiN4gNjYW06ZNQ2xsLFxcXNCjR483Dh6rD3JycvDNN9/g3r17sLKyQkBAALZu3QpjY2Nth/ZO42VNdeNlTaKi6dfh5p3Cy5qkC3hZ8yU+vomIiIhIhzA5IyIiItIhTM6IiIiIdAiTMyIiIiIdwuSMiIiISIcwOSMiIiLSIUzOiIhIrzRt2hSjR49Wqm54eDgkEoncw7w1YePGjbC1tdXoOopy//59SCQShWeTkvYwOSMiouJJJG/3paLg4GBIJBIMHTpUYdrw4cMhkUgQHBwsK9uzZw++/fbb0mwRrZBIJNi3b5+2w6C3gMkZERG989zc3LBjxw5kZGTIyjIzM7F9+3ZUqlRJrq6dnR2srKzedohal52dre0QSElMzoiI6J3n4+ODSpUqYc+ePbKyPXv2wM3NDfXr15er+/plzaysLHz99ddwc3ODqakpqlevjnXr1snNc+HCBfj5+cHc3BwBAQG4efOmbNrdu3fRuXNnODk5wdLSEu+//z6OHTsmN392dja+/vprVKhQARYWFvD390d4eHip2nzlyhU0b94cZmZmsLe3x+eff47U1FTZ9ODgYHTp0gVz586Fq6sratSoAQA4d+4c6tevD6lUCj8/P0RGRsotNy8vD4MHD4aHhwfMzMzg6emJ77//Xq5OwbIXLVoEFxcX2NvbY8SIEcjJySlVm+glJmdERFQmDBw4EBs2bJC9X79+PQYNGvTG+fr3748dO3bghx9+wPXr17Fq1SpYWlrK1Zk8eTIWL16M8+fPw8jISG65qampaNeuHY4dO4bIyEgEBQWhY8eOiI6Olovt9OnT2LFjBy5fvowePXqgTZs2uH37donamp6ejjZt2qBcuXKIiIjAr7/+imPHjmHkyJFy9Y4fP47r16/j6NGjOHDgANLS0tChQwd4enriwoULmDFjBsaNGyc3T35+PipWrIidO3fi2rVrmDZtGr755hvs3LlTrl5YWBju3r2LsLAwbNq0CRs3bsTGjRtL1B6SxwefExGpqiw+Q9fdHVi1CkhL03YkwPnzqtV//hxITUW/2rUxaeJE3N+/HxKJBKf//BM7Jk5E+L59QG7uf8tNSQGePgXOn8etBw+wc+dOHP3pJ7R0cwMSElDFxgawsXlZ///PkM3p3x+BFhZAejomduuG9qNHI/P0aUhNTVEXQF1fXyArC8jKwuwuXbB32zbs/+knjOzZE3cfPcL27dvx6OBBuJqZAS9eYFzTpjhUpw42fPcdvhsxAoiKAvLy3tz2O3eA8+exde9eZKSmYvPo0bDIzASsrfHTl1+i45gxmN+nD5zs7YHnz2FhYoKfhw2Dyf9f7l0zZw7ysrOxfuRImGdk4D1nZzzq2RPD5s0Drl4FcnNhDGBmx44v1xcfDw9PT5xp1w47V69GzypVZNu8nIUFfgoOhmFqKrycndE+IADHf/0Vn9WvD/j5qfYZkhwmZ0REVCY42NqifaNG2HTwIIQQaN+oERze0APy0q1bMDQ0RKCvb7H16lSvLvvbxcEBABD34gUqOTsjLSMDM9euxYE//8STZ8+Qm5eHjKwsRMfGAgAu3rgBIQRqdOsmt8ys7GzY29iUoKXA9fv3Ubd6dViYmcnKGtWti/z8fNx88OBlcgagdrVqMDE2VpjP/JWHijesXVth+at278bPv/2GBzExyMjKQnZODur9/2XRAu9VqQJDQ8P/tou9Pa7cvVui9pA8JmdERFRmDOrUCSMXLgQALP/66zfWNzM1VWq5xkb//VwWnDfNz88HAIz//nsc/usvLPryS1Rzc4OZqSm6T5iA7P+//yo/Px+Ghoa4sHmzXDIDAJavJFeqEEJAUsQZ3FfLLV5bvhDijcveefQovlq6FIu//BIN69SBlbk5Fv7yC/7+91+5eq9uk4L1FmwTKh0mZ0REVGa0adhQlhQFNWjwxvq1q1VDfn4+Tl64gJb+/iVa56lLlxDcoQO6NmsGAEhNT8f9mBjZ9PqensjLy0Pcixdo/FrnhJLy9vDApoMHkZaRIUvATv/zDwwMDFDjtd6pr8/3yx9/ICMzE2b/f/bsr9eSrlOXLiGgdm0M79FDVnb30SO1xE3KYYcAIiIqMwwNDXF9505c37lT4SxVYSq7umJA+/YY9O232BcejqjHjxF+4QJ2Hj2q9DqrublhT1gYLt28iX9u3ULfKVOQ/8oZqhru7vi4TRv0nzEDe06cQNTjx4i4ehXzN21C6OnTJWrnx23bQmpiggEzZuDfO3cQdv48vli4EP3atpVd0ixM3zZtYCCRYPDs2bh27x5CT5/Goi1b5NtTsSLOX7+Ow2fP4taDB5i6ciUirl0rUZxUMkzOiIioTLG2tIT1a70ti7Ny4kR0b9ECw+fPh1ePHvhszhykvTJe2pss/eorlLO2RsDgweg4ZgyCGjSAj6enXJ0N06ejf7t2GPv99/Ds3h2dxo7F3//+CzcnJ6XWUXC50Oj/E05zqRSHf/wRCcnJeD84GN0nTkSL99/HT2+4lGtpbo7flyzBtXv3UP+TTzB5xQrMf62H59Bu3fBRs2bo9c038B84EPFJSRjevbuym4PUQCKUuQBdhiQnJ8PGxgZJSUmwtrZW/wrKYi8uInUpK4ebMrifZ7q7I2rVKng4OED65ur0lsU+fw6Xtm0RsWkT/Ly9tR3Om5Wwt2ZmZiaioqLg4eEBqVT+m6jx328dwnvOiIiIdJQQAg9iYrBoyxY42dmhVtWq2g6J3gImZ0RERDoqKTUVnt27o2blytjx3XeQKtm7lN5tTM6IiIh0lK2VFbLOnNF2GPSWsUMAERERkQ5hckZERC87awiBMtJlg95RetZHsUhMzoiICMbx8UB2NtK1HQjptezsbABQaoy6soz3nBEREQzT0mC7fz/i+vQBbG1hjv8eU0SkssxMlWfJz8/Hs2fPYG5uDiMj/U5P9Lv1REQk47xhAwAgrlMnwMSkTI7nRm9JVFSJZjMwMEClSpWKfG6ovuAgtOqm518oomKVlcNNGd/P88zNkePgUObbSRp040aJZjMxMYGBQeF3XHEQ2rdoxYoVWLhwIWJiYvDee+9h2bJlaNy4caF1w8PD0ez/Hyz7quvXr8PLy0vToRIR6QXD9HQYRkdrOwx6l0n5nInS0GqHgJCQEIwePRqTJ09GZGQkGjdujLZt2yL6DQeFmzdvIiYmRvaqXr36W4qYiIiISLO0mpwtWbIEgwcPxqeffoqaNWti2bJlcHNzw8qVK4udz9HREc7OzrKXvvfqICIiorJDa8lZdnY2Lly4gNatW8uVt27dGmfeMBpy/fr14eLighYtWiAsLKzYullZWUhOTpZ7EREREekqrd1z9vz5c+Tl5cHJyUmu3MnJCbGxsYXO4+LigjVr1sDX1xdZWVn45Zdf0KJFC4SHh6NJkyaFzjN37lzMnDlToTwtDSjshJuhofyl8rS0ottgYACYmb1e17zwusiHGf7rWpwOM4giOqpLIGCOjBLVzYAU+cXk3BavjGKkSt1MmCIPRZ+hVKWuOdJlrcmCCXKL+RqqUtcMGTD4/yE0s2GMHBirpa4UmTBEvsp1c2CEbJgUWdcUWTBCnsp1c2GILBT9fD0TZMMYuSrXzYMBMlH0fSLGyIEJclSumw8JMvD/O0oh+5Ox8cuOgQCQnw9kZCjWKWBkBBQ8WlAIIL2YQblUqavKfm9oCLmWpxWxzwOK+70qdXmMeInHiHf0GJFX/Ggaquz3r9bVG0JLHj9+LACIM2fOyJXPnj1beHp6Kr2cDh06iI4dOxY5PTMzUyQlJcleDx8+FAAEkCRkQ2K/8mrXTn5+c3PFOgWvwED5ug4ORdf1wzm5AndEFVnXG//KFXjj3yLruiNKrsAP54qs64A4uYJAhBVZ1xypcgXtcKDIuv8/prPs1R07i62biv826gBsKLZuHP7bqMPxU7F1o+AuezMOC4qt+y+8ZW+mY3qxdc/BT/ZmAcYVWzcMgbI3P2F4sXUPoJ3szQYMKLbuTnSXvdmJ7sXW3YABsjcH0K7Yuj9huOxNGAKLrbsA42RvzsGv2LrTMV325l94F1t33Lj/9qGoqKLrAUIMH/5f3bi44usOGPBf3dTU4ut27y6/LxdXt107+QrmSC2ybiDC5AocEFdkXR4j/nvxGPHy9U4fI84VXQ8QYvr0//a3f/8tvm7BMSIpKUkAEElJSUX+5pcVWrus6eDgAENDQ4WzZHFxcQpn04rToEED3L59u8jppqamsLa2lnsRERER6SqtjnPm7+8PX19frFixQlbm7e2Nzp07Y+7cuUoto3v37khISMCJEyeUql8wTsqTJ4WPk1Lqy5qWFoXX5SULGV6yeOmdvmRR0suaqYo71Dt5WdPsv/2RlzVf4jGCxwi5Y0SuUPtlTX0a50yryVlISAj69euHVatWoWHDhlizZg3Wrl2Lq1evwt3dHZMmTcLjx4+xefNmAMCyZctQuXJlvPfee8jOzsaWLVswb9487N69Gx999JFS6+QgtERapL3DjXpxPycqngb2dX1KzrQ6CG2vXr0QHx+PWbNmISYmBrVq1UJoaCjc3d0BADExMXJjnmVnZ2PcuHF4/PgxzMzM8N577+HgwYNo166dtppAREREpFZ8fJO68T9qoqKVlcMN93Oi4vHMWalodRBaIiIiIpLH5IyIiIhIhzA5IyIiItIhTM6IiIiIdAiTMyIiIiIdwuSMiIiISIcwOSMiIiLSIUzOiIiIiHQIkzMiIiIiHaLS45uSkpKwd+9enDp1Cvfv30d6ejrKly+P+vXrIygoCAEBAZqKk4iIiEgvKHXmLCYmBp999hlcXFwwa9YspKWloV69emjRogUqVqyIsLAwtGrVCt7e3ggJCdF0zERERERlllJnzurWrYv+/fvj3LlzqFWrVqF1MjIysG/fPixZsgQPHz7EuHHj1BooERERkT5Q6sHnz549Q/ny5ZVeqKr13yY++JxIi/jgcyL9wAefl4pSlzVVTbR0NTEjIiIi0nUl7q2ZkpKC8ePH4/3334ePjw+++OILPH/+XJ2xEREREekdpS5rFqZ3794wMzNDjx49kJOTgzVr1iA3NxeHDx9Wd4xqxcuaRFrEy5pE+oGXNUtF6aE0li5ditGjR0Py/weliIgI3Lp1C4aGhgAAT09PNGjQQDNREhEREekJpZOzO3fuwN/fH6tXr0b9+vXRqlUrtG/fHl26dEFOTg5++eUXBAUFaTJWIiIiojJP6eRs+fLlOHv2LAYNGoRmzZph7ty52LJlC44ePYq8vDz06NEDI0eO1GSsRERERGWeSk8IaNiwISIiIjBv3jw0bNgQCxcuxO7duzUVGxEREZHeKXGHgNu3b2Po0KEoV64cfvrpJzg7O6s7No1ghwAiLWKHACL9wA4BpaL0UBpXrlzBBx98ACsrKzRq1Aj5+fk4fvw42rVrh4CAAKxcuVKTcRIRERHpBaWTs4EDB+LDDz9EREQEevTogaFDhwIABg0ahL///ht//vknGjZsqLFAiYiIiPSB0pc1raysEBkZiWrVqiEvLw9Vq1bF/fv35eocOXIErVu31kScasPLmkRaxMuaRPqBlzVLRekOAU2bNsXnn3+O3r1748SJE2jUqJFCHV1PzIiIiIh0ndKXNTdv3gwfHx/89ttvqFKlCu8xIyIiItKAEvfWfFfxsiaRFpWVww33c6Li8bJmqSh95iwhIQGPHj2SK7t69SoGDhyInj17Ytu2bWoPjoiIiEjfKJ2cjRgxAkuWLJG9j4uLQ+PGjREREYGsrCwEBwfjl19+0UiQRERERPpC6eTsr7/+QqdOnWTvN2/eDDs7O1y6dAm//fYbvvvuOyxfvlwjQRIRERHpC6WTs9jYWHh4eMjenzhxAl27doWR0csOn506dcLt27fVHyERERGRHlE6ObO2tkZiYqLs/blz59CgQQPZe4lEgqysLLUGR0RERKRvlE7OPvjgA/zwww/Iz8/Hrl27kJKSgubNm8um37p1C25ubhoJkoiIiEhfKD0I7axZs9CqVSts2bIFubm5+Oabb1CuXDnZ9B07diAwMFAjQRIRERHpC6WTs/r16+P69es4c+YMnJ2d4e/vLze9d+/e8Pb2VnuARERERPpE6UFomzdvjj179sDW1lbDIWkWB6El0iIOQkukHzgIbakofc9ZeHg4srOzNRkLERERkd5TOjkjIiIiIs1T+p4zAEhJSYFUKi22Tlk/1UhERESkSSolZzVq1ChymhACEokEeXl5pQ6KiIiISF+plJzt2rULdnZ2moqFiIiISO+plJw1atQIjo6OmoqFiIiISO+xQwARERGRDlE6OXN3d4ehoaEmYyEiIiLSe0pf1oyKitJkHEREREQEJc+ctWnTBmfOnHljvZSUFMyfPx/Lly8vdWBERERE+kipM2c9evRAz549YWVlhU6dOsHPzw+urq6QSqV48eIFrl27hj///BOhoaHo0KEDFi5cqOm4iYiIiMokpZ+tmZ2djV27diEkJASnTp1CYmLiywVIJPD29kZQUBA+++wzeHp6ajLeUuOzNYm0iM/WJNIPfLZmqSidnL0uKSkJGRkZsLe3h7Gxsbrj0hgmZ0RaxOSMSD8wOSsVlcY5e5WNjQ1sbGzUGQsRERGR3uM4Z0REREQ6ROvJ2YoVK+Dh4QGpVApfX1+cOnVKqflOnz4NIyMj1KtXT7MBEhEREb1FWk3OQkJCMHr0aEyePBmRkZFo3Lgx2rZti+jo6GLnS0pKQv/+/dGiRYu3FCkRERHR21HiDgHq4O/vDx8fH6xcuVJWVrNmTXTp0gVz584tcr7evXujevXqMDQ0xL59+3Dp0iWl18kOAURaxA4BRPqBHQJKpURnzhITE/Hzzz9j0qRJSEhIAABcvHgRjx8/VnoZ2dnZuHDhAlq3bi1X3rp162IHvN2wYQPu3r2L6dOnK7WerKwsJCcny72IiIiIdJXKvTUvX76Mli1bwsbGBvfv38dnn30GOzs77N27Fw8ePMDmzZuVWs7z58+Rl5cHJycnuXInJyfExsYWOs/t27cxceJEnDp1CkZGyoU+d+5czJw5U6m6RERERNqm8pmzMWPGIDg4GLdv34ZUKpWVt23bFv/73/9UDkDy2uUBIYRCGQDk5eWhb9++mDlzJmrUqKH08idNmoSkpCTZ6+HDhyrHSERERPS2qHzmLCIiAqtXr1Yor1ChQpFnvArj4OAAQ0NDhXni4uIUzqYBL5/bef78eURGRmLkyJEAgPz8fAghYGRkhCNHjqB58+YK85mamsLU1FTpuIiIiIi0SeUzZ1KptND7tm7evIny5csrvRwTExP4+vri6NGjcuVHjx5FQECAQn1ra2tcuXIFly5dkr2GDh0KT09PXLp0Cf7+/qo2hYiIiEjnqHzmrHPnzpg1axZ27twJ4OVlyejoaEycOBHdunVTaVljxoxBv3794Ofnh4YNG2LNmjWIjo7G0KFDAby8JPn48WNs3rwZBgYGqFWrltz8jo6OkEqlCuVERERE7yqVk7NFixahXbt2cHR0REZGBgIDAxEbG4uGDRtizpw5Ki2rV69eiI+Px6xZsxATE4NatWohNDQU7u7uAICYmJg3jnlGREREVJaUeJyzEydO4OLFi8jPz4ePjw9atmyp7tg0guOcEWkRxzkj0g8c56xUtDoIrTYwOSPSorJyuOF+TlQ8JmelovJlzR9++KHQcolEAqlUimrVqqFJkyYwNDQsdXBERERE+kbl5Gzp0qV49uwZ0tPTUa5cOQghkJiYCHNzc1haWiIuLg5VqlRBWFgY3NzcNBEzERERUZml8lAa3333Hd5//33cvn0b8fHxSEhIwK1bt+Dv74/vv/8e0dHRcHZ2xldffaWJeImIiIjKNJXvOatatSp2796NevXqyZVHRkaiW7duuHfvHs6cOYNu3bohJiZGnbGqBe85I9Ii3nNGpB94z1mpqHzmLCYmBrm5uQrlubm5stH+XV1dkZKSUvroiIiIiPSMyslZs2bNMGTIEERGRsrKIiMjMWzYMNnjk65cuQIPDw/1RUlERESkJ1ROztatWwc7Ozv4+vrKnlvp5+cHOzs7rFu3DgBgaWmJxYsXqz1YIiIiorKuxOOc3bhxA7du3YIQAl5eXvD09FR3bBrBe86ItIj3nBHpB95zVioqD6VRwMvLC15eXuqMhYiIiEjvlSg5e/ToEfbv34/o6GhkZ2fLTVuyZIlaAiMiIiLSRyonZ8ePH0enTp3g4eGBmzdvolatWrh//z6EEPDx8dFEjERERER6Q+UOAZMmTcLYsWPx77//QiqVYvfu3Xj48CECAwPRo0cPTcRIREREpDdUTs6uX7+OAQMGAACMjIyQkZEBS0tLzJo1C/Pnz1d7gERERET6ROXkzMLCAllZWQBeDjZ79+5d2bTnz5+rLzIiIiIiPaTyPWcNGjTA6dOn4e3tjfbt22Ps2LG4cuUK9uzZgwYNGmgiRiIiIiK9oXJytmTJEqSmpgIAZsyYgdTUVISEhKBatWpYunSp2gMkIiIi0iclHoT2XcVBaIm0qKwcbrifExWPg9CWisr3nFWpUgXx8fEK5YmJiahSpYpagiIiIiLSVyonZ/fv30deXp5CeVZWFh4/fqyWoIiIiIj0ldL3nO3fv1/29+HDh2FjYyN7n5eXh+PHj6Ny5cpqDY6IiIhI3yidnHXp0gUAIJFIZOOcFTA2NkblypWxePFitQZHREREpG+UTs7y8/MBAB4eHoiIiICDg4PGgiIiIiLSVyoPpREVFaWJOIiIiIgIJUjOgJcPPz9+/Dji4uJkZ9QKrF+/Xi2BEREREekjlZOzmTNnYtasWfDz84OLiwskHO+HiIiISG1UTs5WrVqFjRs3ol+/fpqIh4iIiEivqTzOWXZ2NgICAjQRCxEREZHeUzk5+/TTT7Ft2zZNxEJERESk91S+rJmZmYk1a9bg2LFjqFOnDoyNjeWmL1myRG3BEREREekblZOzy5cvo169egCAf//9V24aOwcQERERlY7KyVlYWJgm4iAiIiIilOCeswJ37tzB4cOHkZGRAQAQQqgtKCIiIiJ9pXJyFh8fjxYtWqBGjRpo164dYmJiALzsKDB27Fi1B0hERESkT1ROzr766isYGxsjOjoa5ubmsvJevXrh0KFDag2OiIiISN+ofM/ZkSNHcPjwYVSsWFGuvHr16njw4IHaAiMiIiLSRyqfOUtLS5M7Y1bg+fPnMDU1VUtQRERERPpK5eSsSZMm2Lx5s+y9RCJBfn4+Fi5ciGbNmqk1OCIiIiJ9o/JlzYULF6Jp06Y4f/48srOz8fXXX+Pq1atISEjA6dOnNREjERERkd5Q+cyZt7c3Ll++jA8++ACtWrVCWloaPvroI0RGRqJq1aqaiJGIiIhIb0iEng1QlpycDBsbGyQlJcHa2lr9K+BTEoiKVlYON9zPiYqngX1d47/fOkTlM2cbNmzAr7/+qlD+66+/YtOmTWoJioiIiEhfqZyczZs3Dw4ODgrljo6O+O6779QSFBEREZG+Ujk5e/DgATw8PBTK3d3dER0drZagiIiIiPSVysmZo6MjLl++rFD+zz//wN7eXi1BEREREekrlZOz3r17Y9SoUQgLC0NeXh7y8vJw4sQJfPnll+jdu7cmYiQiIiLSGyqPczZ79mw8ePAALVq0gJHRy9nz8/PRv39/3nNGREREVEoqDaUhhEB0dDTKly+Px48f49KlSzAzM0Pt2rXh7u6uyTjVhkNpEGkRh9Ig0g8cSqNUVDpzJoRA9erVcfXqVVSvXh3Vq1fXVFxEREREekmle84MDAxQvXp1xMfHayoeIiIiIr2mcoeABQsWYPz48fj33381EQ8RERGRXlM5Ofvkk09w7tw51K1bF2ZmZrCzs5N7qWrFihXw8PCAVCqFr68vTp06VWTdP//8E40aNYK9vT3MzMzg5eWFpUuXqrxOIiIiIl2lcm/NZcuWqW3lISEhGD16NFasWIFGjRph9erVaNu2La5du4ZKlSop1LewsMDIkSNRp04dWFhY4M8//8SQIUNgYWGBzz//XG1xEREREWmLVh987u/vDx8fH6xcuVJWVrNmTXTp0gVz585VahkfffQRLCws8MsvvyhVn701ibSIvTWJ9AN7a5aKypc1AeDu3buYMmUK+vTpg7i4OADAoUOHcPXqVaWXkZ2djQsXLqB169Zy5a1bt8aZM2eUWkZkZCTOnDmDwMDAIutkZWUhOTlZ7kVERESkq1ROzk6ePInatWvj77//xp49e5CamgoAuHz5MqZPn670cp4/f468vDw4OTnJlTs5OSE2NrbYeStWrAhTU1P4+flhxIgR+PTTT4usO3fuXNjY2Mhebm5uSsdIRERE9LapnJxNnDgRs2fPxtGjR2FiYiIrb9