diff --git a/exercicios/para-casa/Readmeexercicio.md b/exercicios/para-casa/Readmeexercicio.md
new file mode 100644
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+++ b/exercicios/para-casa/Readmeexercicio.md
@@ -0,0 +1,26 @@
+Usei o dataset proposto em aula[Kaggle](https://www.kaggle.com/) para buscar os dados sobre Estatísticas dos jogadores da NBA 23/24, escolhi esse tema, pois sou ex-atleta e apaixonada por esse esporte.
+
+Comecei fazendo os import das bibliotecas que iria utilizar que são: 
+import pandas as pd
+import matplotlib.pyplot as plt
+from scipy.stats import ttest_ind
+import seaborn as sns
+import sqlite3
+import scipy.stats as stats
+ 
+Leitura do arquivo em CSV
+
+Alguns teste mostrandos os dados das tabelas como linhas e colunas lunas e exclusão de linhas duplicadas
+
+Renomeie os nomes das colunas principais, os demais dados eram sobre estatistica de jogo, não fiz o uso deles, então só renomeie as que realmente iriam ser utilizadas. 
+
+Foram feitos 4 tipos de graficos, mostrando: 
+Quantos jogadores são Point Guard (PG): Armador?
+Idades dos jogadores
+Qual a taxa dos jogadores com 25 anos? 
+Amostra do melhor e pior Time que não retornou valor (amostra com pouco dado).
+
+Feita consulta em SQL e Pandas para mostrar a contagens de jogadores por Times. 
+Fiz o fork do repositório.
+Clonei o fork na minha máquina (`git clone url-do-meu-fork`).
+Resolvi o exercício.
\ No newline at end of file
diff --git a/exercicios/para-casa/exercicio.ipynb b/exercicios/para-casa/exercicio.ipynb
new file mode 100644
index 0000000..1218987
--- /dev/null
+++ b/exercicios/para-casa/exercicio.ipynb
@@ -0,0 +1,1056 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importar bibliotecas\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats import ttest_ind\n",
+    "import seaborn as sns\n",
+    "import sqlite3\n",
+    "import scipy.stats as stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#leitura do arquivo CSV\n",
+    "df = pd.read_csv(\"nba-player-data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Rk</th>\n",
+       "      <th>Player</th>\n",
+       "      <th>Pos</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Tm</th>\n",
+       "      <th>G</th>\n",
+       "      <th>GS</th>\n",
+       "      <th>MP</th>\n",
+       "      <th>FG</th>\n",
+       "      <th>FGA</th>\n",
+       "      <th>...</th>\n",
+       "      <th>FT%</th>\n",
+       "      <th>ORB</th>\n",
+       "      <th>DRB</th>\n",
+       "      <th>TRB</th>\n",
+       "      <th>AST</th>\n",
+       "      <th>STL</th>\n",
+       "      <th>BLK</th>\n",
+       "      <th>TOV</th>\n",
+       "      <th>PF</th>\n",
+       "      <th>PTS</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Precious Achiuwa</td>\n",
+       "      <td>PF-C</td>\n",
+       "      <td>24</td>\n",
+       "      <td>TOT</td>\n",
+       "      <td>74</td>\n",
+       "      <td>18</td>\n",
+       "      <td>21.9</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>.616</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>7.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Precious Achiuwa</td>\n",
+       "      <td>C</td>\n",
+       "      <td>24</td>\n",
+       "      <td>TOR</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17.5</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>.571</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>7.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Precious Achiuwa</td>\n",
+       "      <td>PF</td>\n",
+       "      <td>24</td>\n",
+       "      <td>NYK</td>\n",
+       "      <td>49</td>\n",
+       "      <td>18</td>\n",
+       "      <td>24.2</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>.643</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>7.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Bam Adebayo</td>\n",
+       "      <td>C</td>\n",
+       "      <td>26</td>\n",
+       "      <td>MIA</td>\n",
+       "      <td>71</td>\n",
+       "      <td>71</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>14.3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>.755</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>19.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Ochai Agbaji</td>\n",
+       "      <td>SG</td>\n",
+       "      <td>23</td>\n",
+       "      <td>TOT</td>\n",
+       "      <td>78</td>\n",
+       "      <td>28</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>.661</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Ochai Agbaji</td>\n",
+       "      <td>SG</td>\n",
+       "      <td>23</td>\n",
+       "      <td>UTA</td>\n",
+       "      <td>51</td>\n",
+       "      <td>10</td>\n",
+       "      <td>19.7</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>...</td>\n",
+       "      <td>.750</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>5.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Ochai Agbaji</td>\n",
+       "      <td>SG</td>\n",
+       "      <td>23</td>\n",
+       "      <td>TOR</td>\n",
+       "      <td>27</td>\n",
+       "      <td>18</td>\n",
+       "      <td>23.6</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>.611</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>6.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Santi Aldama</td>\n",
+       "      <td>PF</td>\n",
+       "      <td>23</td>\n",
+       "      <td>MEM</td>\n",
+       "      <td>61</td>\n",
+       "      <td>35</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>.621</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>10.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Nickeil Alexander-Walker</td>\n",
+       "      <td>SG</td>\n",
+       "      <td>25</td>\n",
+       "      <td>MIN</td>\n",
+       "      <td>82</td>\n",
+       "      <td>20</td>\n",
+       "      <td>23.4</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>.800</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>6</td>\n",
+       "      <td>Grayson Allen</td>\n",
+       "      <td>SG</td>\n",
+       "      <td>28</td>\n",
+       "      <td>PHO</td>\n",
+       "      <td>75</td>\n",
+       "      <td>74</td>\n",
+       "      <td>33.5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>.878</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>13.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Rk                    Player   Pos Age   Tm   G  GS    MP   FG   FGA  ...  \\\n",
+       "0  1          Precious Achiuwa  PF-C  24  TOT  74  18  21.9  3.2   6.3  ...   \n",
+       "1  1          Precious Achiuwa     C  24  TOR  25   0  17.5  3.1   6.8  ...   \n",
+       "2  1          Precious Achiuwa    PF  24  NYK  49  18  24.2  3.2   6.1  ...   \n",
+       "3  2               Bam Adebayo     C  26  MIA  71  71  34.0  7.5  14.3  ...   \n",
+       "4  3              Ochai Agbaji    SG  23  TOT  78  28  21.0  2.3   5.6  ...   \n",
+       "5  3              Ochai Agbaji    SG  23  UTA  51  10  19.7  2.1   4.9  ...   \n",