asGc6ePatyAJLXLg8IIRTKXnfq1CmcP38eq1atwrJly7B9+/Yi606aNAlJSUmy18OHD1WOkYiIiOhtUblDwJUrV7Bt2zaF8vLly6s0/pmDgwMMDQ0VzpLFxcUpnE17nYeHBwCgdu3aePr0KWbMmIE+ffoUWtfU1BSmpqZKx0VERESkTSqfObO1tUVMTIxCeWRkJCpUqKD0ckxMTODr64ujR4/KlR89ehQBAQFKL0cIgaysLKXrExEREekylc+c9e3bFxMmTMCvv/4KiUSC/Px8nD59GuPGjUP//v1VWtaYMWPQr18/+Pn5oWHDhlizZg2io6MxdOhQAC8vST5+/BibN28GACxfvhyVKlWCl5cXgJfjni1atAhffPGFqs0gIiIi0kkqJ2dz5sxBcHAwKlSoACEEvL29kZeXh759+2LKlCkqLatXr16Ij4/HrFmzEBMTg1q1aiE0NFT2EPWYmBhER0fL6ufn52PSpEmIioqCkZERqlatinnz5mHIkCGqNoOIiIhIJ5V4nLN79+7h4sWLyM/PR/369d+Zh6BznDMiLeI4Z0T6geOclYrSZ87y8/OxePFi7Nu3Dzk5OWjZsiWmTZsGqVSqyfiIiIiI9IrSHQLmz5+PiRMnwsLCAi4uLliyZAlGjRqlydiIiIiI9I7SydnGjRvx448/4siRI/jtt9+wb98+bN68GVp8+hMRERFRmaN0cvbgwQN06NBB9j4oKAhCCDx58kQjgRERERHpI6WTs+zsbJiZmcneSyQSmJiYcIwxIiIiIjVSaSiNqVOnwtzcXPY+Ozsbc+bMgY2NjaxsyZIl6ouOiIiISM8onZw1adIEN2/elCsLCAjAvXv3ZO/f9ExMIiIiIiqe0slZeHi4BsMgIiIiIqAEz9YkIiIiIs1hckZERESkQ5icEREREekQJmdEREREOoTJGREREZEOUWmcMwC4fPlyoeUSiQRSqRSVKlWCqalpqQMjIiIi0kcqJ2f16tUrdjwzY2Nj9OrVC6tXr4ZUKi1VcERERET6RuXLmnv37kX16tWxZs0aXLp0CZGRkVizZg08PT2xbds2rFu3DidOnMCUKVM0ES8RERFRmabymbM5c+bg+++/R1BQkKysTp06qFixIqZOnYpz587BwsICY8eOxaJFi9QaLBEREVFZp/KZsytXrsDd3V2h3N3dHVeuXAHw8tJnTExM6aMjIiIi0jMqJ2deXl6YN28esrOzZWU5OTmYN28evLy8AACPHz+Gk5OT+qIkIiIi0hMqX9Zcvnw5OnXqhIoVK6JOnTqQSCS4fPky8vLycODAAQDAvXv3MHz4cLUHS0RERFTWSYQQQtWZUlNTsWXLFty6dQtCCHh5eaFv376wsrLSRIxqlZycDBsbGyQlJcHa2lr9KyimJyuR3lP9cKObuJ8TFU8D+7rGf791iMpnzgDA0tISQ4cOVXcsRERERHqvRMnZrVu3EB4ejri4OOTn58tNmzZtmloCIyIiItJHKidna9euxbBhw+Dg4ABnZ2e5AWklEgmTMyIiIqJSUDk5mz17NubMmYMJEyZoIh4iIiIivabyUBovXrxAjx49NBELERERkd5TOTnr0aMHjhw5oolYiIiIiPSeypc1q1WrhqlTp+Kvv/5C7dq1YWxsLDd91KhRaguOiIiISN+oPM6Zh4dH0QuTSHDv3r1SB6VJHOeMSIs4zhmRfuA4Z6Wi8pmzqKgoTcRBRERERCjBPWdEREREpDlKnTkbM2YMvv32W1hYWGDMmDHF1l2yZIlaAiMiIiLSR0olZ5GRkcjJyZH9TURERESaUaIHn7/L2CGASIvKyuGG+zlR8dghoFRUvuds0KBBSElJUShPS0vDoEGD1BIUERERkb5SOTnbtGkTMjIyFMozMjKwefNmtQRFREREpK+UHkojOTkZQggIIZCSkgKpVCqblpeXh9DQUDg6OmokSCIiIiJ9oXRyZmtrC4lEAolEgho1aihMl0gkmDlzplqDIyIiItI3SidnYWFhEEKgefPm2L17N+zs7GTTTExM4O7uDldXV40ESURERKQvlE7OAgMDAbx8QoCbmxsMDDh+LREREZG6qfz4Jnd3dyQmJmLdunW4fv06JBIJvL29MWjQINjY2GgiRiIiIiK9ofLpr/Pnz6Nq1apYunQpEhIS8Pz5cyxZsgRVq1bFxYsXNREjERERkd5QeRDaxo0bo1q1ali7di2MjF6eeMvNzcWnn36Ke/fu4X//+59GAlUXDkJLpEUchJZIP3AQ2lJROTkzMzNDZGQkvLy85MqvXbsGPz8/pKenqzVAdWNyRqRFTM6I9AOTs1JR+bKmtbU1oqOjFcofPnwIKysrtQRFREREpK9UTs569eqFwYMHIyQkBA8fPsSjR4+wY8cOfPrpp+jTp48mYiQiIiLSGyr31ly0aBEkEgn69++P3NxcAICxsTGGDRuGefPmqT1AIiIiIn2i8j1nBdLT03H37l0IIVCtWjWYm5urOzaN4D1nRFrEe86I9APvOSsVlc+cFTA3N0e5cuUgkUjemcSMiIiISNepfM9Zfn4+Zs2aBRsbG7i7u6NSpUqwtbXFt99+i/z8fE3ESERERKQ3VD5zNnnyZKxbtw7z5s1Do0aNIITA6dOnMWPGDGRmZmLOnDmaiJOIiIhIL6h85mzTpk34+eefMWzYMNSpUwd169bF8OHDsXbtWmzcuFHlAFasWAEPDw9IpVL4+vri1KlTRdbds2cPWrVqhfLly8Pa2hoNGzbE4cOHVV4nERERka5SOTlLSEhQGIAWALy8vJCQkKDSskJCQjB69GhMnjwZkZGRaNy4Mdq2bVvoOGoA8L///Q+tWrVCaGgoLly4gGbNmqFjx46IjIxUtRlEREREOknl3pr+/v7w9/fHDz/8IFf+xRdfICIiAn/99ZdKy/Lx8cHKlStlZTVr1kSXLl0wd+5cpZbx3nvvoVevXpg2bZpS9dlbk0iL2FuTSD+wt2apqHzP2YIFC9C+fXscO3YMDRs2hEQiwZkzZ/Dw4UOEhoYqvZzs7GxcuHABEydOlCtv3bo1zpw5o9Qy8vPzkZKSAjs7O5XaQERERKSrVL6sGRgYiFu3bqFr165ITExEQkICPvroI9y8eRONGzdWejnPnz9HXl4enJyc5MqdnJwQGxur1DIWL16MtLQ09OzZs8g6WVlZSE5OlnsRERER6aoSjXPm6uqqtl6ZktcuDwghFMoKs337dsyYMQO//fYbHB0di6w3d+5czJw5s9RxEhEREb0NJUrOEhMTce7cOcTFxSmMbda/f3+lluHg4ABDQ0OFs2RxcXEKZ9NeFxISgsGDB+PXX39Fy5Yti607adIkjBkzRvY+OTkZbm5uSsVIRERE9LapnJz9/vvv+Pjjj5GWlgYrKyu5s1wFz9xUhomJCXx9fXH06FF07dpVVn706FF07ty5yPm2b9+OQYMGYfv27Wjfvv0b12NqagpTU1OlYiIiIiLSNpWTs7Fjx2LQoEH47rvvSv3YpjFjxqBfv37w8/NDw4YNsWbNGkRHR2Po0KEAXp71evz4MTZv3gzgZWLWv39/fP/992jQoIHsrJuZmRlsbGxKFQsRERGRLlA5OXv8+DFGjRqlludp9urVC/Hx8Zg1axZiYmJQq1YthIaGwt3dHQAQExMjN+bZ6tWrkZubixEjRmDEiBGy8gEDBpRoAFwiIiIiXaPyOGcfffQRevfuXWwPSV3Gcc6ItIjjnBHpB45zVipKnTnbv3+/7O/27dtj/PjxuHbtGmrXrg1jY2O5up06dVJvhERERER6RKkzZwYGyg2HJpFIkJeXV+qgNIlnzoi0iGfOiPQDz5yVilJnzl4fLoOIiIiINEPlJwQQERERkeYonZz9/fff+OOPP+TKNm/eDA8PDzg6OuLzzz9HVlaW2gMkIiIi0idKJ2czZszA5cuXZe+vXLmCwYMHo2XLlpg4cSJ+//13zJ07VyNBEhEREekLpZOzS5cuoUWLFrL3O3bsgL+/P9auXYsxY8bghx9+wM6dOzUSJBEREZG+UDo5e/HihdwzL0+ePIk2bdrI3r///vt4+PCheqMjIiIi0jNKJ2dOTk6IiooCAGRnZ+PixYto2LChbHpKSorCmGdEREREpBqlk7M2bdpg4sSJOHXqFCZNmgRzc3M0btxYNv3y5cuoWrWqRoIkIiIi0hdKP1tz9uzZ+OijjxAYGAhLS0ts2rQJJiYmsunr169H69atNRIkERERkb5Q+dmaSUlJsLS0hKGhoVx5QkICLC0t5RI2XcQnBBBpEZ8QQKQf+ISAUlH6zFkBGxubQsvt7OxKHQwRERGRvuMTAoiIiIh0CJMzIiIiIh3C5IyIiIhIhzA5IyIiItIhJUrOfvnlFzRq1Aiurq548OABAGDZsmX47bff1BocERERkb5ROTlbuXIlxowZg3bt2iExMRF5eXkAAFtbWyxbtkzd8RERERHpFZWTsx9//BFr167F5MmT5cY68/Pzw5UrV9QaHBEREZG+UTk5i4qKQv369RXKTU1NkZaWppagiIiIiPSVysmZh4cHLl26pFD+xx9/wNvbWx0xEREREektlZ8QMH78eIwYMQKZmZkQQuDcuXPYvn075s6di59//lkTMRIRERHpDZWTs4EDByI3Nxdff/010tPT0bdvX1SoUAHff/89evfurYkYiYiIiPSGyg8+f9Xz58+Rn58PR0dHdcakUXzwOZEW8cHnRPqBDz4vFZXPnL3KwcFBXXEQEREREUqQnHl4eEBSzH+N9+7dK1VARERERPpM5eRs9OjRcu9zcnIQGRmJQ4cOYfz48eqKi4iIiEgvqZycffnll4WWL1++HOfPny91QERERET6TG0PPm/bti12796trsURERER6SW1JWe7du2CnZ2duhZHREREpJdUvqxZv359uQ4BQgjExsbi2bNnWLFihVqDIyIiItI3KidnXbp0kXtvYGCA8uXLo2nTpvDy8lJXXERERER6SaXkLDc3F5UrV0ZQUBCcnZ01FRMRERGR3lLpnjMjIyMMGzYMWVlZmoqHiIiISK+p3CHA398fkZGRmoiFiIiISO+pfM/Z8OHDMXbsWDx69Ai+vr6wsLCQm16nTh21BUdERESkb5R+8PmgQYOwbNky2NraKi5EIoEQAhKJBHl5eeqOUa344HMiLeKDz4n0Ax98XipKJ2eGhoaIiYlBRkZGsfXc3d3VEpimMDkj0iImZ0T6gclZqSh9WbMgh9P15IuIiIjoXaZShwAJ/1skIiIi0iiVOgTUqFHjjQlaQkJCqQIiIiIi0mcqJWczZ86EjY2NpmIhIiIi0nsqJWe9e/eGo6OjpmIhIiIi0ntK33PG+82IiIiINE/p5EzJETeIiIiIqBSUvqyZn5+vyTiIiIiICCV4tiYRERERaQ6TMyIiIiIdwuSMiIiISIcwOSMiIiLSIVpPzlasWAEPDw9IpVL4+vri1KlTRdaNiYlB37594enpCQMDA4wePfrtBUpERET0Fmg1OQsJCcHo0aMxefJkREZGonHjxmjbti2io6MLrZ+VlYXy5ctj8uTJqFu37luOloiIiEjzJEKLA5j5+/vDx8cHK1eulJXVrFkTXbp0wdy5c4udt2nTpqhXrx6WLVum0jqTk5NhY2ODpKQkWFtblyTs4nGwXqKilZXxErmfExVPA/u6xn+/dYjWzpxlZ2fjwoULaN26tVx569atcebMGS1FRURERKRdKj1bU52eP3+OvLw8ODk5yZU7OTkhNjZWbevJyspCVlaW7H1ycrLalk1ERESkblrvEPD6MzuFEGp9jufcuXNhY2Mje7m5ualt2URERETqprXkzMHBAYaGhgpnyeLi4hTOppXGpEmTkJSUJHs9fPhQbcsmIiIiUjetJWcmJibw9fXF0aNH5cqPHj2KgIAAta3H1NQU1tbWci8iIiIiXaW1e84AYMyYMejXrx/8/PzQsGFDrFmzBtHR0Rg6dCiAl2e9Hj9+jM2bN8vmuXTpEgAgNTUVz549w6VLl2BiYgJvb29tNIGIiIhIrbSanPXq1Qvx8fGYNWsWYmJiUKtWLYSGhsLd3R3Ay0FnXx/zrH79+rK/L1y4gG3btsHd3R33799/m6ETERERaYRWxznTBo5zRqRFZeVww/2cqHgc56xUtN5bk4iIiIj+w+SMiIiISIcwOSMiIiLSIUzOiIiIiHQIkzMiIiIiHcLkjIiIiEiHMDkjIiIi0iFMzoiIiIh0CJMzIiIiIh3C5IyIiIhIhzA5IyIiItIhTM6IiIiIdAiTMyIiIiIdwuSMiIiISIcwOSMiIiLSIUzOiIiIiHQIkzMiIiIiHcLkjIiIiEiHMDkjIiIi0iFMzoiIiIh0CJMzIiIiIh3C5IyIiIhIhzA5IyIiItIhTM6IiIiIdAiTMyIiIiIdwuSMiIiISIcwOSMiIiLSIUzOiIiIiHQIkzMiIiIiHcLkjIiIiEiHMDkjIiIi0iFMzoiIiIh0CJMzIiIiIh3C5IyIiIhIhzA5IyIiItIhTM6IiIiIdAiTMyIiIiIdwuSMiIiISIcwOSMiIiLSIUzOiIiIiHQIkzMiIiIiHcLkjIiIiEiHMDkjIiIi0iFMzoiIiIh0CJMzIiIiIh3C5IyIiIhIhzA5IyIiItIhTM6IiIiIdAiTMyIiIiIdwuSMiIiISIcwOSMiIiLSIVpPzlasWAEPDw9IpVL4+vri1KlTxdY/efIkfH19IZVKUaVKFaxateotRUpERESkeVpNzkJCQjB69GhMnjwZkZGRaNy4Mdq2bYvo6OhC60dFRaFdu3Zo3LgxIiMj8c0332DUqFHYvXv3W46ciIiISDMkQgihrZX7+/vDx8cHK1eulJXVrFkTXbp0wdy5cxXqT5gwAfv378f169dlZUOHDsU///yDs2fPKrXO5ORk2NjYICkpCdbW1qVvxOskEvUvk6is0N7hRr24nxMVTwP7usZ/v3WI1s6cZWdn48KFC2jdurVceevWrXHmzJlC5zl79qxC/aCgIJw/fx45OTkai5WIiIjobTHS1oqfP3+OvLw8ODk5yZU7OTkhNja20HliY2MLrZ+bm4vnz5/DxcVFYZ6srCxkZWXJ3iclJQF4mYET0VvG/Y5IP2hgXy/43dbiBb+3RmvJWQHJa5cHhBAKZW+qX1h5gblz52LmzJkK5W5ubqqGSkSlZWOj7QiI6G3Q4L6ekpICmzJ+LNFacubg4ABDQ0OFs2RxcXEKZ8cKODs7F1rfyMgI9vb2hc4zadIkjBkzRvY+Pz8fCQkJsLe3LzYJLEuSk5Ph5uaGhw8flvnr9K/S13YD+tt2fW03wLbrY9v1rd1CCKSkpMDV1VXboWic1pIzExMT+Pr64ujRo+jataus/OjRo+jcuXOh8zRs2BC///67XNmRI0fg5+cHY2PjQucxNTWFqampXJmtrW3pgn9HWVtb68UO/Dp9bTegv23X13YDbLs+tl2f2l3Wz5gV0OpQGmPGjMHPP/+M9evX4/r16/jqq68QHR2NoUOHAnh51qt///6y+kOHDsWDBw8wZswYXL9+HevXr8e6deswbtw4bTWBiIiISK20es9Zr169EB8fj1mzZiEmJga1atVCaGgo3N3dAQAxMTFyY555eHggNDQUX331FZYvXw5XV1f88MMP6Natm7aaQERERKRWWu8QMHz4cAwfPrzQaRs3blQoCwwMxMWLFzUcVdliamqK6dOnK1zeLev0td2A/rZdX9sNsO362HZ9bbc+0OogtEREREQkT+vP1iQiIiKi/zA5IyIiItIhTM6IiIiIdAiTMyIiIiIdwuRMS/73v/+hY8eOcHV1hUQiwb59++SmP336FMHBwXB1dYW5uTnatGmD27dvy9W5e/cuunbtivLly8Pa2ho9e/bE06dP5epUrlwZEolE7jVx4sRiY3u9fsFr4cKFsjpNmzZVmN67d2+dajsAHDx4EP7+/jAzM4ODgwM++uijYmMTQmDGjBlwdXWFmZkZmjZtiqtXr8rVycrKwhdffAEHBwdYWFigU6dOePTo0Tvd9pycHEyYMAG1a9eGhYUFXF1d0b9/fzx58kSuXkk/d11tNwAEBwcrtKlBgwZydcriZw5odl9/G+0ODw8vsg0RERFFxlYW9vOStF3T+zmpD5MzLUlLS0PdunXx008/KUwTQqBLly64d+8efvvtN0RGRsLd3R0tW7ZEWlqabP7WrVtDIpHgxIkTOH36NLKzs9GxY0fk5+fLLa9gHLmC15QpU4qN7dW6MTExWL9+PSQSicJ4cp999plcvdWrV+tU23fv3o1+/fph4MCB+Oeff3D69Gn07du32NgWLFiAJUuW4KeffkJERAScnZ3RqlUrpKSkyOqMHj0ae/fuxY4dO/Dnn38iNTUVHTp0QF5e3jvb9vT0dFy8eBFTp07FxYsXsWfPHty6dQudOnVSqFuSz11X212gTZs2cm0KDQ2Vm14WP3NAs/v622h3QECAQhs+/fRTVK5cGX5+fkXGVhb285K0XdP7OamRIK0DIPbu3St7f/PmTQFA/Pvvv7Ky3NxcYWdnJ9auXSuEEOLw4cPCwMBAJCUlyeokJCQIAOLo0aOyMnd3d7F06dJSxde5c2fRvHlzubLAwEDx5Zdflmq5Qmiu7Tk5OaJChQri559/VjqW/Px84ezsLObNmycry8zMFDY2NmLVqlVCCCESExOFsbGx2LFjh6zO48ePhYGBgTh06NA72/bCnDt3TgAQDx48kJWp43PXtXYPGDBAdO7cucjp+vSZa2pf1+Qx7lXZ2dnC0dFRzJo1q8hYysp+XpK2F0ZT+zmVDs+c6aCsrCwAgFQqlZUZGhrCxMQEf/75p6yORCKRG3xQKpXCwMBAVqfA/PnzYW9vj3r16mHOnDnIzs5WOpanT5/i4MGDGDx4sMK0rVu3wsHBAe+99x7GjRsn919nSamr7RcvXsTjx49hYGCA+vXrw8XFBW3btlW4dPGqqKgoxMbGonXr1rIyU1NTBAYG4syZMwCACxcuICcnR66Oq6sratWqJavzLra9MElJSZBIJArPolX3564L7Q4PD4ejoyNq1KiBzz77DHFxcbJp+vKZv819Xd3HuAL79+/H8+fPERwcXOS6y8p+/jpl2l6Yt7Wfk2qYnOkgLy8vuLu7Y9KkSXjx4gWys7Mxb948xMbGIiYmBgDQoEEDWFhYYMKECUhPT0daWhrGjx+P/Px8WR0A+PLLL7Fjxw6EhYVh5MiRWLZsWZFPZCjMpk2bYGVlpXDvyscff4zt27cjPDwcU6dOxe7du994f8vbbPu9e/cAADNmzMCUKVNw4MABlCtXDoGBgUhISCh03bGxsQAAJycnuXInJyfZtNjYWJiYmKBcuXJF1nkX2/66zMxMTJw4EX379pV7oLImPndtt7tt27bYunUrTpw4gcWLFyMiIgLNmzeX/Yjqy2f+Nvd1dR7jXrVu3ToEBQXBzc2tyHWXlf38dcq0/XVvcz8nFWn71B0pnvYWQojz58+LunXrCgDC0NBQBAUFibZt24q2bdvK6hw+fFhUqVJFSCQSYWhoKD755BPh4+Mjhg0bVuS6du3aJQCI58+fKxWbp6enGDly5BvrnT9/XgAQFy5cUGq5BTTV9q1btwoAYvXq1bJ5MjMzhYODg+zSxetOnz4tAIgnT57IlX/66aciKChItlwTExOFeVu2bCmGDBnyzrb9VdnZ2aJz586ifv36cpdVClOSz11X213gyZMnwtjYWOzevVu23LL+mQuh2X39bRzjHj58KAwMDMSuXbuKjaWs7OevUrbtr9L0fk6lo/Vna1LhfH19cenSJSQlJSE7Oxvly5eHv7+/3I2erVu3xt27d/H8+XMYGRnB1tYWzs7O8PDwKHK5Bb3Q7ty5A3t7+2JjOHXqFG7evImQkJA3xuvj4wNjY2Pcvn0bPj4+SraycOpou4uLCwDA29tbNo+pqSmqVKmC6OjoQtfr7OwM4OV/zQXzA0BcXJzsv2xnZ2dkZ2fjxYsXcv9Vx8XFISAgoFTt1mbbC+Tk5KBnz56IiorCiRMn5P6bLoy6Pndtt/tVLi4ucHd3l/WeK+ufOaCdfV3dx7gNGzbA3t6+0JvbX1VW9vNXKdv2Atraz0l5vKyp42xsbFC+fHncvn0b58+fR+fOnRXqODg4wNbWFidOnEBcXFyxO2hkZCQAyB2UirJu3Tr4+vqibt26b6x79epV5OTkKLVcZZWm7b6+vjA1NcXNmzdldXNycnD//n24u7sXuj4PDw84Ozvj6NGjsrLs7GycPHlSdkD29fWFsbGxXJ2YmBj8+++/ajloF3jbbS+o07NnT9y+fRvHjh17Y/IOqP9z10a7XxcfH4+HDx/K2lSWP/MC2tzX1XGME0Jgw4YN6N+/P4yNjYtdX1nZzwuo0nZAN/ZzUoK2T93pq5SUFBEZGSkiIyMFALFkyRIRGRkp6zGzc+dOERYWJu7evSv27dsn3N3dxUcffSS3jPXr14uzZ8+KO3fuiF9++UXY2dmJMWPGyKafOXNGttx79+6JkJAQ4erqKjp16iS3HE9PT7Fnzx65sqSkJGFubi5WrlypEPudO3fEzJkzRUREhIiKihIHDx4UXl5eon79+iI3N1cn2i6EEF9++aWoUKGCOHz4sLhx44YYPHiwcHR0FAkJCUW2fd68ecLGxkbs2bNHXLlyRfTp00e4uLiI5ORkWZ2hQ4eKihUrimPHjomLFy+K5s2bi7p1677Tbc/JyRGdOnUSFStWFJcuXRIxMTGyV1ZWlhCidJ+7rrY7JSVFjB07Vpw5c0ZERUWJsLAw0bBhQ1GhQoUy/5kX0NS+/rbaLYQQx44dEwDEtWvXCo2lrO7nqrZd0/s5qQ+TMy0JCwsTABReAwYMEEII8f3334uKFSsKY2NjUalSJTFlyhTZzlNgwoQJwsnJSRgbG4vq1auLxYsXi/z8fNn0CxcuCH9/f2FjYyOkUqnw9PQU06dPF2lpaXLLASA2bNggV7Z69WphZmYmEhMTFWKPjo4WTZo0EXZ2dsLExERUrVpVjBo1SsTHx+tM24V4eU/F2LFjhaOjo7CyshItW7aU675eWNvz8/PF9OnThbOzszA1NRVNmjQRV65ckZsnIyNDjBw5UtjZ2QkzMzPRoUMHER0d/U63PSoqqtC4AIiwsDAhROk+d11td3p6umjdurUoX768bN0DBgxQ+DzL4mdeQFP7+ttqtxBC9OnTRwQEBBQZS1ndz1Vtu6b3c1IfiRBCqOEEHBERERGpAe85IyIiItIhTM6IiIiIdAiTMyIiIiIdwuSMiIiISIcwOSMiIiLSIUzOiIiIiHQIkzMiIiIiHcLkjIiIiEiHMDkjorcuLi4OQ4YMQaVKlWBqagpnZ2cEBQXh7Nmz2g6NiEjrjLQdABHpn27duiEnJwebNm1ClSpV8PTpUxw/fhwJCQnaDo2ISOt45oyI3qrExET8+eefmD9/Ppo1awZ3d3d88MEHmDRpEtq3bw8ASEpKwueffw5HR0dYW1ujefPm+Oeff2TLuHv3Ljp37gwnJydYWlri/fffx7Fjx+TWs2LFClSvXh1SqRROTk7o3r27bFpWVhZGjRoFR0dHSKVSfPjhh4iIiJBNDw8Ph0QiwfHjx+Hn5wdzc3MEBATg5s2bGt46RERMzojoLbO0tISlpSX27duHrKwshelCCLRv3x6xsbEIDQ3FhQsX4OPjgxYtWsjOrKWmpqJdu3Y4duwYIiMjERQUhI4dOyI6OhoAcP78eYwaNQqzZs3CzZs3cejQITRp0kS2jq+//hq7d+/Gpk2bcPHiRVSrVg1BQUEKZ+4mT56MxYsX4/z58zAyMsKgQYM0uGWIiP6flh+8TkR6aNeuXaJcuXJCKpWKgIAAMWnSJPHPP/8IIYQ4fvy4sLa2FpmZmXLzVK1aVaxevbrIZXp7e4sff/xRCCHE7t27hbW1tUhOTlaol5qaKoyNjcXWrVtlZdnZ2cLV1VUsWLBACCFEWFiYACCOHTsmq3Pw4EEBQGRkZJS84URESuCZMyJ667p164YnT55g//79CAoKQnh4OHx8fLBx40ZcuHABqampsLe3l51ls7S0RFRUFO7evQsASEtLw9dffw1vb2/Y2trC0tISN27ckJ05a9WqFdzd3VGlShX069cPW7duRXp6OoCXl0RzcnLQqFEjWTzGxsb44IMPcP36dbk469SpI/vbxcUFwMvODEREmsQOAUSkFVKpFK1atUKrVq0wbdo0fPrpp5g+fTqGDx8OFxcXhIeHK8xja2sLABg/fjwOHz6MRYsWoVq1ajAzM0P37t2RnZ0NALCyssLFixcRHh6OI0eOYNq0aZgxYwYiIiIghAAASCQSuWULIRTKjI2NZX8XTMvPz1fXJiAiKhTPnBGRTvD29kZaWhp8fHwQGxsLIyMjVKtWTe7l4OAAADh16hSCg4PRtWtX1K5dG87Ozrh//77c8oyMjNCyZUssWLAAly9fxv3793HixAlUq1YNJiYm+PPPP2V1c3JycP78edSsWfNtNpmIqFA8c0ZEb1V8fDx69OiBQYMGoU6dOrCyssL58+exYMECdO7cGS1btkTDhg3RpUsXzJ8/H56ennjy5AlCQ0PRpUsX+Pn5oVq1atizZw86duwIiUSCqVOnyp3ROnDgAO7du4cmTZqgXLlyCA0NRX5+Pjw9PWFhYYFhw4Zh/PjxsLOzQ6VKlbBgwQKkp6dj8ODBWtwyREQvMTkjorfK0tIS/v7+WLp0qez+Lzc3N3z22Wf45ptvIJFIEBoaismTJ2PQoEF49uwZnJ2d0aRJEzg5OQEAli5dikGDBiEgIAAODg6YMGECkpOTZeuwtbXFnj17MGPGDGRmZqJ69erYvn073nvvPQDAvHnzkJ+fj379+iElJQV+fn44fPgwypUrp5VtQkT0KokouAGDiIiIiLSO95wRERER6RAmZ0REREQ6hMkZERERkQ5hckZERESkQ5icEREREekQJmdEREREOoTJGREREZEOYXJGREREpEOYnBERERHpECZnRERERDqEyRkRERGRDmFyRkRERKRD/g8beS6disjZnwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mj_ts_avg = mj_prime_stats_df.groupby('Season')['TS%'].mean()\n",
    "league_avg_ts = prime_1996_1997_df['TS%'].mean()\n",
    "plt.bar(mj_ts_avg.index, mj_ts_avg, color='red', label='Michael Jordan')\n",
    "plt.axhline(league_avg_ts, color='blue', linestyle='--', label='League Average')\n",
    "plt.title('True Shooting Percentage Michael Jordan compared to League Average')\n",
    "plt.xlabel('Season')\n",
    "plt.ylabel('True Shooting Percentage (TS%)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           League  Michael Jordan\n",
      "count  763.000000        2.000000\n",
      "mean     0.497723        0.550000\n",
      "std      0.089691        0.024042\n",
      "min      0.000000        0.533000\n",
      "25%      0.470000        0.541500\n",
      "50%      0.514000        0.550000\n",
      "75%      0.543000        0.558500\n",
      "max      1.042000        0.567000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_1996_1997_df['TS%'].describe()\n",
    "summary_mj = mj_prime_stats_df['TS%'].describe()\n",
    "\n",
    "# Concatenate the summaries horizontally\n",
    "summary_df = pd.concat([summary_league, summary_mj], axis=1)\n",
    "summary_df.columns = ['League', 'Michael Jordan']\n",
    "\n",
    "# Print the summary DataFrame\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, too, we observe a similar outcome to the eFG% when examining the TS%. Michael displays extremly high efficiency when it comes to scoring."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Black Mamba: Kobe Bryant\n",
    "\n",
    "Kobe Bryant, one of the greatest basketball players of all time, dominated the NBA during his prime years from 2005 to 2007. Known for his incredible scoring ability and relentless competitiveness, Bryant's performances during this period were nothing short of legendary. As we analyze his stats and compare them to the league's season averages, we gain deeper insights into his extraordinary impact on the game. \n",
    "\n",
    "Key Achievements during his prime:\n",
    "\n",
    "- NBA Most Valuable Player (2007-2008)\n",
    "- All-NBA First Team (2005-2006, 2006-2007, 2007-2008)\n",
    "- NBA scoring champion (2005-2006, 2006-2007)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparation\n",
    "\n",
    "Before we start to analyse we need to define the dataframes containing the season stats of those individual years in which Kobe was at his peak. To do to that we use following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "prime_2005_2007_df = nba_stats_df[nba_stats_df['Season'].isin([2005, 2006, 2007])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>DD2</th>\n",
       "      <th>L</th>\n",
       "      <th>PLAYER_NAME</th>\n",
       "      <th>PLUS_MINUS</th>\n",
       "      <th>TD3</th>\n",
       "      <th>W</th>\n",
       "      <th>ast</th>\n",
       "      <th>blk</th>\n",
       "      <th>dreb</th>\n",
       "      <th>...</th>\n",
       "      <th>GS</th>\n",
       "      <th>OBPM</th>\n",
       "      <th>OWS</th>\n",
       "      <th>PER</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Season</th>\n",
       "      <th>TS%</th>\n",
       "      <th>USG%</th>\n",
       "      <th>WS</th>\n",
       "      <th>eFG%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>29</td>\n",
       "      <td>20.0</td>\n",
       "      <td>25</td>\n",
       "      <td>Kobe Bryant</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57</td>\n",
       "      <td>5.38</td>\n",
       "      <td>0.49</td>\n",
       "      <td>5.16</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>9.5</td>\n",
       "      <td>24.2</td>\n",
       "      <td>SG</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.576</td>\n",
       "      <td>31.4</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0.503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1667</th>\n",
       "      <td>27</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35</td>\n",
       "      <td>Kobe Bryant</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.38</td>\n",
       "      <td>4.43</td>\n",
       "      <td>...</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>11.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>2005</td>\n",
       "      <td>0.559</td>\n",
       "      <td>38.7</td>\n",
       "      <td>15.3</td>\n",
       "      <td>0.491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2854</th>\n",
       "      <td>28</td>\n",
       "      <td>10.0</td>\n",
       "      <td>38</td>\n",
       "      <td>Kobe Bryant</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39</td>\n",
       "      <td>5.36</td>\n",
       "      <td>0.47</td>\n",
       "      <td>4.73</td>\n",
       "      <td>...</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>10.8</td>\n",
       "      <td>26.1</td>\n",
       "      <td>SG</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.580</td>\n",
       "      <td>33.6</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      AGE   DD2   L  PLAYER_NAME  PLUS_MINUS  TD3   W   ast   blk  dreb  ...  \\\n",
       "14     29  20.0  25  Kobe Bryant         7.3  0.0  57  5.38  0.49  5.16  ...   \n",
       "1667   27   4.0  35  Kobe Bryant         4.0  0.0  45  4.50  0.38  4.43  ...   \n",
       "2854   28  10.0  38  Kobe Bryant         1.3  0.0  39  5.36  0.47  4.73  ...   \n",
       "\n",
       "        GS  OBPM   OWS   PER  Pos  Season    TS%  USG%    WS   eFG%  \n",
       "14    82.0   5.1   9.5  24.2   SG    2007  0.576  31.4  13.8  0.503  \n",
       "1667  80.0   7.3  11.6  28.0   SG    2005  0.559  38.7  15.3  0.491  \n",
       "2854  77.0   6.1  10.8  26.1   SG    2006  0.580  33.6  13.0  0.502  \n",
       "\n",
       "[3 rows x 43 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kobe_prime_stats_df = prime_2005_2007_df[prime_2005_2007_df['PLAYER_NAME'] == 'Kobe Bryant']\n",
    "kobe_prime_stats_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Points\n",
    "\n",
    "In this section, we will analyze the points scored by players during the 2005-2007 NBA seasons. Specifically, we will focus on comparing Kobe Bryant's scoring performance to that of the league as a whole:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the boxplot\n",
    "plt.boxplot(prime_2005_2007_df['pts'], vert=False)\n",
    "\n",
    "# Plot Kobe Bryant's stats as red markers\n",
    "kobe_points = kobe_prime_stats_df['pts']\n",
    "for point in kobe_points:\n",
    "    plt.plot(point, 1, 'ro')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title('Boxplot of Points in the 2005-2007 NBA Seasons')\n",
    "plt.xlabel('Points')\n",
    "plt.ylabel('Season')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            League  Kobe Bryant\n",
      "count  1287.000000     3.000000\n",
      "mean      8.087910    31.763333\n",
      "std       6.121379     3.539383\n",
      "min       0.000000    28.330000\n",
      "25%       3.330000    29.945000\n",
      "50%       6.480000    31.560000\n",
      "75%      11.495000    33.480000\n",
      "max      35.400000    35.400000\n"
     ]
    }
   ],
   "source": [
    "# Create summaries for 'pts' column in both DataFrames\n",
    "summary_league = prime_2005_2007_df['pts'].describe()\n",
    "summary_kobe = kobe_prime_stats_df['pts'].describe()\n",
    "\n",
    "# Concatenate the summaries horizontally\n",
    "summary_df = pd.concat([summary_league, summary_kobe], axis=1)\n",
    "summary_df.columns = ['League', 'Kobe Bryant']\n",
    "\n",