+       "6  3              Ochai Agbaji    SG  23  TOR  27  18  23.6  2.7   6.8  ...   \n",
+       "7  4              Santi Aldama    PF  23  MEM  61  35  26.5  4.0   9.3  ...   \n",
+       "8  5  Nickeil Alexander-Walker    SG  25  MIN  82  20  23.4  2.9   6.6  ...   \n",
+       "9  6             Grayson Allen    SG  28  PHO  75  74  33.5  4.5   9.1  ...   \n",
+       "\n",
+       "    FT%  ORB  DRB   TRB  AST  STL  BLK  TOV   PF   PTS  \n",
+       "0  .616  2.6  4.0   6.6  1.3  0.6  0.9  1.1  1.9   7.6  \n",
+       "1  .571  2.0  3.4   5.4  1.8  0.6  0.5  1.2  1.6   7.7  \n",
+       "2  .643  2.9  4.3   7.2  1.1  0.6  1.1  1.1  2.1   7.6  \n",
+       "3  .755  2.2  8.1  10.4  3.9  1.1  0.9  2.3  2.2  19.3  \n",
+       "4  .661  0.9  1.8   2.8  1.1  0.6  0.6  0.8  1.5   5.8  \n",
+       "5  .750  0.7  1.8   2.5  0.9  0.5  0.6  0.7  1.3   5.4  \n",
+       "6  .611  1.4  1.9   3.3  1.3  0.7  0.6  1.1  1.9   6.7  \n",
+       "7  .621  1.2  4.6   5.8  2.3  0.7  0.9  1.1  1.5  10.7  \n",
+       "8  .800  0.4  1.6   2.0  2.5  0.8  0.5  0.9  1.7   8.0  \n",
+       "9  .878  0.6  3.3   3.9  3.0  0.9  0.6  1.3  2.1  13.5  \n",
+       "\n",
+       "[10 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(736, 30)"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#quantidades de linhas e colunas\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_backup = df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rk         0\n",
+      "Player     0\n",
+      "Pos        0\n",
+      "Age        0\n",
+      "Tm         0\n",
+      "G          0\n",
+      "GS         0\n",
+      "MP         0\n",
+      "FG         0\n",
+      "FGA        0\n",
+      "FG%        8\n",
+      "3P         0\n",
+      "3PA        0\n",
+      "3P%       46\n",
+      "2P         0\n",
+      "2PA        0\n",
+      "2P%       13\n",
+      "eFG%       8\n",
+      "FT         0\n",
+      "FTA        0\n",
+      "FT%       59\n",
+      "ORB        0\n",
+      "DRB        0\n",
+      "TRB        0\n",
+      "AST        0\n",
+      "STL        0\n",
+      "BLK        0\n",
+      "TOV        0\n",
+      "PF         0\n",
+      "PTS        0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#mostrando os nulos em cada coluna \n",
+    "nulos_por_coluna = df.isnull().sum()\n",
+    "print(nulos_por_coluna)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0      0\n",
+      "1      0\n",
+      "2      0\n",
+      "3      0\n",
+      "4      0\n",
+      "      ..\n",
+      "731    0\n",
+      "732    0\n",
+      "733    0\n",
+      "734    0\n",
+      "735    1\n",
+      "Length: 736, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#mostrnado os nulos por linhas\n",
+    "nulos_por_linhas = df.isnull().sum(axis=1)\n",
+    "print(nulos_por_linhas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#remover linhas duplicadas\n",
+    "df = df.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Rk', 'Player', 'Pos', 'Age', 'Tm', 'G', 'GS', 'MP', 'FG', 'FGA', 'FG%',\n",
+       "       '3P', '3PA', '3P%', '2P', '2PA', '2P%', 'eFG%', 'FT', 'FTA', 'FT%',\n",
+       "       'ORB', 'DRB', 'TRB', 'AST', 'STL', 'BLK', 'TOV', 'PF', 'PTS'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#renomear as colunas para PT\n",
+    "df.rename(columns = {\n",
+    "    'Rk': 'Valor', \n",
+    "    'Player':'Jogador',\n",
+    "    'Pos': 'Posicao',\n",
+    "    'Age': 'Idade',\n",
+    "    'Tm' : 'Times',\n",
+    "    'G' : 'Jogo'\n",
+    "\n",
+    "}, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#salve no csv novo arquivo \n",
+    "df.to_csv('nba-player-data-novo.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#conversão da tabela jogador para numerica\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "le = LabelEncoder()\n",
+    "df['ID_Jogador'] = le.fit_transform(df['Jogador'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#conversão da tabela idade para numerica\n",
+    "df['Idade'] = pd.to_numeric(df['Idade'], errors='coerce')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Análises "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Quantos jogadores são Point Guard (PG): Armador?\n",
+    "numero_pg = df['Posicao'].value_counts()\n",
+    "#criação do grafico\n",
+    "numero_pg.plot(kind=\"bar\", edgecolor=\"blue\", color=\"red\")\n",
+    "#configurações\n",
+    "plt.xlabel('Numero Jogadores')\n",
+    "plt.ylabel('Numero de Times')\n",
+    "plt.title(\"Número de Jogadores na posição de PG\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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P4uLilJiYKHd3MuCvxcmT0tmzNr31p/8qMvycq8up1g587aX7n2+skyc5qwTg18GlaaJhw4aaOXOmmjZtKmOMlixZojvvvFOff/65brrpJo0fP17/+te/tHz5cvn5+SkhIUGDBg3Sf/7zH0nSxYsX1a9fPwUHB2v79u06fvy4hg4dKg8PD73wwguunBpcIDL8nNreeNbVZQAAqhGbMca4uoifCggI0EsvvaTf//73CgwM1NKlS/X73/9eknTw4EFFRkYqOTlZHTt21EcffaTf/e53+vbbbx1nmebPn68nn3xS3333nTw9Pa9pm/n5+fLz81NeXp58fX0rbG6oGLt3S+3aSSlvfklQqmC7v6qpdqOilJIitW3r6moA/Npdj9/fleZbbxcvXtSyZct05swZRUdHKyUlRefPn1fPnj0dfZo3b66wsDAlJydLkpKTk9WyZUunS3ExMTHKz8/X/v37LbdVWFio/Px8pwUAAOByLg9Ke/fulY+Pj+x2u0aPHq1Vq1YpKipKWVlZ8vT0lL+/v1P/oKAgZWVlSZKysrKcQtKl9ZfWWUlMTJSfn59jCQ0NLd9JAQCAasHlQalZs2ZKTU3Vzp07NWbMGMXFxenLL7+s0G1OmjRJeXl5jiUzM7NCtwcAAKoml381zNPTU02aNJEktWvXTp999plef/113XPPPSoqKlJubq7TWaXs7GwFBwdLkoKDg/Xpp586jZedne1YZ8Vut8tut5fzTAAAQHXj8jNKlysuLlZhYaHatWsnDw8Pbdy40bHu0KFDysjIUHR0tCQpOjpae/fu1YkTJxx9NmzYIF9fX0VFRV332gEAQPXi0jNKkyZNUt++fRUWFqbTp09r6dKl2rJliz7++GP5+flpxIgRmjBhggICAuTr66tHH31U0dHR6tixoySpd+/eioqK0gMPPKAXX3xRWVlZeuaZZxQfH88ZIwAA8Iu5NCidOHFCQ4cO1fHjx+Xn56dWrVrp448/Vq9evSRJr732mtzc3BQbG+v0wMlLatSoobVr12rMmDGKjo5WrVq1FBcXp2nTprlqSgAAoBpxaVBauHDhFdd7eXlpzpw5mjNnjmWf8PBwffjhh+VdGgAAQOW7RwkAAKCyICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYICgBAABYcGlQSkxM1M0336zatWurfv36GjhwoA4dOuTUp3v37rLZbE7L6NGjnfpkZGSoX79+qlmzpurXr6+JEyfqwoUL13MqAACgGnJ35caTkpIUHx+vm2++WRcuXNDTTz+t3r1768svv1StWrUc/UaOHKlp06Y5XtesWdPx88WLF9WvXz8FBwdr+/btOn78uIYOHSoPDw+98MIL13U+AACgenFpUFq3bp3T68WLF6t+/fpKSUlR165dHe01a9ZUcHBwqWOsX79eX375pT755BMFBQWpTZs2mj59up588klNnTpVnp6eFToHAABQfVWqe5Ty8vIkSQEBAU7tb7/9turVq6cWLVpo0qRJOnv2rGNdcnKyWrZsqaCgIEdbTEyM8vPztX///lK3U1hYqPz8fKcFAADgci49o/RTxcXFGjdunG699Va1aNHC0T548GCFh4erQYMG2rNnj5588kkdOnRIK1eulCRlZWU5hSRJjtdZWVmlbisxMVHPPfdcBc0EAABUF5UmKMXHx2vfvn3atm2bU/uoUaMcP7ds2VIhISHq0aOH0tLSFBER8bO2NWnSJE2YMMHxOj8/X6GhoT+vcAAAUG1ViktvCQkJWrt2rTZv3qyGDRtesW+HDh0kSUeOHJEkBQcHKzs726nPpddW9zXZ7Xb5+vo6LQAAAJdzaVAyxighIUGrVq3Spk2b1KhRo6u+JzU1VZIUEhIiSYqOjtbevXt14sQJR58NGzbI19dXUVFRFVI3AAD4dXDppbf4+HgtXbpUa9asUe3atR33FPn5+cnb21tpaWlaunSp7rjjDtWtW1d79uzR+PHj1bVrV7Vq1UqS1Lt3b0VFRemBBx7Qiy++qKysLD3zzDOKj4+X3W535fQAAEAV59IzSvPmzVNeXp66d++ukJAQx/LOO+9Ikjw9PfXJJ5+od+/eat68uf74xz8qNjZWH3zwgWOMGjVqaO3atapRo4aio6N1//33a+jQoU7PXQIAAPg5XHpGyRhzxfWhoaFKSkq66jjh4eH68MMPy6ssAAAASZXkZm4AAIDKiKAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABgwaVBKTExUTfffLNq166t+vXra+DAgTp06JBTn3Pnzik+Pl5169aVj4+PYmNjlZ2d7dQnIyND/fr1U82aNVW/fn1NnDhRFy5cuJ5TAQAA1ZBLg1JSUpLi4+O1Y8cObdiwQefPn1fv3r115swZR5/x48frgw8+0PLly5WUlKRvv/1WgwYNcqy/ePGi+vXrp6KiIm3fvl1LlizR4sWLNXnyZFdMCQAAVCPurtz4unXrnF4vXrxY9evXV0pKirp27aq8vDwtXLhQS5cu1e233y5JWrRokSIjI7Vjxw517NhR69ev15dffqlPPvlEQUFBatOmjaZPn64nn3xSU6dOlaenpyumBgAAqoFKdY9SXl6eJCkgIECSlJKSovPnz6tnz56OPs2bN1dYWJiSk5MlScnJyWrZsqWCgoIcfWJiYpSfn6/9+/eXup3CwkLl5+c7LQAAAJerNEGpuLhY48aN06233qoWLVpIkrKysuTp6Sl/f3+nvkFBQcrKynL0+WlIurT+0rrSJCYmys/Pz7GEhoaW82wAAEB1UGmCUnx8vPbt26dly5ZV+LYmTZqkvLw8x5KZmVnh2wQAAFWPS+9RuiQhIUFr167V1q1b1bBhQ0d7cHCwioqKlJub63RWKTs7W8HBwY4+n376qdN4l74Vd6nP5ex2u+x2eznPAgAAVDcuPaNkjFFCQoJWrVqlTZs2qVGjRk7r27VrJw8PD23cuNHRdujQIWVkZCg6OlqSFB0drb179+rEiROOPhs2bJCvr6+ioqKuz0QAAEC15NIzSvHx8Vq6dKnWrFmj2rVrO+4p8vPzk7e3t/z8/DRixAhNmDBBAQEB8vX11aOPPqro6Gh17NhRktS7d29FRUXpgQce0IsvvqisrCw988wzio+P56wRAAD4RVwalObNmydJ6t69u1P7okWLNGzYMEnSa6+9Jjc3N8XGxqqwsFAxMTGaO3euo2+NGjW0du1ajRkzRtHR0apVq5bi4uI0bdq06zUNAABQTbk0KBljrtrHy8tLc+bM0Zw5cyz7hIeH68MPPyzP0gAAACrPt94AAAAqG4ISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACAhWsOSsYYZWRk6Ny5cxVZDwAAQKVRpqDUpEkTZWZmVmQ9AAAAlcY1ByU3Nzc1bdpUp06dqsh6AAAAKo0y3aM0c+ZMTZw4Ufv27auoegAAACoN97J0Hjp0qM6ePavWrVvL09NT3t7eTutzcnLKtTgAAABXKlNQmjVrVgWVAQAAUPmUKSjFxcVVVB0AAACVTpmCkiRdvHhRq1ev1oEDByRJN910kwYMGKAaNWqUe3EAAACuVKagdOTIEd1xxx365ptv1KxZM0lSYmKiQkND9a9//UsREREVUiQAAIArlOlbb2PHjlVERIQyMzO1e/du7d69WxkZGWrUqJHGjh1bUTUCAAC4RJnOKCUlJWnHjh0KCAhwtNWtW1czZ87UrbfeWu7FAQAAuFKZzijZ7XadPn26RHtBQYE8PT3LrSgAAIDKoExB6Xe/+51GjRqlnTt3yhgjY4x27Nih0aNHa8CAARVVIwAAgEuUKSjNnj1bERERio6OlpeXl7y8vHTrrbeqSZMmev311yuqRgAAAJco0z1K/v7+WrNmjQ4fPqyDBw9KkiIjI9WkSZMKKQ4AAMCVyvwcJUlq2rSpmjZtWt61AAAAVCpXDUoTJky45sFeffXVX1QMAABAZXLVoPT55587vd69e7cuXLjgeODkV199pRo1aqhdu3YVUyEAAICLXDUobd682fHzq6++qtq1a2vJkiWqU6eOJOn777/X8OHD1aVLl4qrEgAAwAXK9K23V155RYmJiY6QJEl16tTRjBkz9Morr5R7cQAAAK5UpqCUn5+v7777rkT7d999V+qDKAEAAKqyMgWlu+66S8OHD9fKlSt17NgxHTt2TO+9955GjBihQYMGVVSNAAAALlGmxwPMnz9fjz/+uAYPHqzz58//OIC7u0aMGKGXXnqpQgoEAABwlTIFpZo1a2ru3Ll66aWXlJaWJkmKiIhQrVq1KqQ4AAAAV/pZD5ysVauWWrVqVd61AAAAVCplDkq7du3Su+++q4yMDBUVFTmtW7lyZbkVBgAA4Gplupl72bJl6tSpkw4cOKBVq1bp/Pnz2r9/vzZt2iQ/P7+KqhEAAMAlyhSUXnjhBb322mv64IMP5Onpqddff10HDx7UH/7wB4WFhVVUjQAAAC5RpqCUlpamfv36SZI8PT115swZ2Ww2jR8/Xm+++WaFFAgAAOAqZQpKderUcTxY8je/+Y327dsnScrNzdXZs2fLvzoAAAAXKtPN3F27dtWGDRvUsmVL3X333Xrssce0adMmbdiwQT169KioGgEAAFyiTEHpL3/5i86dOydJ+tOf/iQPDw9t375dsbGxeuaZZyqkQAAAAFcpU1AKCAhw/Ozm5qannnqq3AsCAACoLMr8R3FLW06fPl3imUrXYuvWrerfv78aNGggm82m1atXO60fNmyYbDab09KnTx+nPjk5ORoyZIh8fX3l7++vESNGqKCgoMy1AAAAXK5MQcnf31916tQpsfj7+8vb21vh4eGaMmWKiouLr2m8M2fOqHXr1pozZ45lnz59+uj48eOO5Z///KfT+iFDhmj//v3asGGD1q5dq61bt2rUqFFlmRYAAECpynTpbfHixfrTn/6kYcOG6ZZbbpEkffrpp1qyZImeeeYZfffdd3r55Zdlt9v19NNPX3W8vn37qm/fvlfsY7fbFRwcXOq6AwcOaN26dfrss8/Uvn17SdIbb7yhO+64Qy+//LIaNGhQlukBAAA4KVNQWrJkiV555RX94Q9/cLT1799fLVu21IIFC7Rx40aFhYXp+eefv6agdC22bNmi+vXrq06dOrr99ts1Y8YM1a1bV5KUnJwsf39/R0iSpJ49e8rNzU07d+7UXXfdVS41AACAX6cyXXrbvn27fvvb35Zo/+1vf6vk5GRJUufOnZWRkVEuxfXp00d///vftXHjRv35z39WUlKS+vbtq4sXL0qSsrKyVL9+faf3uLu7KyAgQFlZWZbjFhYWlrjPCgAA4HJlOqMUGhqqhQsXaubMmU7tCxcuVGhoqCTp1KlTqlOnTrkUd++99zp+btmypVq1aqWIiAht2bLlFz23KTExUc8991x5lHhVGRnSyZPXZVO/WgcOuLoCAEB1Vaag9PLLL+vuu+/WRx99pJtvvlmStGvXLh08eFArVqyQJH322We65557yr9SSY0bN1a9evV05MgR9ejRQ8HBwTpx4oRTnwsXLignJ8fyviZJmjRpkiZMmOB4nZ+f7wh65SkjQ4qMNDp71lbuYwMAgIpXpqA0YMAAHTx4UAsWLNBXX30l6ccbslevXq0bbrhBkjRmzJhyL/KSY8eO6dSpUwoJCZEkRUdHKzc3VykpKWrXrp0kadOmTSouLlaHDh0sx7Hb7bLb7RVW5yUnT0pnz9r01p/+q8jwcxW+vV+rD3f46tm/NXR1GQCAaqhMQUmSGjVqVOLS289VUFCgI0eOOF6np6crNTVVAQEBCggI0HPPPafY2FgFBwcrLS1NTzzxhJo0aaKYmBhJUmRkpPr06aORI0dq/vz5On/+vBISEnTvvfdWqm+8RYafU9sb+Vt4FeXA116uLuFXh8udFa9ePSkszNVVAChzUMrNzdXChQt14P//l/Kmm27Sgw8+KD8/vzJvfNeuXbrtttscry9dDouLi9O8efO0Z88eLVmyRLm5uWrQoIF69+6t6dOnO50Nevvtt5WQkKAePXrIzc1NsbGxmj17dplrAXB1x0+5y83N6P77uZxc0WrWNDpwwEZYAlysTEFp165diomJkbe3t+M5Sq+++qqef/55rV+/Xm3bti3Txrt37y5jjOX6jz/++KpjBAQEaOnSpWXaLoCfJ7fAXcXFXE6uaAe+9tL9zzfWyZOcVQJcrUxBafz48RowYID+7//+T+7uP771woULeuihhzRu3Dht3bq1QooEULlwORnAr0WZzyj9NCRJPz636IknnnB66CMAAEB1UKYHTvr6+pb6MMnMzEzVrl273IoCAACoDMoUlO655x6NGDFC77zzjjIzM5WZmally5bpoYce0n333VdRNQIAALhEmR84abPZNHToUF24cEHGGHl6emrMmDHl9sgAAACAyqJMQcnT01Ovv/66EhMTlZaWJkmKiIhQzZo1K6Q4AAAAV7qmoDRo0KCrD+TuruDgYPXq1Uv9+/f/xYUBAAC42jXdo+Tn53fVxdvbW4cPH9Y999yjyZMnV3TdAAAAFe6azigtWrTomgdcu3atHnnkEU2bNu1nFwUAAFAZlOlbb9eic+fOPFMJAABUC+UelPz9/bVy5cryHhYAAOC6K/egBAAAUF0QlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACy4NCht3bpV/fv3V4MGDWSz2bR69Wqn9cYYTZ48WSEhIfL29lbPnj11+PBhpz45OTkaMmSIfH195e/vrxEjRqigoOA6zgIAAFRXLg1KZ86cUevWrTVnzpxS17/44ouaPXu25s+fr507d6pWrVqKiYnRuXPnHH2GDBmi/fv3a8OGDVq7dq22bt2qUaNGXa8pAACAaszdlRvv27ev+vbtW+o6Y4xmzZqlZ555Rnfeeack6e9//7uCgoK0evVq3XvvvTpw4IDWrVunzz77TO3bt5ckvfHGG7rjjjv08ssvq0GDBtdtLgAAoPqptPcopaenKysrSz179nS0+fn5qUOHDkpOTpYkJScny9/f3xGSJKlnz55yc3PTzp07LccuLCxUfn6+0wIAAHC5ShuUsrKyJElBQUFO7UFBQY51WVlZql+/vtN6d3d3BQQEOPqUJjExUX5+fo4lNDS0nKsHAADVQaUNShVp0qRJysvLcyyZmZmuLgkAAFRClTYoBQcHS5Kys7Od2rOzsx3rgoODdeLECaf1Fy5cUE5OjqNPaex2u3x9fZ0WAACAy1XaoNSoUSMFBwdr48aNjrb8/Hzt3LlT0dHRkqTo6Gjl5uYqJSXF0WfTpk0qLi5Whw4drnvNAACgenHpt94KCgp05MgRx+v09HSlpqYqICBAYWFhGjdunGbMmKGmTZuqUaNGevbZZ9WgQQMNHDhQkhQZGak+ffpo5MiRmj9/vs6fP6+EhATde++9fOMNAAD8Yi4NSrt27dJtt93meD1hwgRJUlxcnBYvXqwnnnhCZ86c0ahRo5Sbm6vOnTtr3bp18vLycrzn7bffVkJCgnr06CE3NzfFxsZq9uzZ130uAACg+nFpUOrevbuMMZbrbTabpk2bpmnTpln2CQgI0NKlSyuiPAAA8CtXae9RAgAAcDWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWXPh4AAGDtwAFXV1D91asnhYW5ugpUZgQlAKhkjp9yl5ub0f3321xdSrVXs6bRgQM2whIsEZQAoJLJLXBXcbFNb/3pv4oMP+fqcqqtA1976f7nG+vkSc4qwRpBCQAqqcjwc2p741lXlwH8qnEzNwAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgAWCEgAAgIVKHZSmTp0qm83mtDRv3tyx/ty5c4qPj1fdunXl4+Oj2NhYZWdnu7BiAABQnVTqoCRJN910k44fP+5Ytm3b5lg3fvx4ffDBB1q+fLmSkpL07bffatCgQS6sFgAAVCfuri7gatzd3RUcHFyiPS8vTwsXLtTSpUt1++23S5IWLVqkyMhI7dixQx07drzepQIAgGqm0p9ROnz4sBo0aKDGjRtryJAhysjIkCSlpKTo/Pnz6tmzp6Nv8+bNFRYWpuTk5CuOWVhYqPz8fKcFAADgcpU6KHXo0EGLFy/WunXrNG/ePKWnp6tLly46ffq0srKy5OnpKX9/f6f3BAUFKSsr64rjJiYmys/Pz7GEhoZW4CwAAEBVVakvvfXt29fxc6tWrdShQweFh4fr3Xfflbe3988ed9KkSZowYYLjdX5+PmEJAACUUKmD0uX8/f1144036siRI+rVq5eKioqUm5vrdFYpOzu71Huafsput8tut1dwtQCAquDAAVdXUP3VqyeFhbm6ip+nSgWlgoICpaWl6YEHHlC7du3k4eGhjRs3KjY2VpJ06NAhZWRkKDo62sWVAgAqu+On3OXmZnT//TZXl1Lt1axpdOCArUqGpUodlB5//HH1799f4eHh+vbbbzVlyhTVqFFD9913n/z8/DRixAhNmDBBAQEB8vX11aOPPqro6Gi+8QYAuKrcAncVF9v01p/+q8jwc64up9o68LWX7n++sU6erJpnlSp1UDp27Jjuu+8+nTp1SoGBgercubN27NihwMBASdJrr70mNzc3xcbGqrCwUDExMZo7d66LqwYAVCWR4efU9sazri4DlVSlDkrLli274novLy/NmTNHc+bMuU4VAQCAX5NK/XgAAAAAVyIoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWKg2QWnOnDm64YYb5OXlpQ4dOujTTz91dUkAAKCKqxZB6Z133tGECRM0ZcoU7d69W61bt1ZMTIxOnDjh6tIAAEAVVi2C0quvvqqRI0dq+PDhioqK0vz581WzZk397W9/c3VpAACgCqvyQamoqEgpKSnq2bOno83NzU09e/ZUcnKyCysDAABVnburC/ilTp48qYsXLyooKMipPSgoSAcPHiz1PYWFhSosLHS8zsvLkyTl5+eXa20FBT/+M+Wr8yr4obhcx8b/HPi6UFI+n/N1wGd9ffA5Xx98ztfHoczzkvJVUCCV869Zx+9tY0z5DvxTpor75ptvjCSzfft2p/aJEyeaW265pdT3TJkyxUhiYWFhYWFhqQZLWlpaheWMKn9GqV69eqpRo4ays7Od2rOzsxUcHFzqeyZNmqQJEyY4XhcXFysnJ0d169aVzWar0HrLS35+vkJDQ5WZmSlfX19Xl1PumF/VV93nWN3nJ1X/OTK/qi8vL09hYWEKCAiosG1U+aDk6empdu3aaePGjRo4cKCkH4PPxo0blZCQUOp77Ha77Ha7U5u/v38FV1oxfH19q+0BIDG/6qC6z7G6z0+q/nNkflWfm1vF3XJd5YOSJE2YMEFxcXFq3769brnlFs2aNUtnzpzR8OHDXV0aAACowqpFULrnnnv03XffafLkycrKylKbNm20bt26Ejd4AwAAlEW1CEqSlJCQYHmprTqy2+2aMmVKiUuI1QXzq/qq+xyr+/yk6j9H5lf1XY852oypyO/UAQAAVF1V/oGTAAAAFYWgBAAAYIGgBAAAYIGgBAAAYIGg5GKJiYm6+eabVbt2bdWvX18DBw7UoUOHnPqcO3dO8fHxqlu3rnx8fBQbG1viSeSXM8Zo8uTJCgkJkbe3t3r27KnDhw9X5FQsXW2OOTk5evTRR9WsWTN5e3srLCxMY8eOdfwNPivDhg2TzWZzWvr06VPR0ynhWvZh9+7dS9Q6evToK45blfbh0aNHS8zv0rJ8+XLLcSvLPpw3b55atWrleDBfdHS0PvroI8f6qn4MXml+Vf34u+Rq+7CqH4NXml9VP/5KM3PmTNlsNo0bN87R5rLjsML+OAquSUxMjFm0aJHZt2+fSU1NNXfccYcJCwszBQUFjj6jR482oaGhZuPGjWbXrl2mY8eOplOnTlccd+bMmcbPz8+sXr3afPHFF2bAgAGmUaNG5ocffqjoKZVwtTnu3bvXDBo0yLz//vvmyJEjZuPGjaZp06YmNjb2iuPGxcWZPn36mOPHjzuWnJyc6zElJ9eyD7t162ZGjhzpVGteXt4Vx61K+/DChQtOczt+/Lh57rnnjI+Pjzl9+rTluJVlH77//vvmX//6l/nqq6/MoUOHzNNPP208PDzMvn37jDFV/xi80vyq+vF3ydX2YVU/Bq80v6p+/F3u008/NTfccINp1aqVeeyxxxztrjoOCUqVzIkTJ4wkk5SUZIwxJjc313h4eJjly5c7+hw4cMBIMsnJyaWOUVxcbIKDg81LL73kaMvNzTV2u93885//rNgJXIPL51iad99913h6eprz589b9omLizN33nlnBVT4y5Q2v27dujkd8FdTHfZhmzZtzIMPPnjFcSrrPjTGmDp16pi//vWv1fIYNOZ/8ytNVT7+fuqnc6xux6AxV96HVfX4O336tGnatKnZsGGD0z5z5XHIpbdK5tLp7kt/4C8lJUXnz59Xz549HX2aN2+usLAwJScnlzpGenq6srKynN7j5+enDh06WL7nerp8jlZ9fH195e5+5WeibtmyRfXr11ezZs00ZswYnTp1qlxr/Tms5vf222+rXr16atGihSZNmqSzZ89ajlHV92FKSopSU1M1YsSIq45V2fbhxYsXtWzZMp05c0bR0dHV7hi8fH6lqcrHn2Q9x+pyDF5tH1bl4y8+Pl79+vVz+twl1/4urDZP5q4OiouLNW7cON16661q0aKFJCkrK0uenp4l/mhvUFCQsrKySh3nUvvlf8LlSu+5Xkqb4+VOnjyp6dOna9SoUVccq0+fPho0aJAaNWqktLQ0Pf300+rbt6+Sk5NVo0aNiij/qqzmN3jwYIWHh6tBgwbas2ePnnzySR06dEgrV64sdZyqvg8XLlyoyMhIderU6YpjVaZ9uHfvXkVHR+vcuXPy8fHRqlWrFBUVpdTU1GpxDFrN73JV+fi70hyrwzF4rfuwKh5/krRs2TLt3r1bn332WYl1rvxdSFCqROLj47Vv3z5t27bN1aVUmKvNMT8/X/369VNUVJSmTp16xbHuvfdex88tW7ZUq1atFBERoS1btqhHjx7lWfY1s5rfT3/ptGzZUiEhIerRo4fS0tIUERFxvcv8Ra62D3/44QctXbpUzz777FXHqkz7sFmzZkpNTVVeXp5WrFihuLg4JSUlXdcaKpLV/H76i7aqH39XmmN1OAavZR9W1eMvMzNTjz32mDZs2CAvL6/ruu2r4dJbJZGQkKC1a9dq8+bNatiwoaM9ODhYRUVFys3NdeqfnZ2t4ODgUse61H75twGu9J7rwWqOl5w+fVp9+vRR7dq1tWrVKnl4eJRp/MaNG6tevXo6cuRIeZVcJleb30916NBBkixrrar7UJJWrFihs2fPaujQoWUe35X70NPTU02aNFG7du2UmJio1q1b6/XXX682x6DV/C6p6sefdPU5/lRVPAavZX5V9fhLSUnRiRMn1LZtW7m7u8vd3V1JSUmaPXu23N3dFRQU5LLjkKDkYsYYJSQkaNWqVdq0aZMaNWrktL5du3by8PDQxo0bHW2HDh1SRkaG5f0FjRo1UnBwsNN78vPztXPnTsv3VKSrzfFSfb1795anp6fef//9n/V/FMeOHdOpU6cUEhJSHmVfs2uZ3+VSU1MlybLWqrgPL1m4cKEGDBigwMDAMm/HVfuwNMXFxSosLKwWx2BpLs1PqtrH35X8dI6Xq2rHYGlKm19VPf569OihvXv3KjU11bG0b99eQ4YMcfzssuOw7PekozyNGTPG+Pn5mS1btjh9RfPs2bOOPqNHjzZhYWFm06ZNZteuXSY6OtpER0c7jdOsWTOzcuVKx+uZM2caf39/s2bNGrNnzx5z5513uuxrrVebY15enunQoYNp2bKlOXLkiFOfCxculDrH06dPm8cff9wkJyeb9PR088knn5i2bduapk2bmnPnzlWq+R05csRMmzbN7Nq1y6Snp5s1a9aYxo0bm65duzqNU5X34SWHDx82NpvNfPTRR6WOU1n34VNPPWWSkpJMenq62bNnj3nqqaeMzWYz69evN8ZU/WPwSvOr6sfftcyxOhyDV/t31Jiqe/xZufybiq46DglKLiap1GXRokWOPj/88IN55JFHTJ06dUzNmjXNXXfdZY4fP15inJ++p7i42Dz77LMmKCjI2O1206NHD3Po0KHrNCtnV5vj5s2bLfukp6c7jXPpPWfPnjW9e/c2gYGBxsPDw4SHh5uRI0earKysSje/jIwM07VrVxMQEGDsdrtp0qSJmThxYolnuFTlfXjJpEmTTGhoqLl48aLlOJVxHz744IMmPDzceHp6msDAQNOjRw+nX0BV/Ri80vyq+vF3yZXmWB2Owav9O2pM1T3+rFwelFx1HNr+/8AAAAC4DPcoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAQAAWCAoAaiyunfvrnHjxv2iMbZs2SKbzVbib0gBgERQAlCJDRs2TAMHDnR1GQB+xQhKAAAAFghKAKqEM2fOaOjQofLx8VFISIheeeWVEn3+8Y9/qH379qpdu7aCg4M1ePBgnThxwqnPhx9+qBtvvFHe3t667bbbdPTo0RLjbNu2TV26dJG3t7dCQ0M1duxYnTlzpqKmBqASIygBqBImTpyopKQkrVmzRuvXr9eWLVu0e/dupz7nz5/X9OnT9cUXX2j16tU6evSohg0b5lifmZmpQYMGqX///kpNTdVDDz2kp556ymmMtLQ09enTR7GxsdqzZ4/eeecdbdu2TQkJCddjmgAqGf4oLoBKa9iwYcrNzdVbb72lunXr6q233tLdd98tScrJyVHDhg01atQozZo1q9T379q1SzfffLNOnz4tHx8fPf3001qzZo3279/v6PPUU0/pz3/+s77//nv5+/vroYceUo0aNbRgwQJHn23btqlbt246c+aMvLy8KnTOACoXzigBqPTS0tJUVFSkDh06ONoCAgLUrFkzp34pKSnq37+/wsLCVLt2bXXr1k2SlJGRIUk6cOCA0xiSFB0d7fT6iy++0OLFi+Xj4+NYYmJiVFxcrPT09IqYHoBKzN3VBQBAeThz5oxiYmIUExOjt99+W4GBgcrIyFBMTIyKioqueZyCggI9/PDDGjt2bIl1YWFh5VkygCqAoASg0ouIiJCHh4d27tzpCCvff/+9vvrqK8dZo4MHD+rUqVOaOXOmQkNDJf146e2nIiMj9f777zu17dixw+l127Zt9eWXX6pJkyYVNR0AVQiX3gBUej4+PhoxYoQmTpyoTZs2ad++fRo2bJjc3P73n7CwsDB5enrqjTfe0H//+1+9//77mj59utM4o0eP1uHDhzVx4kQdOnRIS5cu1eLFi536PPnkk9q+fbsSEhKUmpqqw4cPa82aNdzMDfxKEZQAVAkvvfSSunTpov79+6tnz57q3Lmz2rVr51gfGBioxYsXa/ny5YqKitLMmTP18ssvO40RFham9957T6tXr1br1q01f/58vfDCC059WrVqpaSkJH311Vfq0qWLfvvb32ry5Mlq0KDBdZkngMqFb70BAABY4IwSAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACAhf8H9NjBOmgitn0AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Idades dos jogadores\n",
+    "df[\"Idade\"].plot.hist(bins=5, edgecolor= \"blue\", color=\"pink\")\n",
+    "\n",
+    "plt.xlabel(\"Idade\")\n",
+    "plt.ylabel(\"Jogador\")\n",
+    "plt.title(\"Idade dos Jogadores\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Qual a taxa dos jogadores com 25 anos? \n",
+    "taxa_jogador = df.groupby(\"Idade\")[\"Jogador\"].size()\n",
+    "#cores para barras\n",
+    "cores = [\"lightpink\",\"lightblue\"]\n",
+    "# plotagem\n",
+    "barras = taxa_jogador.plot.bar(edgecolor = \"blue\", color= cores)\n",
+    "# os rotulos\n",
+    "plt.xlabel(\"Idade\")\n",
+    "plt.ylabel(\"Taxa de Jogadores\")\n",
+    "plt.title(\"Taxa de Jogadores com 25 anos\")\n",
+    "#adicionar legendas\n",
+    "plt.legend([\"Jogadores com 25 anos\"])\n",
+    "#adicionar rotulos nos graficos\n",
+    "for i, v in enumerate(taxa_jogador):\n",
+    "    barras.text(i, v + 0.01, f'{v:.2f}', color = 'black', ha = 'center')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test T Melhor Time\n",
+      "Estatistica T : nan\n",
+      "Valor P: nan\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Colaborador\\AppData\\Local\\Temp\\ipykernel_14644\\2359973475.py:8: SmallSampleWarning: One or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements.\n",
+      "  estatistica_t, valor_p = ttest_ind(melhor_time, pior_time)\n",
+      "C:\\Users\\Colaborador\\AppData\\Local\\Temp\\ipykernel_14644\\2359973475.py:21: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "  plt.legend()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Não rejeitamos a hipotese nula\n"
+     ]
+    }
+   ],
+   "source": [
+    "#amostras\n",
+    "\n",
+    "melhor_time = df[df['Times']==1]['Jogo'].dropna()\n",
+    "pior_time = df[df['Times']==0]['Jogo'].dropna()\n",
+    "\n",
+    "#teste t \n",
+    "\n",
+    "estatistica_t, valor_p = ttest_ind(melhor_time, pior_time)\n",
+    "\n",
+    "print(\"Test T Melhor Time\")\n",
+    "print(f\"Estatistica T : {estatistica_t}\")\n",
+    "print(f\"Valor P: {valor_p}\")\n",
+    "\n",
+    "#grafico\n",
+    "\n",
+    "sns.histplot(melhor_time,color= 'blue', label='O melhor',bins=20)\n",
+    "sns.histplot(pior_time,color= 'red', label='O pior',bins=20)\n",
+    "\n",
+    "#Rotulo\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.title(\"Distribuição de Times\")\n",
+    "plt.xlabel(\"Times\")\n",
+    "plt.ylabel (\"Contagem\")\n",