    "# Print the summary DataFrame\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kobe Bryant demonstrated exceptional scoring ability, as indicated by his average of 31.76 points per game. This places him significantly above the league average of 8.09 points per game. His performances consistently exceeded not only the league average but also other great players' averages, and he had multiple standout scoring seasons where he displayed his dominance and impact on the court."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assists\n",
    "\n",
    "When analyzing Kobe Bryant's assists during the 2005-2007 NBA seasons, it is essential to consider his position as a shooting guard (SG). While traditionally point guards are primarily responsible for playmaking and distributing the ball, examining assists for shooting guards can provide valuable insights into their role and impact on the game."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(prime_2005_2007_df['ast'], vert=False)\n",
    "\n",
    "# Plot Kobe Bryant's assists as red markers\n",
    "kobe_assists = kobe_prime_stats_df['ast']\n",
    "\n",
    "for assist in kobe_assists:\n",
    "    plt.plot(assist, 1, 'ro')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title('Boxplot of Assists in the 2005-2007 NBA Seasons (Shooting Guards)')\n",
    "plt.xlabel('Assists')\n",
    "plt.ylabel('Season')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            League  Kobe Bryant\n",
      "count  1287.000000     3.000000\n",
      "mean      1.757389     5.080000\n",
      "std       1.819485     0.502394\n",
      "min       0.000000     4.500000\n",
      "25%       0.490000     4.930000\n",
      "50%       1.130000     5.360000\n",
      "75%       2.395000     5.370000\n",
      "max      11.630000     5.380000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_2005_2007_df['ast'].describe()\n",
    "summary_kobe = kobe_prime_stats_df['ast'].describe()\n",
    "\n",
    "# Concatenate the summaries horizontally\n",
    "summary_df = pd.concat([summary_league, summary_kobe], axis=1)\n",
    "summary_df.columns = ['League', 'Kobe Bryant']\n",
    "\n",
    "# Print the summary DataFrame\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the boxplot, we observe that Kobe Bryant's assist numbers are generally higher than the league average, positioning him as an outlier. The summary statistics indicate that Kobe Bryant averaged 5.08 assists per game which places him above the league average of 1.76 assists, showcasing his exceptional playmaking skills. This consistency in his assists highlights his ability to consistently contribute to his team's offense despite not playing as a point guard."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rebounds\n",
    "\n",
    "Examining Kobe Bryant's rebounding performance during the 2005-2007 NBA seasons provides insights into his versatility and ability to contribute beyond his primary role as a shooting guard. Despite being 198cm tall, which is relatively average for a shooting guard, he was very athletic. Did that help him? Let us find out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter the DataFrame for shooting guards\n",
    "shooting_guards_df = prime_2005_2007_df[prime_2005_2007_df['Pos'] == 'SG']\n",
    "\n",
    "# Calculate the league average rebounds for shooting guards\n",
    "league_avg_rebounds_sg = shooting_guards_df['reb'].mean()\n",
    "\n",
    "# Calculate the league average rebounds for each season\n",
    "league_avg_rebounds_2005 = prime_2005_2007_df[prime_2005_2007_df['Season'] == 2005]['reb'].mean()\n",
    "league_avg_rebounds_2006 = prime_2005_2007_df[prime_2005_2007_df['Season'] == 2006]['reb'].mean()\n",
    "league_avg_rebounds_2007 = prime_2005_2007_df[prime_2005_2007_df['Season'] == 2007]['reb'].mean()\n",
    "\n",
    "# Plot the league average rebounds for shooting guards as three distinct points\n",
    "plt.plot([2005, 2006, 2007], [league_avg_rebounds_2005, league_avg_rebounds_2006, league_avg_rebounds_2007],\n",
    "         color='blue', marker='o', linestyle='--', label='League Average (SG)')\n",
    "\n",
    "# Plot Kobe Bryant's rebounds as a line graph\n",
    "kobe_rebounds = kobe_prime_stats_df['reb']\n",
    "seasons = kobe_rebounds.index\n",
    "\n",
    "plt.plot([2005, 2006, 2007], kobe_rebounds, color='red', marker='o', label='Kobe Bryant (SG)')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title('Rebounds in the 2005-2007 NBA Seasons (Shooting Guards)')\n",
    "plt.xlabel('Season')\n",
    "plt.ylabel('Rebounds')\n",
    "plt.legend()\n",
    "\n",
    "# Set the x-axis tick positions and labels\n",
    "plt.xticks([2005, 2006, 2007], ['2005', '2006', '2007'])\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            League  Kobe Bryant\n",
      "count  1287.000000     3.000000\n",
      "mean      3.521974     5.770000\n",
      "std       2.445793     0.498698\n",
      "min       0.000000     5.310000\n",
      "25%       1.750000     5.505000\n",
      "50%       2.890000     5.700000\n",
      "75%       4.640000     6.000000\n",
      "max      14.160000     6.300000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_2005_2007_df['reb'].describe()\n",
    "summary_kobe = kobe_prime_stats_df['reb'].describe()\n",
    "\n",
    "# Concatenate the summaries horizontally\n",
    "summary_df = pd.concat([summary_league, summary_kobe], axis=1)\n",
    "summary_df.columns = ['League', 'Kobe Bryant']\n",
    "\n",
    "# Print the summary DataFrame\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kobe Bryant's rebounding performance as a shooting guard during the 2005-2007 NBA seasons was exceptional. With an average of 5.77 rebounds per game, Kobe consistently outperformed the league average of 3.52 rebounds per game for shooting guards. Standing at 198cm tall, Kobe's ability to secure rebounds demonstrated his versatility and determination on the court. His rebounding prowess added to his all-around impact on the game, showcasing his ability to contribute in multiple facets beyond scoring."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Steals\n",
    "\n",
    "Kobe was generally considered a two-way player meaning that he was good both on offence and defence, but do the stats mirror this view of him? Let us find out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "league_steals_2005_2007 = prime_2005_2007_df.groupby('PLAYER_NAME')['stl'].mean()\n",
    "\n",
    "# Sort the data by steals per game in descending order\n",
    "sorted_steals = league_steals_2005_2007.sort_values(ascending=False)\n",
    "\n",
    "# Determine the position of Kobe Bryant in the sorted list\n",
    "kobe_position = sorted_steals.index.get_loc('Kobe Bryant')\n",
    "\n",
    "# Create the bar chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(range(len(sorted_steals)), sorted_steals, color='blue')\n",
    "plt.axhline(y=sorted_steals.iloc[kobe_position], color='red', linestyle='--', label='Kobe Bryant')\n",
    "\n",
    "# Set the title and axis labels\n",
    "plt.title('Steals in the 2005-2007 NBA Seasons')\n",
    "plt.xlabel('Player')\n",
    "plt.ylabel('Steals per Game')\n",
    "\n",
    "# Label Kobe Bryant's position\n",
    "plt.text(kobe_position, sorted_steals.iloc[kobe_position], 'Kobe Bryant', ha='center', va='bottom')\n",
    "\n",
    "# Remove the player labels at the bottom\n",
    "plt.xticks([])\n",
    "\n",
    "# Show the chart\n",
    "plt.tight_layout()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            League  Kobe Bryant\n",
      "count  1287.000000     3.000000\n",
      "mean      0.613644     1.706667\n",
      "std       0.435087     0.230940\n",
      "min       0.000000     1.440000\n",
      "25%       0.300000     1.640000\n",
      "50%       0.530000     1.840000\n",
      "75%       0.860000     1.840000\n",
      "max       2.710000     1.840000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_2005_2007_df['stl'].describe()\n",
    "summary_kobe = kobe_prime_stats_df['stl'].describe()\n",
    "\n",
    "# Concatenate the summaries horizontally\n",
    "summary_df = pd.concat([summary_league, summary_kobe], axis=1)\n",
    "summary_df.columns = ['League', 'Kobe Bryant']\n",
    "\n",
    "# Print the summary DataFrame\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The numbers and the graphs reflect his ability to make impactful defensive plays and contribute to the team's overall defensive efforts. Comparatively, the league average for steals during the same period was 0.61 steals per game. Kobe's steal averages significantly exceed the league average, indicating his proficiency in disrupting opponents' offensive plays and showcasing his defensive prowess. These statistics support the notion that Kobe Bryant was indeed a formidable defensive player in addition to his offensive skills. His ability to consistently contribute steals demonstrates his active presence on the defensive end and highlights his commitment to being a well-rounded player."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Blocks\n",
    "\n",
    "Since shorter players and positions like guards usually are not protecting the basketball rim, they block less shots. For this reason we will compare Kobe's blocks stats between him and other shooting guards:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter the data for shooting guards in the league\n",
    "shooting_guards_df = prime_2005_2007_df[prime_2005_2007_df['Pos'] == 'SG']\n",
    "\n",
    "# Calculate the blocks per game for shooting guards\n",
    "shooting_guard_blocks = shooting_guards_df.groupby('PLAYER_NAME')['blk'].mean()\n",
    "\n",
    "# Sort the data by blocks per game in descending order\n",
    "sorted_blocks = shooting_guard_blocks.sort_values(ascending=False)\n",
    "\n",
    "# Determine the position of Kobe Bryant in the sorted list\n",
    "kobe_position = sorted_blocks.index.get_loc('Kobe Bryant')\n",
    "\n",
    "# Create the bar chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(range(len(sorted_blocks)), sorted_blocks, color='blue')\n",
    "plt.axhline(y=sorted_blocks.iloc[kobe_position], color='red', linestyle='--', label='Kobe Bryant')\n",
    "\n",
    "# Set the title and axis labels\n",
    "plt.title('Blocks per Game in the 2005-2007 NBA Seasons')\n",
    "plt.xlabel('Player')\n",
    "plt.ylabel('Blocks per Game')\n",
    "\n",
    "# Label Kobe Bryant's position\n",
    "plt.text(kobe_position, sorted_blocks.iloc[kobe_position], 'Kobe Bryant', ha='center', va='bottom')\n",
    "\n",
    "# Remove the player labels at the bottom\n",
    "plt.xticks([])\n",
    "\n",
    "# Show the chart\n",
    "plt.tight_layout()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            League  Kobe Bryant\n",
      "count  1287.000000     3.000000\n",
      "mean      0.393085     0.446667\n",
      "std       0.486088     0.058595\n",
      "min       0.000000     0.380000\n",
      "25%       0.090000     0.425000\n",
      "50%       0.240000     0.470000\n",
      "75%       0.490000     0.480000\n",
      "max       3.610000     0.490000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_2005_2007_df['blk'].describe()\n",
    "summary_kobe = kobe_prime_stats_df['blk'].describe()\n",
    "\n",
    "# Concatenate the summaries horizontally\n",
    "summary_df = pd.concat([summary_league, summary_kobe], axis=1)\n",
    "summary_df.columns = ['League', 'Kobe Bryant']\n",
    "\n",
    "# Print the summary DataFrame\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the data Kobe had an average of 0.446 blocks per game during the 2005-2007 NBA seasons. Comparing this to the league average for shooting guards, which was 0.393 blocks per game, we can see that Kobe performed slightly above the average for players in his position. The statistics also indicate that Kobe's blocks per game were relatively consistent, with a minimum of 0.380 blocks and a maximum of 0.490 blocks. Additionally, his blocks per game fell within the interquartile range of 0.425 to 0.480, suggesting a consistent performance compared to other shooting guards."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Effective Field Goal Percentage\n",
    "\n",
    "Effective Field Goal Percentage (eFG%) is a metric that adjusts the traditional field goal percentage to account for the added value of made 3-point field goals. By giving 3-pointers 1.5 times the weight of 2-pointers, eFG% provides a more accurate measure of a player's shooting efficiency. In comparing Kobe Bryant's eFG% to the league average, we can assess his effectiveness as a scorer in terms of maximizing the value of his field goal attempts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kobe_efg_avg = kobe_prime_stats_df.groupby('Season')['eFG%'].mean()\n",
    "league_avg_efg = prime_2005_2007_df['eFG%'].mean()\n",
    "\n",
    "plt.bar(kobe_efg_avg.index, kobe_efg_avg, color='red', label='Kobe Bryant')\n",
    "plt.axhline(league_avg_efg, color='blue', linestyle='--', label='League Average')\n",
    "\n",
    "plt.title('Effective Field Goal Percentage: Kobe Bryant vs League Average')\n",
    "plt.xlabel('Season')\n",
    "plt.ylabel('Effective Field Goal Percentage (eFG%)')\n",
    "\n",
    "plt.xticks([2005, 2006, 2007], ['2005', '2006', '2007'])\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            League  Kobe Bryant\n",
      "count  1287.000000     3.000000\n",
      "mean      0.471028     0.498667\n",
      "std       0.098323     0.006658\n",
      "min       0.000000     0.491000\n",
      "25%       0.440000     0.496500\n",
      "50%       0.480000     0.502000\n",
      "75%       0.515000     0.502500\n",
      "max       1.500000     0.503000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_2005_2007_df['eFG%'].describe()\n",
    "summary_kobe = kobe_prime_stats_df['eFG%'].describe()\n",
    "\n",
    "# Concatenate the summaries horizontally\n",
    "summary_df = pd.concat([summary_league, summary_kobe], axis=1)\n",
    "summary_df.columns = ['League', 'Kobe Bryant']\n",
    "\n",
    "# Print the summary DataFrame\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kobe had an average effective field goal percentage (eFG%) of 0.499, which is higher than the league average of 0.471. This suggests that Kobe was an efficient scorer, as he was able to maximize the value of his field goal attempts by shooting at a higher percentage than the league average. Throughout the three seasons in his prime he consistently scored more efficiently than the league average which is not easy to do and explains further as to why he was such a great offensive weapon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### True Shooting Percentage\n",
    "\n",
    "True Shooting Percentage (TS%) is a comprehensive measure that incorporates the value of three-pointers and free throws, along with two-point field goals, to provide a holistic view of a player's scoring efficiency. By considering the scoring impact of different shot types, TS% offers a more complete assessment of Kobe's ability to maximize his scoring opportunities compared to the league average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kobe_ts_avg = kobe_prime_stats_df.groupby('Season')['TS%'].mean()\n",
    "league_avg_ts = prime_2005_2007_df['TS%'].mean()\n",
    "\n",
    "plt.bar(kobe_ts_avg.index, kobe_ts_avg, color='red', label='Kobe Bryant')\n",
    "plt.axhline(league_avg_ts, color='blue', linestyle='--', label='League Average')\n",
    "\n",
    "plt.title('True Shooting Percentage: Kobe Bryant vs League Average')\n",
    "plt.xlabel('Season')\n",
    "plt.ylabel('True Shooting Percentage (TS%)')\n",
    "\n",
    "plt.xticks([2005, 2006, 2007], ['2005', '2006', '2007'])\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            League  Kobe Bryant\n",
      "count  1287.000000     3.000000\n",
      "mean      0.510106     0.571667\n",
      "std       0.088791     0.011150\n",
      "min       0.000000     0.559000\n",
      "25%       0.480000     0.567500\n",
      "50%       0.521000     0.576000\n",
      "75%       0.556000     0.578000\n",
      "max       1.064000     0.580000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_2005_2007_df['TS%'].describe()\n",
    "summary_kobe = kobe_prime_stats_df['TS%'].describe()\n",
    "\n",
    "# Concatenate the summaries horizontally\n",
    "summary_df = pd.concat([summary_league, summary_kobe], axis=1)\n",
    "summary_df.columns = ['League', 'Kobe Bryant']\n",
    "\n",
    "# Print the summary DataFrame\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kobe's True Shooting Percentage (TS%) of 0.57 exceeds the league average of 0.51. This indicates that Kobe was highly efficient in scoring, as he consistently outperformed the average player (by at least 4%-7%) in terms of maximizing his scoring opportunities across various shot types, including three-pointers, two-pointers, and free throws. This statistic really seperated Kobe from the rest of the league and signifies his true efficiency during those prime years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The King: LeBron James\n",
    "\n",
    "LeBron James, one of the greatest basketball players of all time, has achieved numerous milestones throughout his career. \n",
    "\n",
    "His accomplishments during the viewed period include:\n",
    "\n",
    "- NBA Champion (2012, 2013)\n",
    "- NBA Finals MVP (2012, 2013)\n",
    "- NBA Most Valuable Player (2012, 2013)\n",
    "- All-NBA First Team (2012, 2013)\n",
    "- NBA All-Defensive First Team (2012, 2013)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>DD2</th>\n",
       "      <th>L</th>\n",
       "      <th>PLAYER_NAME</th>\n",
       "      <th>PLUS_MINUS</th>\n",
       "      <th>TD3</th>\n",
       "      <th>W</th>\n",
       "      <th>ast</th>\n",
       "      <th>blk</th>\n",
       "      <th>dreb</th>\n",
       "      <th>...</th>\n",
       "      <th>GS</th>\n",
       "      <th>OBPM</th>\n",
       "      <th>OWS</th>\n",
       "      <th>PER</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Season</th>\n",
       "      <th>TS%</th>\n",
       "      <th>USG%</th>\n",
       "      <th>WS</th>\n",
       "      <th>eFG%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1067</th>\n",
       "      <td>27</td>\n",
       "      <td>23.0</td>\n",
       "      <td>17</td>\n",
       "      <td>LeBron James</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45</td>\n",
       "      <td>6.24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>6.42</td>\n",
       "      <td>...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.7</td>\n",
       "      <td>SF</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.605</td>\n",
       "      <td>32.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>0.554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733</th>\n",
       "      <td>28</td>\n",
       "      <td>36.0</td>\n",
       "      <td>15</td>\n",
       "      <td>LeBron James</td>\n",
       "      <td>9.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>61</td>\n",
       "      <td>7.25</td>\n",
       "      <td>0.88</td>\n",
       "      <td>6.75</td>\n",
       "      <td>...</td>\n",
       "      <td>76.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>14.6</td>\n",
       "      <td>31.6</td>\n",
       "      <td>PF</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.640</td>\n",
       "      <td>30.2</td>\n",
       "      <td>19.3</td>\n",
       "      <td>0.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>29</td>\n",
       "      <td>12.0</td>\n",
       "      <td>25</td>\n",
       "      <td>LeBron James</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52</td>\n",
       "      <td>6.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>5.87</td>\n",
       "      <td>...</td>\n",
       "      <td>77.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>29.3</td>\n",
       "      <td>PF</td>\n",
       "      <td>2013</td>\n",
       "      <td>0.649</td>\n",
       "      <td>31.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>0.610</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      AGE   DD2   L   PLAYER_NAME  PLUS_MINUS  TD3   W   ast   blk  dreb  ...  \\\n",
       "1067   27  23.0  17  LeBron James         7.6  0.0  45  6.24  0.81  6.42  ...   \n",
       "1733   28  36.0  15  LeBron James         9.5  4.0  61  7.25  0.88  6.75  ...   \n",
       "1923   29  12.0  25  LeBron James         5.3  1.0  52  6.34  0.34  5.87  ...   \n",
       "\n",
       "        GS  OBPM   OWS   PER  Pos  Season    TS%  USG%    WS   eFG%  \n",