+    "plt.show()\n",
+    "\n",
+    "#interpretação \n",
+    "\n",
+    "if valor_p < 0.10:\n",
+    "    print(\"Rejeitamos a hipotese nula\")\n",
+    "else:\n",
+    "    print(\"Não rejeitamos a hipotese nula\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "baby_df = df.sample(50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Valor</th>\n",
+       "      <th>Jogador</th>\n",
+       "      <th>Posicao</th>\n",
+       "      <th>Idade</th>\n",
+       "      <th>Times</th>\n",
+       "      <th>Jogo</th>\n",
+       "      <th>GS</th>\n",
+       "      <th>MP</th>\n",
+       "      <th>FG</th>\n",
+       "      <th>FGA</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ORB</th>\n",
+       "      <th>DRB</th>\n",
+       "      <th>TRB</th>\n",
+       "      <th>AST</th>\n",
+       "      <th>STL</th>\n",
+       "      <th>BLK</th>\n",
+       "      <th>TOV</th>\n",
+       "      <th>PF</th>\n",
+       "      <th>PTS</th>\n",
+       "      <th>ID_Jogador</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>216</th>\n",
+       "      <td>161</td>\n",
+       "      <td>Daniel Gafford</td>\n",
+       "      <td>C-PF</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>TOT</td>\n",
+       "      <td>74</td>\n",
+       "      <td>66</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>107</td>\n",
+       "      <td>Ricky Council IV</td>\n",
+       "      <td>SG</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>PHI</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>151</td>\n",
+       "      <td>Simone Fontecchio</td>\n",
+       "      <td>SF</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>TOT</td>\n",
+       "      <td>66</td>\n",
+       "      <td>43</td>\n",
+       "      <td>24.9</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>506</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Valor            Jogador Posicao  Idade Times Jogo  GS    MP   FG  FGA  \\\n",
+       "216   161     Daniel Gafford    C-PF   25.0   TOT   74  66  24.5  4.7  6.5   \n",
+       "143   107   Ricky Council IV      SG   22.0   PHI   32   0   9.0  1.7  3.5   \n",
+       "200   151  Simone Fontecchio      SF   28.0   TOT   66  43  24.9  3.8  8.3   \n",
+       "\n",
+       "     ...  ORB  DRB  TRB  AST  STL  BLK  TOV   PF   PTS ID_Jogador  \n",
+       "216  ...  2.6  5.0  7.6  1.6  0.9  2.1  1.0  3.1  11.0        110  \n",
+       "143  ...  0.4  1.0  1.4  0.5  0.3  0.0  0.2  0.5   5.4        478  \n",
+       "200  ...  1.0  2.8  3.7  1.5  0.7  0.3  1.0  1.7  10.5        506  \n",
+       "\n",
+       "[3 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "baby_df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               Jogador  Contagem\n",
+      "0         Adam Flagler         1\n",
+      "1           Al Horford         1\n",
+      "2      Anfernee Simons         1\n",
+      "3      Bismack Biyombo         1\n",
+      "4     Bojan Bogdanović         1\n",
+      "5   Brandon Boston Jr.         1\n",
+      "6          Bruce Brown         1\n",
+      "7      Cade Cunningham         1\n",
+      "8          Cory Joseph         1\n",
+      "9       Daniel Gafford         1\n",
+      "10      David Duke Jr.         1\n",
+      "11         David Roddy         1\n",
+      "12           Dean Wade         1\n",
+      "13     Dejounte Murray         1\n",
+      "14      Gordon Hayward         1\n",
+      "15         Harry Giles         1\n",
+      "16         Ivica Zubac         1\n",
+      "17             JT Thor         1\n",
+      "18       Jacob Gilyard         2\n",
+      "19      Jaden Springer         1\n",
+      "20        Jake LaRavia         1\n",
+      "21         Jalen Duren         1\n",
+      "22      Jalen Williams         1\n",
+      "23          Jamal Cain         1\n",
+      "24       James Johnson         1\n",
+      "25       Jaylen Nowell         1\n",
+      "26       Jeremy Sochan         1\n",
+      "27      Jordan Goodwin         1\n",
+      "28     Josh Richardson         1\n",
+      "29   Julian Champagnie         1\n",
+      "30     Julian Phillips         1\n",
+      "31  Karl-Anthony Towns         1\n",
+      "32      Kira Lewis Jr.         1\n",
+      "33          Kyle Kuzma         1\n",
+      "34       Lamar Stevens         1\n",
+      "35          Malik Monk         1\n",
+      "36   Marvin Bagley III         1\n",
+      "37         Moses Brown         1\n",
+      "38      Nick Smith Jr.         1\n",
+      "39     Otto Porter Jr.         1\n",
+      "40      Quentin Grimes         1\n",
+      "41    Ricky Council IV         1\n",
+      "42    Robert Covington         1\n",
+      "43       Rui Hachimura         1\n",
+      "44   Simone Fontecchio         1\n",
+      "45   Spencer Dinwiddie         1\n",
+      "46       Thomas Bryant         1\n",
+      "47      Troy Brown Jr.         1\n",
+      "48           Ty Jerome         1\n"
+     ]
+    }
+   ],
+   "source": [
+    "#SQL\n",
+    "conn = sqlite3.connect(':memory:')\n",
+    "baby_df.to_sql('baby_df',conn, index = False, if_exists='replace')\n",
+    "\n",
+    "query_sql = \"\"\"\n",
+    "SELECT Jogador, COUNT(Times) AS Contagem\n",
+    "FROM baby_df\n",
+    "GROUP BY Jogador;\n",
+    "\"\"\"\n",
+    "\n",
+    "contagem_por_jogador =pd.read_sql_query(query_sql,conn)\n",
+    "print (contagem_por_jogador)\n",
+    "#fechar conexão\n",
+    "conn.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jogador\n",
+      "Jacob Gilyard         2\n",
+      "Daniel Gafford        1\n",
+      "Ricky Council IV      1\n",
+      "Thomas Bryant         1\n",
+      "Simone Fontecchio     1\n",
+      "Spencer Dinwiddie     1\n",
+      "Jalen Duren           1\n",
+      "Cory Joseph           1\n",
+      "Adam Flagler          1\n",
+      "Julian Champagnie     1\n",
+      "Julian Phillips       1\n",
+      "Karl-Anthony Towns    1\n",
+      "JT Thor               1\n",
+      "Robert Covington      1\n",
+      "Rui Hachimura         1\n",
+      "Ivica Zubac           1\n",
+      "Otto Porter Jr.       1\n",
+      "Josh Richardson       1\n",
+      "Bruce Brown           1\n",
+      "Moses Brown           1\n",
+      "Malik Monk            1\n",
+      "Ty Jerome             1\n",
+      "Jordan Goodwin        1\n",
+      "Dejounte Murray       1\n",
+      "David Roddy           1\n",
+      "Troy Brown Jr.        1\n",
+      "Jake LaRavia          1\n",
+      "Kyle Kuzma            1\n",
+      "Jalen Williams        1\n",
+      "Bismack Biyombo       1\n",
+      "David Duke Jr.        1\n",
+      "Marvin Bagley III     1\n",
+      "Quentin Grimes        1\n",
+      "Harry Giles           1\n",
+      "Lamar Stevens         1\n",
+      "Nick Smith Jr.        1\n",
+      "Jamal Cain            1\n",
+      "Jaden Springer        1\n",
+      "Gordon Hayward        1\n",
+      "Al Horford            1\n",
+      "Brandon Boston Jr.    1\n",