       "1067  62.0   8.3  10.0  30.7   SF    2011  0.605  32.0  14.5  0.554  \n",
       "1733  76.0   9.2  14.6  31.6   PF    2012  0.640  30.2  19.3  0.603  \n",
       "1923  77.0   8.0  12.3  29.3   PF    2013  0.649  31.0  15.9  0.610  \n",
       "\n",
       "[3 rows x 43 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prime_2011_2013_df = nba_stats_df[nba_stats_df['Season'].isin([2011, 2012, 2013])]\n",
    "lebron_prime_stats_df = prime_2011_2013_df[prime_2011_2013_df['PLAYER_NAME'] == 'LeBron James']\n",
    "lebron_prime_stats_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "We analyze the points of LeBron James:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(prime_2011_2013_df['pts'], vert=False)\n",
    "lebron_points = lebron_prime_stats_df['pts']\n",
    "for point in lebron_points:\n",
    "    plt.plot(point, 1, 'ro')\n",
    "\n",
    "plt.title('Boxplot of Points in the 2011-2013 NBA Seasons')\n",
    "plt.xlabel('Points')\n",
    "plt.ylabel('Season')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            League  LeBron James\n",
      "count  1307.000000      3.000000\n",
      "mean      7.947728     27.023333\n",
      "std       5.621730      0.202320\n",
      "min       0.000000     26.790000\n",
      "25%       3.500000     26.960000\n",
      "50%       6.580000     27.130000\n",
      "75%      11.270000     27.140000\n",
      "max      32.010000     27.150000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_2011_2013_df['pts'].describe()\n",
    "summary_lebron = lebron_prime_stats_df['pts'].describe()\n",
    "summary_df = pd.concat([summary_league, summary_lebron], axis=1)\n",
    "summary_df.columns = ['League', 'LeBron James']\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the average points per game for LeBron James are significantly higher (27.02) compared to the league average (7.95). In the box plot, LeBron James is seen as an outlier (represented in red), highlighting his exceptional scoring ability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assists\n",
    "\n",
    "Let's now take a look at LeBron James' assist statistics. LeBron James is not only a prolific scorer but also an exceptional playmaker. His ability to distribute the ball and create scoring opportunities for his teammates is a key aspect of his game."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(prime_2011_2013_df['ast'], vert=False)\n",
    "lebron_assists = lebron_prime_stats_df['ast']\n",
    "\n",
    "for assist in lebron_assists:\n",
    "    plt.plot(assist, 1, 'ro')\n",
    "\n",
    "plt.title('Boxplot of Assists in the 2011-2013 NBA Seasons')\n",
    "plt.xlabel('Assists')\n",
    "plt.ylabel('Season')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            League  LeBron James\n",
      "count  1307.000000      3.000000\n",
      "mean      1.761270      6.610000\n",
      "std       1.793505      0.556507\n",
      "min       0.000000      6.240000\n",
      "25%       0.540000      6.290000\n",
      "50%       1.120000      6.340000\n",
      "75%       2.335000      6.795000\n",
      "max      11.700000      7.250000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_2011_2013_df['ast'].describe()\n",
    "summary_lebron = lebron_prime_stats_df['ast'].describe()\n",
    "summary_df = pd.concat([summary_league, summary_lebron], axis=1)\n",
    "summary_df.columns = ['League', 'LeBron James']\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to his points, LeBron James also ranks among the top players in terms of assists, with his average being three times higher than the league average during the seasons considered."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rebounds\n",
    "\n",
    "Now let's examine whether LeBron James' large stature of 2.06m helps him secure more rebounds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='whitegrid')\n",
    "sf_df = prime_2011_2013_df[prime_2011_2013_df['Pos'] == 'SF']\n",
    "\n",
    "league_avg_rebounds_2011 = prime_2011_2013_df[prime_2011_2013_df['Season'] == 2011]['reb'].mean()\n",
    "league_avg_rebounds_2012 = prime_2011_2013_df[prime_2011_2013_df['Season'] == 2012]['reb'].mean()\n",
    "league_avg_rebounds_2013 = prime_2011_2013_df[prime_2011_2013_df['Season'] == 2013]['reb'].mean()\n",
    "\n",
    "plt.plot([2011, 2012, 2013], [league_avg_rebounds_2011, league_avg_rebounds_2012, league_avg_rebounds_2013],\n",
    "         color='blue', marker='o', linestyle='--', label='League Average (SF)')\n",
    "\n",
    "lebron_rebounds = lebron_prime_stats_df['reb']\n",
    "seasons = lebron_rebounds.index\n",
    "\n",
    "plt.plot([2011, 2012, 2013], lebron_rebounds, color='red', marker='o', label='LeBron James (SF)')\n",
    "plt.title('Rebounds in the 2011-2013 NBA Seasons (Small Forward)', fontweight='bold')\n",
    "plt.xlabel('Season', fontweight='bold')\n",
    "plt.ylabel('Rebounds', fontweight='bold')\n",
    "plt.legend()\n",
    "plt.xticks([2011, 2012, 2013], ['2011', '2012', '2013'])\n",
    "sns.despine(trim=True, offset=5)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            League  LeBron James\n",
      "count  1307.000000      3.000000\n",
      "mean      3.540099      7.630000\n",
      "std       2.429330      0.616523\n",
      "min       0.000000      6.920000\n",
      "25%       1.830000      7.430000\n",
      "50%       2.930000      7.940000\n",
      "75%       4.700000      7.985000\n",
      "max      14.540000      8.030000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_2011_2013_df['reb'].describe()\n",
    "summary_lebron = lebron_prime_stats_df['reb'].describe()\n",
    "summary_df = pd.concat([summary_league, summary_lebron], axis=1)\n",
    "summary_df.columns = ['League', 'LeBron James']\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that LeBron James is far superior to his colleagues in the league. However, the graph shows that LeBron James' rebounding numbers declined from the 2012 season to the 2013 season."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Steals\n",
    "\n",
    "Until now, the Small Forward has consistently excelled in the statistics. Will this also be the case for \"steals\"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "league_steals_2011_2013 = prime_2011_2013_df.groupby('PLAYER_NAME')['stl'].mean()\n",
    "\n",
    "sorted_steals = league_steals_2011_2013.sort_values(ascending=False)\n",
    "\n",
    "lebron_position = sorted_steals.index.get_loc('LeBron James')\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(range(len(sorted_steals)), sorted_steals, color='blue')\n",
    "plt.axhline(y=sorted_steals.iloc[lebron_position], color='red', linestyle='--', label='LeBron James')\n",
    "\n",
    "plt.title('Steals in the 2011-2013 NBA Seasons')\n",
    "plt.xlabel('Player')\n",
    "plt.ylabel('Steals per Game')\n",
    "plt.text(lebron_position, sorted_steals.iloc[lebron_position], 'LeBron James', ha='center', va='bottom')\n",
    "plt.xticks([])\n",
    "plt.tight_layout()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            League  LeBron James\n",
      "count  1307.000000      3.000000\n",
      "mean      0.643168      1.706667\n",
      "std       0.429658      0.140119\n",
      "min       0.000000      1.570000\n",
      "25%       0.330000      1.635000\n",
      "50%       0.570000      1.700000\n",
      "75%       0.880000      1.775000\n",
      "max       2.530000      1.850000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_2011_2013_df['stl'].describe()\n",
    "summary_lebron = lebron_prime_stats_df['stl'].describe()\n",
    "summary_df = pd.concat([summary_league, summary_lebron], axis=1)\n",
    "summary_df.columns = ['League', 'LeBron James']\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that LeBron James has reached the maximum value among players within his league during the seasons considered in terms of steals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sf_df = prime_2011_2013_df[prime_2011_2013_df['Pos'] == 'SF']\n",
    "sf_blocks = sf_df.groupby('PLAYER_NAME')['blk'].mean()\n",
    "sorted_blocks = sf_blocks.sort_values(ascending=False)\n",
    "lebron_position = sorted_blocks.index.get_loc('LeBron James')\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(range(len(sorted_blocks)), sorted_blocks, color='blue')\n",
    "plt.axhline(y=sorted_blocks.iloc[lebron_position], color='red', linestyle='--', label='LeBron James')\n",
    "\n",
    "plt.title('Blocks per Game - SF')\n",
    "plt.xlabel('Player')\n",
    "plt.ylabel('Blocks per Game')\n",
    "\n",
    "plt.text(lebron_position, sorted_blocks.iloc[lebron_position], 'LeBron James', ha='center', va='bottom')\n",
    "plt.xticks([])\n",
    "plt.tight_layout()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            League  LeBron James\n",
      "count  1307.000000      3.000000\n",
      "mean      0.416442      0.676667\n",
      "std       0.454066      0.293655\n",
      "min       0.000000      0.340000\n",
      "25%       0.110000      0.575000\n",
      "50%       0.270000      0.810000\n",
      "75%       0.560000      0.845000\n",
      "max       3.650000      0.880000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_2011_2013_df['blk'].describe()\n",
    "summary_lebron = lebron_prime_stats_df['blk'].describe()\n",
    "summary_df = pd.concat([summary_league, summary_lebron], axis=1)\n",
    "summary_df.columns = ['League', 'LeBron James']\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the graph, it is evident that LeBron James, playing in the small forward position, records a higher number of blocks compared to 75% of the players in the league who also play in the same position."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Effective Field Goal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lebron_efg_avg = lebron_prime_stats_df.groupby('Season')['eFG%'].mean()\n",
    "league_avg_efg = prime_2011_2013_df['eFG%'].mean()\n",
    "plt.bar(lebron_efg_avg.index, lebron_efg_avg, color='red', label='LeBron James')\n",
    "plt.axhline(league_avg_efg, color='blue', linestyle='--', label='League Average')\n",
    "plt.title('Effective Field Goals Percentage LeBron James compared to League Average')\n",
    "plt.xlabel('Season')\n",