+      "James Johnson         1\n",
+      "Bojan Bogdanović      1\n",
+      "Jaylen Nowell         1\n",
+      "Jeremy Sochan         1\n",
+      "Cade Cunningham       1\n",
+      "Anfernee Simons       1\n",
+      "Kira Lewis Jr.        1\n",
+      "Dean Wade             1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#PANDAS\n",
+    "contagem_por_jogador = baby_df ['Jogador'].value_counts()\n",
+    "contagem_por_jogador.columns = ['Jogador', 'contagem']\n",
+    "print(contagem_por_jogador)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-casa/nba-player-data-novo.csv b/exercicios/para-casa/nba-player-data-novo.csv
new file mode 100644
index 0000000..2e1510b
--- /dev/null
+++ b/exercicios/para-casa/nba-player-data-novo.csv
@@ -0,0 +1,737 @@
+Valor,Jogador,Posicao,Idade,Times,Jogo,GS,MP,FG,FGA,FG%,3P,3PA,3P%,2P,2PA,2P%,eFG%,FT,FTA,FT%,ORB,DRB,TRB,AST,STL,BLK,TOV,PF,PTS
+1,Precious Achiuwa,PF-C,24,TOT,74,18,21.9,3.2,6.3,.501,0.4,1.3,.268,2.8,5.0,.562,.529,0.9,1.5,.616,2.6,4.0,6.6,1.3,0.6,0.9,1.1,1.9,7.6
+1,Precious Achiuwa,C,24,TOR,25,0,17.5,3.1,6.8,.459,0.5,1.9,.277,2.6,4.9,.528,.497,1.0,1.7,.571,2.0,3.4,5.4,1.8,0.6,0.5,1.2,1.6,7.7
+1,Precious Achiuwa,PF,24,NYK,49,18,24.2,3.2,6.1,.525,0.3,1.0,.260,2.9,5.1,.578,.547,0.9,1.4,.643,2.9,4.3,7.2,1.1,0.6,1.1,1.1,2.1,7.6
+2,Bam Adebayo,C,26,MIA,71,71,34.0,7.5,14.3,.521,0.2,0.6,.357,7.3,13.7,.528,.529,4.1,5.5,.755,2.2,8.1,10.4,3.9,1.1,0.9,2.3,2.2,19.3
+3,Ochai Agbaji,SG,23,TOT,78,28,21.0,2.3,5.6,.411,0.8,2.7,.294,1.5,2.8,.523,.483,0.5,0.7,.661,0.9,1.8,2.8,1.1,0.6,0.6,0.8,1.5,5.8
+3,Ochai Agbaji,SG,23,UTA,51,10,19.7,2.1,4.9,.426,0.9,2.8,.331,1.2,2.1,.551,.520,0.3,0.4,.750,0.7,1.8,2.5,0.9,0.5,0.6,0.7,1.3,5.4
+3,Ochai Agbaji,SG,23,TOR,27,18,23.6,2.7,6.8,.391,0.6,2.6,.217,2.1,4.3,.496,.432,0.8,1.3,.611,1.4,1.9,3.3,1.3,0.7,0.6,1.1,1.9,6.7
+4,Santi Aldama,PF,23,MEM,61,35,26.5,4.0,9.3,.435,1.7,5.0,.349,2.3,4.3,.534,.528,0.9,1.4,.621,1.2,4.6,5.8,2.3,0.7,0.9,1.1,1.5,10.7
+5,Nickeil Alexander-Walker,SG,25,MIN,82,20,23.4,2.9,6.6,.439,1.6,4.1,.391,1.3,2.5,.517,.560,0.6,0.8,.800,0.4,1.6,2.0,2.5,0.8,0.5,0.9,1.7,8.0
+6,Grayson Allen,SG,28,PHO,75,74,33.5,4.5,9.1,.499,2.7,5.9,.461,1.8,3.2,.570,.649,1.7,2.0,.878,0.6,3.3,3.9,3.0,0.9,0.6,1.3,2.1,13.5
+7,Jarrett Allen,C,25,CLE,77,77,31.7,6.7,10.6,.634,0.0,0.1,.000,6.7,10.6,.638,.634,3.0,4.1,.742,3.2,7.4,10.5,2.7,0.7,1.1,1.6,1.9,16.5
+8,Timmy Allen,SF,24,MEM,5,0,25.0,1.2,4.6,.261,0.0,1.4,.000,1.2,3.2,.375,.261,0.2,0.4,.500,0.8,2.6,3.4,1.0,0.8,0.0,0.4,3.6,2.6
+9,Jose Alvarado,PG,25,NOP,56,0,18.4,2.5,6.2,.412,1.4,3.7,.377,1.1,2.5,.464,.525,0.6,0.9,.673,0.4,1.8,2.3,2.1,1.1,0.3,0.7,1.6,7.1
+10,Kyle Anderson,PF,30,MIN,79,10,22.6,2.5,5.5,.460,0.1,0.6,.229,2.4,4.9,.488,.472,1.2,1.7,.708,0.8,2.7,3.5,4.2,0.9,0.6,1.2,1.6,6.4
+11,Giannis Antetokounmpo,PF,29,MIL,73,73,35.2,11.5,18.8,.611,0.5,1.7,.274,11.0,17.1,.645,.624,7.0,10.7,.657,2.7,8.8,11.5,6.5,1.2,1.1,3.4,2.9,30.4
+12,Thanasis Antetokounmpo,PF,31,MIL,34,0,4.6,0.5,0.9,.533,0.0,0.0,.000,0.5,0.9,.552,.533,0.0,0.1,.000,0.2,0.2,0.4,0.5,0.2,0.1,0.4,0.7,0.9
+13,Cole Anthony,PG,23,ORL,81,0,22.4,4.1,9.4,.435,1.1,3.4,.338,3.0,6.0,.490,.496,2.2,2.7,.826,0.8,3.0,3.8,2.9,0.8,0.5,1.6,2.2,11.6
+14,OG Anunoby,SF,26,TOT,50,50,34.0,5.6,11.5,.489,2.0,5.3,.382,3.6,6.2,.581,.577,1.3,1.8,.753,0.9,3.2,4.2,2.1,1.4,0.7,1.6,2.4,14.7
+14,OG Anunoby,SF,26,TOR,27,27,33.3,5.8,11.9,.489,2.3,6.0,.374,3.6,5.9,.608,.584,1.2,1.7,.717,0.9,3.0,3.9,2.7,1.0,0.5,1.6,2.3,15.1
+14,OG Anunoby,SF,26,NYK,23,23,34.9,5.4,11.1,.488,1.8,4.5,.394,3.7,6.6,.553,.568,1.5,1.9,.791,1.0,3.4,4.4,1.5,1.7,1.0,1.7,2.5,14.1
+15,Ryan Arcidiacono,PG,29,NYK,20,0,2.3,0.0,0.3,.000,0.0,0.3,.000,0.0,0.0,,.000,0.0,0.0,,0.0,0.4,0.4,0.2,0.1,0.0,0.1,0.3,0.0
+16,Deni Avdija,SF,23,WAS,75,75,30.1,5.4,10.7,.506,1.2,3.1,.374,4.2,7.6,.560,.560,2.7,3.6,.740,1.1,6.1,7.2,3.8,0.8,0.5,2.1,2.5,14.7
+17,Deandre Ayton,C,25,POR,55,55,32.4,7.8,13.6,.570,0.0,0.2,.100,7.7,13.4,.576,.571,1.2,1.4,.823,3.2,7.9,11.1,1.6,1.0,0.8,1.8,2.0,16.7
+18,Udoka Azubuike,C,24,PHO,16,0,7.1,1.0,1.4,.696,0.0,0.0,,1.0,1.4,.696,.696,0.2,0.8,.231,0.7,1.3,2.0,0.2,0.1,0.4,0.3,1.1,2.2
+19,Ibou Badji,C,21,POR,22,1,10.3,0.6,1.0,.636,0.0,0.0,,0.6,1.0,.636,.636,0.3,0.5,.500,0.9,1.4,2.3,0.6,0.1,0.9,0.7,2.1,1.5
+20,Marvin Bagley III,C,24,TOT,50,25,21.1,4.8,8.2,.586,0.2,0.5,.391,4.6,7.7,.597,.597,1.9,2.5,.762,2.6,3.6,6.2,1.1,0.4,0.7,1.2,1.6,11.7
+20,Marvin Bagley III,C,24,DET,26,10,18.4,4.1,7.0,.591,0.0,0.2,.167,4.1,6.7,.606,.594,1.9,2.3,.820,1.8,2.8,4.5,1.0,0.2,0.5,1.1,1.5,10.2
+20,Marvin Bagley III,C,24,WAS,24,15,24.0,5.5,9.5,.581,0.3,0.7,.471,5.2,8.8,.590,.599,1.9,2.7,.708,3.5,4.6,8.1,1.2,0.6,0.8,1.3,1.7,13.3
+Rk,Player,Pos,Age,Tm,G,GS,MP,FG,FGA,FG%,3P,3PA,3P%,2P,2PA,2P%,eFG%,FT,FTA,FT%,ORB,DRB,TRB,AST,STL,BLK,TOV,PF,PTS
+21,Amari Bailey,PG,19,CHO,10,0,6.5,0.8,2.4,.333,0.1,0.8,.125,0.7,1.6,.438,.354,0.6,0.7,.857,0.6,0.3,0.9,0.7,0.3,0.0,0.4,0.2,2.3
+22,Patrick Baldwin Jr.,SF,21,WAS,38,7,13.0,1.6,4.1,.381,0.8,2.6,.320,0.7,1.5,.483,.481,0.5,0.7,.679,0.4,2.8,3.2,0.8,0.5,0.4,0.5,1.5,4.4
+23,LaMelo Ball,PG,22,CHO,22,22,32.3,8.3,19.2,.433,3.2,9.0,.355,5.1,10.3,.500,.515,4.1,4.7,.865,1.3,3.8,5.1,8.0,1.8,0.2,3.8,3.6,23.9
+24,Mo Bamba,C,25,PHI,57,17,13.0,1.7,3.5,.490,0.4,1.1,.391,1.3,2.4,.537,.553,0.6,0.9,.680,1.4,2.8,4.2,0.7,0.4,1.1,0.7,1.8,4.4
+25,Paolo Banchero,PF,21,ORL,80,80,35.0,8.0,17.6,.455,1.5,4.4,.339,6.5,13.2,.493,.497,5.1,7.0,.725,1.0,5.9,6.9,5.4,0.9,0.6,3.1,1.9,22.6
+26,Desmond Bane,SG,25,MEM,42,42,34.4,8.6,18.5,.464,3.3,8.6,.381,5.3,9.9,.536,.552,3.3,3.8,.870,0.9,3.5,4.4,5.5,1.0,0.5,2.7,2.9,23.7
+27,Dalano Banton,PG,24,TOT,54,9,19.4,3.7,9.3,.404,1.1,3.7,.296,2.6,5.6,.475,.463,1.7,2.2,.783,0.8,2.5,3.3,2.4,0.6,0.4,1.4,1.2,10.3
+27,Dalano Banton,PG,24,BOS,24,1,7.1,0.8,2.1,.373,0.1,0.7,.125,0.7,1.5,.486,.392,0.7,0.8,.800,0.5,1.0,1.5,0.8,0.2,0.1,0.4,0.8,2.3
+27,Dalano Banton,PG,24,POR,30,8,29.2,6.1,15.0,.408,1.9,6.1,.311,4.2,8.9,.474,.471,2.6,3.3,.780,1.0,3.8,4.8,3.6,0.9,0.6,2.2,1.5,16.7
+28,Dominick Barlow,PF,20,SAS,33,1,12.7,1.7,3.4,.496,0.1,0.3,.333,1.6,3.2,.510,.509,0.9,1.3,.690,1.4,2.1,3.4,1.1,0.4,0.4,0.3,1.4,4.4
+29,Harrison Barnes,PF,31,SAC,82,82,29.0,4.2,8.9,.474,1.8,4.7,.387,2.4,4.2,.571,.576,1.9,2.4,.801,0.8,2.2,3.0,1.2,0.7,0.1,0.7,1.2,12.2
+30,Scottie Barnes,SG,22,TOR,60,60,34.9,7.5,15.7,.475,1.7,4.9,.341,5.8,10.8,.536,.528,3.3,4.2,.781,2.4,5.9,8.2,6.1,1.3,1.5,2.8,2.0,19.9
+31,RJ Barrett,SG,23,TOT,58,58,31.7,7.5,15.2,.495,1.6,4.3,.360,6.0,10.9,.549,.547,3.6,5.0,.715,0.9,4.5,5.4,3.3,0.5,0.4,2.2,2.2,20.2
+31,RJ Barrett,SG,23,NYK,26,26,29.5,6.3,14.9,.423,1.7,5.0,.331,4.7,9.9,.469,.478,4.0,4.8,.831,0.7,3.5,4.3,2.4,0.5,0.3,1.8,1.7,18.2
+31,RJ Barrett,SG,23,TOR,32,32,33.5,8.5,15.4,.553,1.5,3.8,.392,7.0,11.6,.605,.601,3.3,5.2,.629,1.1,5.3,6.4,4.1,0.6,0.4,2.5,2.6,21.8
+32,Charles Bassey,C,23,SAS,19,0,10.8,1.5,2.1,.725,0.0,0.1,.000,1.5,2.1,.744,.725,0.3,0.3,.833,1.2,2.8,4.0,1.1,0.4,0.9,0.8,1.6,3.3
+33,Emoni Bates,SF,20,CLE,15,0,8.9,1.0,3.3,.306,0.7,2.2,.303,0.3,1.1,.313,.408,0.1,0.3,.250,0.1,0.9,0.9,0.7,0.1,0.1,0.5,0.3,2.7
+34,Keita Bates-Diop,SF,28,TOT,53,8,12.5,1.4,3.1,.434,0.4,1.3,.304,1.0,1.8,.526,.497,0.6,0.8,.762,0.6,1.5,2.1,0.7,0.5,0.4,0.3,0.7,3.7
+34,Keita Bates-Diop,SF,28,PHO,39,8,15.3,1.6,3.8,.427,0.5,1.6,.313,1.1,2.2,.512,.493,0.7,0.9,.722,0.8,1.8,2.6,0.9,0.6,0.5,0.3,0.8,4.5
+34,Keita Bates-Diop,SF,28,BRK,14,0,4.9,0.6,1.1,.500,0.1,0.4,.200,0.5,0.8,.636,.531,0.4,0.4,1.000,0.1,0.6,0.6,0.3,0.2,0.1,0.1,0.1,1.6
+35,Nicolas Batum,PF,35,TOT,60,38,25.5,1.9,4.3,.453,1.2,3.1,.395,0.7,1.2,.606,.596,0.3,0.4,.714,1.2,3.0,4.1,2.1,0.8,0.6,0.7,1.8,5.3
+35,Nicolas Batum,PF,35,LAC,3,0,18.0,1.0,2.7,.375,0.7,2.3,.286,0.3,0.3,1.000,.500,0.0,0.0,,0.0,2.3,2.3,1.7,1.0,1.3,0.3,1.7,2.7
+35,Nicolas Batum,PF,35,PHI,57,38,25.9,2.0,4.4,.456,1.2,3.1,.399,0.7,1.2,.600,.599,0.3,0.4,.714,1.2,3.0,4.2,2.2,0.8,0.6,0.7,1.8,5.5
+36,Darius Bazley,PF,23,TOT,9,0,16.9,2.0,3.3,.600,0.2,0.9,.250,1.8,2.4,.727,.633,1.1,1.3,.833,0.9,2.2,3.1,0.8,0.7,0.8,0.9,0.3,5.3
+36,Darius Bazley,PF,23,PHI,3,0,3.3,0.0,0.3,.000,0.0,0.0,,0.0,0.3,.000,.000,0.0,0.0,,0.0,0.3,0.3,0.7,0.0,0.0,0.3,0.0,0.0
+36,Darius Bazley,PF,23,UTA,6,0,23.7,3.0,4.8,.621,0.3,1.3,.250,2.7,3.5,.762,.655,1.7,2.0,.833,1.3,3.2,4.5,0.8,1.0,1.2,1.2,0.5,8.0
+37,Bradley Beal,SG,30,PHO,53,53,33.3,7.1,13.9,.513,1.9,4.4,.430,5.2,9.4,.552,.582,2.1,2.5,.813,1.0,3.4,4.4,5.0,1.0,0.5,2.5,2.4,18.2
+38,Malik Beasley,SG,27,MIL,79,77,29.6,4.1,9.1,.443,2.8,6.9,.413,1.2,2.3,.533,.598,0.3,0.4,.714,0.5,3.2,3.7,1.4,0.7,0.1,0.7,2.0,11.3
+39,MarJon Beauchamp,SF,23,MIL,48,1,12.7,1.7,3.5,.488,0.6,1.6,.400,1.1,1.9,.560,.578,0.4,0.6,.679,0.5,1.6,2.1,0.6,0.3,0.1,0.6,1.1,4.4
+40,Jules Bernard,SG,24,WAS,19,0,7.8,1.5,3.4,.453,0.6,1.5,.379,0.9,1.8,.514,.539,0.3,0.5,.556,0.5,0.8,1.4,0.8,0.2,0.1,0.4,0.7,3.9
+41,Dāvis Bertāns,PF,31,TOT,43,1,15.7,2.1,5.4,.393,1.8,4.7,.380,0.4,0.8,.471,.556,0.7,0.8,.909,0.2,1.2,1.4,0.8,0.6,0.3,0.4,1.3,6.7
+41,Dāvis Bertāns,PF,31,OKC,15,0,6.1,0.7,1.7,.385,0.7,1.6,.417,0.0,0.1,.000,.577,0.9,1.0,.933,0.1,0.5,0.7,0.6,0.2,0.2,0.1,0.7,2.9
+41,Dāvis Bertāns,PF,31,CHO,28,1,20.8,2.9,7.4,.394,2.4,6.3,.375,0.6,1.1,.500,.553,0.6,0.6,.889,0.3,1.6,1.8,0.9,0.8,0.3,0.6,1.6,8.8
+42,Patrick Beverley,PG,35,TOT,73,13,20.0,2.2,5.2,.417,0.8,2.5,.337,1.3,2.7,.490,.497,1.0,1.2,.822,0.7,2.6,3.3,2.9,0.6,0.4,0.9,1.8,6.2
+42,Patrick Beverley,PG,35,PHI,47,5,19.6,2.2,5.1,.432,0.7,2.3,.321,1.5,2.8,.523,.504,1.1,1.3,.810,0.6,2.4,3.1,3.1,0.5,0.4,1.0,1.8,6.3
+42,Patrick Beverley,PG,35,MIL,26,8,20.9,2.1,5.3,.391,1.0,2.8,.361,1.1,2.5,.424,.486,0.9,1.0,.852,0.7,2.9,3.6,2.6,0.7,0.5,0.6,1.7,6.0
+43,Saddiq Bey,SF,24,ATL,63,51,32.7,4.6,11.1,.416,1.8,5.7,.316,2.8,5.4,.522,.497,2.7,3.2,.837,2.7,3.9,6.5,1.5,0.8,0.2,0.9,1.4,13.7
+44,Goga Bitadze,C,24,ORL,62,33,15.4,2.0,3.4,.603,0.0,0.2,.143,2.0,3.1,.636,.608,0.9,1.4,.655,1.8,2.9,4.6,1.3,0.5,1.2,0.7,1.7,5.0
+45,Onuralp Bitim,SG,24,CHI,23,1,11.7,1.4,3.7,.381,0.5,1.9,.273,0.9,1.7,.500,.452,0.2,0.2,.800,0.3,1.1,1.4,0.6,0.1,0.1,0.4,1.0,3.5
+46,Bismack Biyombo,C,31,TOT,40,27,19.8,1.9,3.3,.565,0.0,0.0,,1.9,3.3,.565,.565,0.7,1.4,.481,1.6,3.7,5.2,1.3,0.3,0.9,1.1,2.2,4.4
+46,Bismack Biyombo,C,31,MEM,30,27,23.9,2.2,4.0,.563,0.0,0.0,,2.2,4.0,.563,.563,0.7,1.5,.478,1.9,4.5,6.4,1.7,0.3,1.1,1.3,2.7,5.2
+46,Bismack Biyombo,C,31,OKC,10,0,7.3,0.7,1.2,.583,0.0,0.0,,0.7,1.2,.583,.583,0.4,0.8,.500,0.5,1.3,1.8,0.2,0.1,0.3,0.4,0.7,1.8
+47,Anthony Black,PG,20,ORL,69,33,16.9,1.7,3.6,.466,0.5,1.4,.394,1.1,2.2,.510,.540,0.7,1.2,.613,0.5,1.5,2.0,1.3,0.5,0.3,0.8,1.6,4.6
+48,Leaky Black,SF,24,CHO,26,3,10.9,1.0,2.0,.481,0.3,0.8,.450,0.6,1.2,.500,.567,0.4,0.6,.667,0.3,1.5,1.8,0.9,0.3,0.4,0.2,0.7,2.7
+49,Buddy Boeheim,SF,24,DET,10,0,8.4,0.9,2.9,.310,0.8,2.5,.320,0.1,0.4,.250,.448,0.8,1.0,.800,0.3,0.7,1.0,0.3,0.0,0.1,0.0,0.5,3.4
+50,Bogdan Bogdanović,SG,31,ATL,79,33,30.4,6.0,13.9,.428,3.0,8.1,.374,2.9,5.8,.503,.537,1.9,2.1,.921,0.7,2.8,3.4,3.1,1.2,0.3,1.4,2.3,16.9
+51,Bojan Bogdanović,SF-PF,34,TOT,57,27,25.9,5.4,11.9,.454,2.3,5.9,.398,3.1,6.1,.507,.551,2.1,2.6,.785,0.4,2.3,2.7,1.7,0.5,0.1,1.8,1.7,15.2
+51,Bojan Bogdanović,PF,34,DET,28,27,32.9,7.1,15.2,.468,3.1,7.4,.415,4.0,7.8,.518,.569,2.9,3.7,.779,0.5,2.9,3.4,2.5,0.8,0.1,2.3,2.0,20.2
+51,Bojan Bogdanović,SF,34,NYK,29,0,19.2,3.8,8.8,.430,1.6,4.4,.370,2.2,4.4,.488,.521,1.2,1.6,.800,0.3,1.7,2.0,0.9,0.2,0.0,1.3,1.3,10.4
+52,Bol Bol,PF,24,PHO,43,0,10.9,2.2,3.5,.616,0.5,1.2,.423,1.7,2.3,.717,.689,0.3,0.4,.789,0.7,2.4,3.2,0.4,0.2,0.6,0.4,1.0,5.2
+53,Marques Bolden,C,25,TOT,11,2,11.0,1.5,2.3,.680,0.0,0.1,.000,1.5,2.2,.708,.680,0.3,0.4,.750,0.9,2.2,3.1,0.4,0.3,0.6,0.3,0.9,3.4
+53,Marques Bolden,C,25,MIL,2,0,1.5,0.0,0.0,,0.0,0.0,,0.0,0.0,,,0.0,0.0,,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.5,0.0
+53,Marques Bolden,C,25,CHO,9,2,13.1,1.9,2.8,.680,0.0,0.1,.000,1.9,2.7,.708,.680,0.3,0.4,.750,1.1,2.4,3.6,0.4,0.3,0.8,0.3,1.0,4.1
+54,Devin Booker,PG,27,PHO,68,68,36.0,9.4,19.2,.492,2.2,6.1,.364,7.2,13.0,.552,.550,6.0,6.7,.886,0.8,3.7,4.5,6.9,0.9,0.4,2.6,3.0,27.1
+55,Brandon Boston Jr.,SF,22,LAC,32,0,10.8,2.0,5.0,.404,0.4,1.6,.269,1.6,3.4,.468,.447,0.7,1.0,.697,0.6,1.0,1.6,0.4,0.3,0.3,0.8,1.3,5.2
+56,Chris Boucher,PF,31,TOR,50,0,14.1,2.2,4.3,.507,0.6,1.9,.330,1.6,2.5,.642,.578,1.4,1.8,.772,1.2,2.9,4.1,0.5,0.3,0.5,0.6,1.4,6.4
+57,James Bouknight,SG,23,CHO,14,0,5.8,1.3,2.9,.439,0.9,2.1,.433,0.4,0.8,.455,.598,0.1,0.1,.500,0.1,0.6,0.6,0.4,0.1,0.1,0.1,0.2,3.6
+58,Jamaree Bouyea,PG,24,TOT,9,0,10.6,1.1,3.1,.357,0.1,1.0,.111,1.0,2.1,.474,.375,0.0,0.0,,0.3,1.8,2.1,1.2,0.1,0.0,1.0,1.1,2.3
+58,Jamaree Bouyea,PG,24,POR,6,0,9.5,0.8,3.5,.238,0.0,1.3,.000,0.8,2.2,.385,.238,0.0,0.0,,0.3,1.3,1.7,1.3,0.0,0.0,1.3,0.5,1.7
+58,Jamaree Bouyea,PG,24,SAS,3,0,12.7,1.7,2.3,.714,0.3,0.3,1.000,1.3,2.0,.667,.786,0.0,0.0,,0.3,2.7,3.0,1.0,0.3,0.0,0.3,2.3,3.7
+59,Malaki Branham,PG,20,SAS,75,29,21.3,3.6,8.4,.432,1.2,3.6,.347,2.4,4.8,.494,.506,0.7,0.8,.873,0.4,1.6,2.0,2.1,0.4,0.1,1.2,1.3,9.2
+60,Christian Braun,SG,22,DEN,82,4,20.2,2.7,5.9,.460,0.8,2.0,.384,1.9,3.9,.498,.525,1.1,1.6,.694,1.0,2.8,3.7,1.6,0.5,0.4,0.7,1.7,7.3
+61,Mikal Bridges,SF,27,BRK,82,82,34.8,6.9,15.8,.436,2.7,7.2,.372,4.2,8.6,.490,.521,3.1,3.9,.814,0.8,3.7,4.5,3.6,1.0,0.4,2.0,1.4,19.6
+62,Miles Bridges,SF,25,CHO,69,67,37.4,8.1,17.5,.462,2.3,6.5,.349,5.8,11.0,.529,.527,2.5,3.1,.825,1.0,6.3,7.3,3.3,0.9,0.5,2.0,1.7,21.0
+63,Oshae Brissett,SF,25,BOS,55,1,11.5,1.2,2.8,.444,0.3,1.0,.273,1.0,1.8,.541,.493,0.9,1.5,.602,1.1,1.8,2.9,0.8,0.3,0.1,0.4,1.0,3.7
+64,Izaiah Brockington,PG,24,NOP,1,0,3.0,2.0,5.0,.400,0.0,1.0,.000,2.0,4.0,.500,.400,0.0,0.0,,1.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,4.0
+65,Malcolm Brogdon,PG,31,POR,39,25,28.7,5.6,12.7,.440,2.1,5.1,.412,3.5,7.6,.458,.522,2.4,3.0,.819,0.7,3.1,3.8,5.5,0.7,0.2,1.5,1.5,15.7
+66,Armoni Brooks,PG,25,BRK,10,0,10.4,1.5,4.6,.326,1.1,3.2,.344,0.4,1.4,.286,.446,0.1,0.2,.500,0.7,1.1,1.8,0.5,0.1,0.1,0.3,0.3,4.2
+67,Dillon Brooks,SF,28,HOU,72,72,30.9,4.6,10.7,.428,1.8,5.1,.359,2.7,5.5,.492,.515,1.7,2.0,.844,0.7,2.7,3.4,1.7,0.9,0.1,1.2,3.3,12.7
+68,Bruce Brown,PG-SG,27,TOT,67,44,27.9,4.1,8.7,.478,0.8,2.4,.323,3.4,6.2,.538,.523,1.7,2.1,.824,0.8,3.4,4.2,2.9,0.9,0.3,1.3,2.2,10.8