    "plt.ylabel('Effective Field Goals (eFG%)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            League  LeBron James\n",
      "count  1307.000000      3.000000\n",
      "mean      0.471933      0.589000\n",
      "std       0.099201      0.030512\n",
      "min       0.000000      0.554000\n",
      "25%       0.444000      0.578500\n",
      "50%       0.482000      0.603000\n",
      "75%       0.519000      0.606500\n",
      "max       1.000000      0.610000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_2011_2013_df['eFG%'].describe()\n",
    "summary_lebron = lebron_prime_stats_df['eFG%'].describe()\n",
    "summary_df = pd.concat([summary_league, summary_lebron], axis=1)\n",
    "summary_df.columns = ['League', 'LeBron James']\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the bar chart, it is evident that LeBron James had a higher effective field goal percentage (eFG%) than the league average in all three years of his prime. Additionally, it is noticeable that LeBron James improved his shooting efficiency each year, surpassing his previous year's average while still maintaining a higher eFG% than the league average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lebron_ts_avg = lebron_prime_stats_df.groupby('Season')['TS%'].mean()\n",
    "league_avg_ts = prime_2011_2013_df['TS%'].mean()\n",
    "plt.bar(lebron_ts_avg.index, lebron_ts_avg, color='red', label='LeBron James')\n",
    "plt.axhline(league_avg_ts, color='blue', linestyle='--', label='League Average')\n",
    "plt.title('True Shooting Percentage LeBron James compared to League Average')\n",
    "plt.xlabel('Season')\n",
    "plt.ylabel('True Shooting Percentage (TS%)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            League  LeBron James\n",
      "count  1307.000000      3.000000\n",
      "mean      0.504979      0.631333\n",
      "std       0.096533      0.023245\n",
      "min       0.000000      0.605000\n",
      "25%       0.477000      0.622500\n",
      "50%       0.518000      0.640000\n",
      "75%       0.555000      0.644500\n",
      "max       1.000000      0.649000\n"
     ]
    }
   ],
   "source": [
    "summary_league = prime_2011_2013_df['TS%'].describe()\n",
    "summary_lebron = lebron_prime_stats_df['TS%'].describe()\n",
    "\n",
    "# Concatenate the summaries horizontally\n",
    "summary_df = pd.concat([summary_league, summary_lebron], axis=1)\n",
    "summary_df.columns = ['League', 'LeBron James']\n",
    "\n",
    "# Print the summary DataFrame\n",
    "print(summary_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the bar chart, it is noticeable that LeBron James had a higher shooting percentage than the league average in all three years of his prime. This indicates his exceptional efficiency in scoring compared to the average performance of other players in the league."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison of Overall Impact\n",
    "\n",
    "Now that we have taken a look at the players' individual statistics it comes down to the analysis of their overall impact to really determine who is the greatest of them all statistically. To help us with this we look at these key categories, which measure to a certain extent how much the three players in focus made their team better:\n",
    "\n",
    "- Team Wins (W)\n",
    "- Defensive Win Shares (DWS)\n",
    "- Offensive Win Shares (OWS)\n",
    "- Win Shares (WS)\n",
    "- Player Efficiency Rating (PER) and Plus-Minus (+/-)\n",
    "\n",
    "By looking at these numbers we will get a clearer picture and decide once and for all who was the best at their best!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "players = ['Michael Jordan', 'Kobe Bryant', 'LeBron James']\n",
    "\n",
    "mj_wins = mj_prime_stats_df['W'].sum()\n",
    "mj_games_played = mj_prime_stats_df['games_played'].sum()\n",
    "mj_ratio = (mj_wins / mj_games_played) * 100\n",
    "\n",
    "kobe_wins = kobe_prime_stats_df['W'].sum()\n",
    "kobe_games_played = kobe_prime_stats_df['games_played'].sum()\n",
    "kobe_ratio = (kobe_wins / kobe_games_played) * 100\n",
    "\n",
    "\n",
    "lebron_wins = lebron_prime_stats_df['W'].sum()\n",
    "lebron_games_played = lebron_prime_stats_df['games_played'].sum()\n",
    "lebron_ratio = (lebron_wins / lebron_games_played) * 100\n",
    "\n",
    "ratios = [mj_ratio, kobe_ratio, lebron_ratio]\n",
    "players_sorted, ratios_sorted = zip(*sorted(zip(players, ratios), key=lambda x: x[1], reverse=True))\n",
    "plt.barh(players_sorted, ratios_sorted, color=['red', 'purple', 'blue'])\n",
    "\n",
    "for i, (player, ratio) in enumerate(zip(players_sorted, ratios_sorted)):\n",
    "    plt.text(ratio, i, f'{ratio:.2f}%', ha='left', va='center')\n",
    "\n",
    "plt.title('Win-to-Games-Played Ratio Comparison')\n",
    "plt.xlabel('Win-to-Games-Played Ratio (%)')\n",
    "plt.ylabel('Player')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Michael Jordan has the highest win ratio among the three players. During his prime seasons, he achieved an astonishing win ratio of 79.88%, indicating his exceptional success on the court. LeBron James follows in second place with a win ratio of 73.49%, also displaying a significant level of success. On the other hand, Kobe Bryant has the lowest win ratio among the three players, with a rate of 59%, indicating a relatively lower level of success in terms of winning games during his prime seasons compared to the other two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "players = ['Michael Jordan', 'Kobe Bryant', 'LeBron James']\n",
    "data_frames = [mj_prime_stats_df, kobe_prime_stats_df, lebron_prime_stats_df]\n",
    "dws_values = []\n",
    "for df in data_frames:\n",
    "    dws_values.append(df['DWS'].mean())\n",
    "x_pos = numpy.arange(len(players))\n",
    "plt.bar(x_pos, dws_values, color=['red', 'purple', 'blue'], align='center')\n",
    "plt.xticks(x_pos, players)\n",
    "for i, dws in enumerate(dws_values):\n",
    "    plt.text(i, dws, str(dws), ha='center', va='bottom')\n",
    "\n",
    "plt.title('Defensive Win Shares (DWS) Comparison')\n",
    "plt.xlabel('Player')\n",
    "plt.ylabel('DWS')\n",
    "\n",
    "# Show the chart\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Michael Jordan has the highest defensive win shares among the players, with a value of 5.2. This indicates his exceptional defensive skills and contributions to his team's success. Following Jordan, LeBron James secures the second place with a defensive win share of 4.3, demonstrating his significant impact on the defensive end of the game. Kobe Bryant has the lowest defensive win share among the players, with a value of 3.4, suggesting a relatively lesser defensive contribution compared to the other players."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Offensive Win Shares (OWS)\n",
    "\n",
    "We will now examine the Offensive Win Shares (OWS) of the three players to gain insight into their offensive contributions and impact on team success."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Player data for Offensive Win Shares (OWS)\n",
    "mj_ows = mj_prime_stats_df['OWS'].mean()\n",
    "kobe_ows = kobe_prime_stats_df['OWS'].mean()\n",
    "lebron_ows = lebron_prime_stats_df['OWS'].mean()\n",
    "\n",
    "# List of players and their OWS values\n",
    "players = ['Michael Jordan', 'Kobe Bryant',  'LeBron James']\n",
    "ows_values = [mj_ows, kobe_ows, lebron_ows]\n",
    "\n",
    "# Create the bar chart\n",
    "plt.bar(players, ows_values, color=['red', 'purple', 'blue'])\n",
    "\n",
    "# Set the title and axis labels\n",
    "plt.title('Offensive Win Shares (OWS)')\n",
    "plt.xlabel('Player')\n",
    "plt.ylabel('OWS')\n",
    "for i, value in enumerate(ows_values):\n",
    "    plt.text(i, value, str(round(value, 2)), ha='center', va='bottom')\n",
    "\n",
    "# Show the chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that it is a very close competition. However, LeBron James has achieved the highest result in terms of the mean value of Offensive Win Shares."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Win Shares\n",
    "\n",
    "Now that we have examined the Offensive Win Shares (OWS), let's also delve into the overall Win Shares (WS) to gain a comprehensive understanding of the impact and contribution of each player to their team's success."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Player data for Win Shares (WS)\n",
    "mj_ws = mj_prime_stats_df['WS'].mean()\n",
    "kobe_ws = kobe_prime_stats_df['WS'].mean()\n",
    "lebron_ws = lebron_prime_stats_df['WS'].mean()\n",
    "\n",
    "# List of players and their OWS values\n",
    "players = ['Michael Jordan', 'Kobe Bryant', 'LeBron James']\n",
    "ws_values = [mj_ws, kobe_ws, lebron_ws]\n",
    "\n",
    "# Create the bar chart\n",
    "plt.bar(players, ws_values, color=['red', 'purple', 'blue'])\n",
    "\n",
    "# Set the title and axis labels\n",
    "plt.title(' Win Shares (WS)')\n",
    "plt.xlabel('Player')\n",
    "plt.ylabel('WS')\n",
    "for i, value in enumerate(ws_values):\n",
    "    plt.text(i, value, str(round(value, 2)), ha='center', va='bottom')\n",
    "\n",
    "# Show the chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In terms of Win Shares, Michael Jordan is the winner (17.05), closely followed by LeBron James (16.57)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Player Efficiency Rating and Plus-Minus\n",
    "\n",
    "These two statistics are defined as:\n",
    "\n",
    "- Player Efficiency Rating (PER): The Player Efficiency Rating (PER) is a per-minute rating developed by ESPN.com columnist John Hollinger. In John's words, \"The PER sums up all a player's positive accomplishments, subtracts the negative accomplishments, and returns a per-minute rating of a player's performance.\"\n",
    "- Plus Minus (+/-): The point differential when a player or team is on the floor\n",
    "\n",
    "Sources: [Link](https://www.basketball-reference.com/about/per.html) & [Link](https://www.nba.com/stats/help/glossary)\n",
    "\n",
    "By examining the combined Player Efficiency Ratings (PER) and Plus-Minus (+/-) statistics of Michael Jordan, Kobe Bryant, and LeBron James during their prime years, we can gain valuable insights into their overall impact and effectiveness on the basketball court. These metrics provide a comprehensive measure of their individual contributions to team performance and success."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(mj_prime_stats_df['PLUS_MINUS'], mj_prime_stats_df['PER'], color='red', label='Michael Jordan')\n",
    "plt.scatter(kobe_prime_stats_df['PLUS_MINUS'], kobe_prime_stats_df['PER'], color='purple', label='Kobe Bryant')\n",
    "plt.scatter(lebron_prime_stats_df['PLUS_MINUS'], lebron_prime_stats_df['PER'], color='blue', label='LeBron James')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title('Plus-Minus (+/-) and Player Efficiency Rating (PER)')\n",
    "plt.xlabel('Plus-Minus (+/-)')\n",
    "plt.ylabel('Player Efficiency Rating (PER)')\n",
    "\n",
    "# Add a smaller legend\n",
    "plt.legend(fontsize='small')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When looking at the combined correlation of these two numbers we see that LeBron seems to completely outshine Michael Jordan and Kobe Bryant, when it comes to overall positive impact on the court. The pattern indicates following ranking based purely on these two statistical metrics of their prime years:\n",
    "\n",
    "1. LeBron James\n",
    "2. Michael Jordan\n",
    "3. Kobe Bryant\n",
    "\n",
    "We should note though that the season which is missing from Michaels prime years is the 1995-1996 season which is the historic 72-10 Chicago Bulls season, which Michael was the absolute center piece of. There is potentially some more insight that can be gained from that data, but overall this graphic seems to paint a decisive picture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "spark.stop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Key Take-Aways and Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To summarise the results of our analysis we created this table containing all the individual stats, overall impact stats and the respective achievements:\n",
    "\n",
    "|                              | Michael Jordan | Kobe Bryant | LeBron James |\n",
    "|------------------------------|----------------|-------------|--------------|\n",
    "| Seasons                      | 1995-1997      | 2005-2007   | 2011-2013    |\n",
    "|                              |                |             |              |\n",
    "| **Individual Stats**         |                |             |              |\n",
    "| PTS                          | 29.2           | 31.76       | 27.02        |\n",
    "| AST                          | 3.87           | 5.08        | 6.61         |\n",
    "| REB                          | 5.84           | 5.77        | 7.63         |\n",
    "| STL                          | 1.72           | 1.71        | 1.71         |\n",
    "| BLK                          | 0.55           | 0.44        | 0.68         |\n",
    "| eFG%                         | 0.49           | 0.49        | 0.59         |\n",
    "| TS%                          | 0.55           | 0.57        | 0.63         |\n",
    "|                              |                |             |              |\n",
    "| **Overall Impact Stats**     |                |             |              |\n",
    "| W                            | 65.5           | 47          | 52.67        |\n",
    "| DWS                          | 5.2            | 3.4         | 4.3          |\n",
    "| OWS                          | 11.85          | 10.63       | 12.3         |\n",
    "| WS                           | 17.04          | 14.03       | 16.57        |\n",
    "| PER                          | 26.5           | 26.1        | 30.53        |\n",
    "| +/-                          | 8.65           | 4.2         | 7.47         |\n",
    "|                              |                |             |              |\n",
    "| **Key Achievements**         |                |             |              |\n",
    "| MVP                          | 2              | 0           | 2            |\n",
    "| All-NBA First Team           | 3              | 3           | 3            |\n",
    "| Scoring Champion             | 3              | 2           | 0            |\n",
    "| NBA Champion                 | 3              | 0           | 2            |\n",
    "| NBA Finals MVP               | 3              | 0           | 2            |\n",
    "| NBA All-Defensive First Team | 3              | 3           | 3            |\n",
    "\n",
    "**Note: The statistics for Michael Jordan only contain two of the three seasons in which he was arguably in his prime, since we did not have official data from the nba.com website, because the NBA started tracking certain stats starting from the 1996-1997 season. The achievements list is complete though.**"
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    "### Key Take-Aways\n",
    "\n",
    "**Key Takeaways - Michael Jordan:**\n",
    "\n",
    "- Michael Jordan showcased exceptional scoring ability and was a dominant force in the NBA during his prime years from 1995 to 1997. Here are the key takeaways from his stats and achievements:\n",
    "  - **Scoring Prowess**: Jordan's scoring average of 29.2 points per game reflects his ability to consistently put up high numbers and lead his team offensively.\n",
    "  - **All-Around Contributions**: With solid rebounding (5.84 RPG), playmaking (3.87 APG), and steals (1.72 SPG), Jordan displayed his versatility and impact beyond scoring.\n",
    "  - **Team Success**: His three NBA championships, three NBA Finals MVP awards, and inclusion in the All-NBA First Team for three seasons highlight his instrumental role in leading the Chicago Bulls to success.\n",
    "\n",
    "**Key Takeaways - Kobe Bryant:**\n",
    "\n",
    "- Kobe Bryant was known for his competitive spirit and incredible scoring ability during his prime from 2005 to 2007. Here are the key takeaways from his stats and achievements:\n",
    "  - **Scoring Machine**: Bryant's scoring average of 31.76 points per game showcases his ability to consistently put up high numbers and carry his team's offense.\n",
    "  - **Efficiency and Versatility**: Despite his high scoring load, Bryant maintained a respectable field goal percentage and made contributions in other areas such as assists (5.08 APG) and rebounds (5.77 RPG).\n",
    "  - **Individual Accolades**: While his achievements in terms of championships and MVP awards may be less compared to others, his inclusion in the All-NBA First Team for three seasons and his scoring titles signify his individual excellence.\n",
    "\n",
    "**Key Takeaways - LeBron James:**\n",
    "\n",
    "- LeBron James is widely regarded as one of the most versatile and impactful players in NBA history. Here are the key takeaways from his stats and achievements:\n",
    "  - **All-Around Dominance**: James consistently demonstrates his all-around skills, reflected in his well-rounded stat line featuring scoring (27.02 PPG), playmaking (6.61 APG), rebounding (7.63 RPG), and solid defensive contributions (1.71 SPG, 0.68 BPG).\n",
    "  - **Efficiency and Shooting**: James exhibits high shooting efficiency with an effective field goal percentage of 0.59 and true shooting percentage of 0.63, showcasing his ability to efficiently contribute on the offensive end.\n",
    "  - **Consistent Success**: His two NBA championships, inclusion in the All-NBA First Team for three seasons, and numerous MVP awards highlight his exceptional performance and impact on both individual and team levels."
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    "### Conclusion\n",
    "\n",
    "In conclusion, our project aimed to determine the best basketball player at their best by analyzing the statistics and achievements of Michael Jordan, Kobe Bryant, and LeBron James during their prime years. By utilizing data science techniques and focusing on objective metrics, we have gained valuable insights into their player profiles. \n",
    "\n",
    "Based on the results and key takeaways from our analysis, we can make a case for the best player at their best:\n",
    "\n",
    "While all three players have remarkable achievements and contributions to the game, LeBron James stands out as the best player at their best. Here's why:\n",
    "\n",
    "1. **All-Around Dominance**: LeBron James showcases exceptional skills in multiple facets of the game. His well-rounded stat line, including scoring, playmaking, rebounding, and defensive contributions, demonstrates his versatility and impact across all aspects of basketball.\n",
    "\n",
    "2. **Consistency and Efficiency**: James consistently performs at a high level, maintaining impressive shooting efficiency with an effective field goal percentage of 0.59 and true shooting percentage of 0.63. His ability to efficiently contribute on the offensive end sets him apart from the competition.\n",
    "\n",
    "3. **Championship Success**: While championships are not the sole determinant of individual greatness, they are a significant factor. LeBron James has won two NBA championships during the viewed period and has been a key contributor to his teams' success throughout his career, further solidifying his claim as the best player at their best.\n",
    "\n",
    "By examining the statistical performance and achievements of Michael Jordan, Kobe Bryant, and LeBron James, it is evident that LeBron James emerges as the best player at their best. However, it is important to acknowledge that basketball is a team sport, and individual greatness cannot solely define a player's overall impact and legacy.\n",
    "\n",
    "Throughout this project, we utilized data science techniques, leveraged multiple data sources, and focused on standardized tools to ensure a comprehensive and unbiased analysis. By combining the principles of Big Data Infrastructure and Big Data Engineering, we were able to derive meaningful insights and make data-driven comparisons.\n",
    "\n",
    "In conclusion, the debate over the best basketball player will continue, and individual preferences may differ. However, through this project, we have provided a data-driven perspective that supports LeBron James as the best player at their best based on his statistical performance, all-around dominance, efficiency, and championship success."
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