+68,Bruce Brown,SG,27,IND,33,33,29.7,4.5,9.5,.475,1.0,3.1,.327,3.5,6.5,.545,.527,2.0,2.5,.817,0.8,3.9,4.7,3.0,1.1,0.2,1.2,2.5,12.1
+68,Bruce Brown,PG,27,TOR,34,11,26.0,3.8,7.9,.481,0.6,1.9,.317,3.2,6.0,.532,.519,1.5,1.8,.833,0.8,3.0,3.8,2.7,0.7,0.3,1.3,2.0,9.6
+69,Charlie Brown Jr.,SG,26,NYK,8,0,4.6,0.3,1.3,.200,0.3,0.9,.286,0.0,0.4,.000,.300,0.0,0.0,,0.1,0.1,0.3,0.0,0.0,0.3,0.3,0.6,0.8
+70,Greg Brown III,PF,22,DAL,6,0,6.7,0.8,1.8,.455,0.2,0.5,.333,0.7,1.3,.500,.500,0.7,1.5,.444,0.5,1.0,1.5,0.7,0.0,0.7,0.7,0.8,2.5
+71,Jaylen Brown,SF,27,BOS,70,70,33.5,9.0,17.9,.499,2.1,5.9,.354,6.9,12.1,.570,.557,3.0,4.3,.703,1.2,4.3,5.5,3.6,1.2,0.5,2.4,2.6,23.0
+72,Kendall Brown,SG,20,IND,15,0,4.2,0.5,1.0,.533,0.0,0.1,.000,0.5,0.9,.615,.533,0.3,0.5,.625,0.2,0.1,0.3,0.3,0.0,0.0,0.1,0.6,1.4
+73,Kobe Brown,PF,24,LAC,44,0,9.0,0.8,2.0,.411,0.3,1.1,.292,0.5,1.0,.548,.489,0.0,0.0,.500,0.5,1.0,1.4,0.6,0.3,0.1,0.2,1.1,2.0
+74,Moses Brown,C,24,POR,22,5,9.1,1.5,3.0,.508,0.0,0.0,,1.5,3.0,.508,.508,0.4,1.4,.290,1.7,2.2,3.9,0.3,0.2,0.3,0.5,1.2,3.4
+75,Troy Brown Jr.,SF,24,TOT,59,15,14.0,1.4,3.8,.372,0.8,2.4,.333,0.6,1.4,.435,.476,0.5,0.6,.865,0.4,2.0,2.4,1.0,0.4,0.1,0.5,0.7,4.2
+75,Troy Brown Jr.,SF,24,MIN,37,3,11.1,1.4,3.2,.441,0.8,2.3,.369,0.6,0.9,.618,.572,0.5,0.6,.864,0.4,1.6,1.9,0.9,0.2,0.1,0.5,0.7,4.2
+75,Troy Brown Jr.,SF,24,DET,22,12,19.0,1.5,4.9,.296,0.7,2.6,.281,0.7,2.3,.314,.370,0.6,0.7,.867,0.5,2.8,3.3,1.1,0.7,0.0,0.5,0.7,4.2
+76,Jalen Brunson,PG,27,NYK,77,77,35.4,10.3,21.4,.479,2.7,6.8,.401,7.5,14.6,.516,.543,5.5,6.5,.847,0.6,3.1,3.6,6.7,0.9,0.2,2.4,1.9,28.7
+77,Thomas Bryant,C,26,MIA,38,4,11.6,2.3,3.9,.577,0.1,0.6,.182,2.2,3.3,.646,.591,1.1,1.2,.872,1.1,2.7,3.7,0.6,0.3,0.4,0.6,1.2,5.7
+78,Kobe Bufkin,SG,20,ATL,17,0,11.5,2.0,5.4,.370,0.5,2.4,.225,1.5,3.1,.481,.418,0.2,0.5,.500,0.4,1.6,1.9,1.6,0.4,0.3,0.6,1.2,4.8
+79,Reggie Bullock,SF,32,HOU,44,0,9.5,0.8,1.9,.415,0.6,1.5,.403,0.2,0.3,.467,.579,0.0,0.0,1.000,0.1,1.5,1.7,0.3,0.3,0.1,0.2,0.7,2.2
+80,Alec Burks,SG,32,TOT,66,1,18.4,3.2,8.6,.369,1.9,5.0,.376,1.3,3.6,.360,.478,2.2,2.5,.868,0.4,1.9,2.3,1.3,0.4,0.2,0.7,1.1,10.4
+80,Alec Burks,SG,32,DET,43,0,21.0,3.7,9.4,.394,2.3,5.7,.401,1.4,3.7,.384,.516,2.8,3.1,.903,0.4,2.2,2.6,1.6,0.5,0.3,0.7,1.5,12.6
+80,Alec Burks,SG,32,NYK,23,1,13.5,2.2,7.1,.307,1.1,3.6,.301,1.1,3.5,.313,.383,1.0,1.4,.727,0.3,1.4,1.7,0.9,0.3,0.0,0.7,0.5,6.5
+81,Jared Butler,SG,23,WAS,40,0,14.2,2.5,5.0,.488,0.6,2.0,.308,1.9,3.1,.602,.547,0.8,0.9,.861,0.2,1.2,1.5,3.2,0.7,0.2,1.3,1.5,6.3
+82,Jimmy Butler,PF,34,MIA,60,60,34.0,6.6,13.2,.499,1.0,2.4,.414,5.6,10.8,.519,.537,6.6,7.7,.858,1.8,3.6,5.3,5.0,1.3,0.3,1.7,1.1,20.8
+83,Jamal Cain,PF,24,MIA,26,1,10.0,1.3,3.0,.430,0.4,1.2,.355,0.9,1.8,.479,.500,0.7,0.9,.783,0.6,0.8,1.4,0.4,0.4,0.2,0.3,0.8,3.7
+84,Kentavious Caldwell-Pope,SG,30,DEN,76,76,31.6,3.6,7.7,.460,1.6,4.1,.406,1.9,3.7,.520,.566,1.3,1.5,.894,0.4,2.0,2.4,2.4,1.3,0.6,1.0,2.0,10.1
+85,Toumani Camara,PF,23,POR,70,49,24.8,2.8,6.3,.450,0.8,2.5,.337,2.0,3.8,.523,.516,1.1,1.4,.758,2.0,2.9,4.9,1.2,0.9,0.5,1.2,2.5,7.5
+86,Clint Capela,C,29,ATL,73,73,25.8,4.8,8.5,.571,0.0,0.0,.000,4.8,8.5,.572,.571,1.8,2.8,.631,4.6,6.0,10.6,1.2,0.6,1.5,1.0,2.2,11.5
+87,Jevon Carter,PG,28,CHI,72,0,13.9,1.9,5.1,.378,1.1,3.3,.329,0.8,1.8,.466,.484,0.1,0.1,.571,0.2,0.7,0.8,1.3,0.5,0.2,0.5,1.0,5.0
+88,Wendell Carter Jr.,C,24,ORL,55,48,25.6,4.1,7.7,.525,1.2,3.1,.374,2.9,4.6,.626,.600,1.7,2.4,.694,2.0,4.9,6.9,1.7,0.6,0.5,1.2,2.2,11.0
+89,D.J. Carton,SG,23,TOR,4,0,9.0,0.8,2.0,.375,0.3,1.0,.250,0.5,1.0,.500,.438,1.0,1.3,.800,0.5,0.5,1.0,0.8,0.3,0.0,0.3,0.8,2.8
+90,Alex Caruso,SG,29,CHI,71,57,28.7,3.6,7.6,.468,1.9,4.7,.408,1.6,2.9,.563,.593,1.0,1.4,.760,0.9,3.0,3.8,3.5,1.7,1.0,1.4,2.5,10.1
+91,Colin Castleton,C,23,LAL,16,0,3.7,0.6,1.0,.563,0.0,0.0,,0.6,1.0,.563,.563,0.4,0.4,1.000,0.4,0.4,0.8,0.2,0.1,0.0,0.2,0.7,1.5
+92,Malcolm Cazalon,SG,22,DET,1,0,3.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+93,Julian Champagnie,SF,22,SAS,74,59,19.8,2.2,5.4,.408,1.4,3.7,.365,0.9,1.7,.504,.534,1.0,1.2,.815,0.6,2.2,2.8,1.4,0.6,0.6,0.8,1.0,6.8
+94,Justin Champagnie,SF,22,WAS,15,1,15.7,2.1,5.2,.410,0.9,3.0,.289,1.3,2.2,.576,.494,0.8,1.0,.800,1.3,2.3,3.5,1.3,0.7,0.6,0.7,0.9,5.9
+95,Max Christie,SG,20,LAL,67,7,14.1,1.5,3.6,.427,0.6,1.8,.356,0.9,1.8,.496,.515,0.5,0.7,.783,0.2,1.9,2.1,0.9,0.3,0.3,0.5,0.9,4.2
+96,Sidy Cissoko,SF,19,SAS,12,0,11.8,1.3,2.8,.485,0.1,1.0,.083,1.3,1.8,.714,.500,1.0,1.3,.800,0.7,1.2,1.8,0.8,0.6,0.3,0.3,1.5,3.8
+97,Brandon Clarke,PF,27,MEM,6,1,22.3,5.5,9.8,.559,0.2,1.0,.167,5.3,8.8,.604,.568,0.2,1.2,.143,2.2,3.2,5.3,1.5,0.8,1.0,0.5,1.3,11.3
+98,Jordan Clarkson,SG,31,UTA,55,19,30.6,6.1,14.8,.413,1.7,5.6,.294,4.5,9.2,.485,.469,3.2,3.7,.881,0.9,2.5,3.4,5.0,0.6,0.1,2.7,1.5,17.1
+99,Nic Claxton,C,24,BRK,71,71,29.8,5.2,8.2,.629,0.0,0.1,.200,5.1,8.1,.633,.630,1.5,2.8,.551,2.7,7.2,9.9,2.1,0.6,2.1,1.3,2.5,11.8
+100,Noah Clowney,PF,19,BRK,23,4,16.1,2.2,4.0,.538,0.5,1.4,.364,1.7,2.6,.633,.602,0.9,1.3,.700,1.1,2.4,3.5,0.8,0.3,0.7,0.7,1.1,5.8
+101,Amir Coffey,SF,26,LAC,70,13,20.9,2.4,5.0,.472,1.0,2.6,.380,1.4,2.4,.571,.571,0.9,1.0,.859,0.4,1.7,2.1,1.1,0.6,0.2,0.5,1.5,6.6
+102,John Collins,PF,26,UTA,68,66,28.0,5.8,11.0,.532,1.3,3.4,.371,4.6,7.6,.605,.590,2.2,2.7,.795,2.3,6.1,8.5,1.1,0.6,0.9,1.4,2.8,15.1
+103,Zach Collins,C,26,SAS,69,29,22.1,4.3,8.9,.484,0.8,2.6,.320,3.5,6.3,.552,.531,1.8,2.3,.753,1.7,3.7,5.4,2.8,0.5,0.8,1.9,3.0,11.2
+104,Mike Conley,PG,36,MIN,76,76,28.9,3.9,8.5,.457,2.4,5.3,.442,1.5,3.1,.481,.595,1.3,1.5,.911,0.5,2.4,2.9,5.9,1.2,0.2,1.3,1.7,11.4
+105,Pat Connaughton,SF,31,MIL,76,3,22.1,2.0,4.6,.435,1.1,3.1,.345,0.9,1.4,.633,.553,0.6,0.8,.759,0.7,2.5,3.1,2.1,0.5,0.3,0.6,1.3,5.6
+106,Bilal Coulibaly,SF,19,WAS,63,15,27.2,3.0,6.9,.435,1.0,2.9,.346,2.0,3.9,.502,.509,1.5,2.1,.702,0.9,3.2,4.1,1.7,0.9,0.8,1.4,2.2,8.4
+107,Ricky Council IV,SG,22,PHI,32,0,9.0,1.7,3.5,.482,0.4,1.0,.375,1.3,2.5,.525,.536,1.7,2.2,.746,0.4,1.0,1.4,0.5,0.3,0.0,0.2,0.5,5.4
+108,Robert Covington,SF-PF,33,TOT,29,6,16.8,1.5,3.4,.439,0.7,1.9,.339,0.8,1.4,.571,.536,0.8,0.9,.846,1.1,2.3,3.3,0.8,1.3,0.6,0.4,1.9,4.4
+108,Robert Covington,PF,33,LAC,3,3,23.0,1.0,3.0,.333,0.7,2.7,.250,0.3,0.3,1.000,.444,0.3,0.7,.500,0.7,2.0,2.7,2.3,2.0,0.7,0.0,2.3,3.0
+108,Robert Covington,SF,33,PHI,26,3,16.1,1.5,3.4,.449,0.7,1.8,.354,0.9,1.6,.561,.545,0.8,0.9,.875,1.1,2.3,3.4,0.7,1.3,0.6,0.4,1.9,4.5
+109,Torrey Craig,SF,33,CHI,53,14,19.8,2.0,4.7,.430,1.1,2.9,.392,0.9,1.8,.490,.550,0.5,0.7,.750,1.3,2.8,4.1,1.1,0.6,0.4,0.6,2.1,5.7
+110,Jae Crowder,PF,33,MIL,50,25,23.1,2.3,5.4,.422,1.2,3.3,.349,1.1,2.1,.538,.530,0.5,0.7,.722,0.6,2.6,3.2,1.3,0.8,0.2,0.5,1.5,6.2
+111,Jalen Crutcher,PG,24,NOP,1,0,3.0,0.0,1.0,.000,0.0,0.0,,0.0,1.0,.000,.000,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+112,Cade Cunningham,PG,22,DET,62,62,33.5,8.5,18.8,.449,1.9,5.4,.355,6.5,13.4,.487,.500,3.8,4.4,.869,0.5,3.8,4.3,7.5,0.9,0.4,3.4,2.5,22.7
+113,Seth Curry,SG,33,TOT,44,4,14.0,1.8,4.6,.392,0.9,2.5,.352,1.0,2.2,.438,.485,0.6,0.7,.903,0.4,1.1,1.5,1.0,0.5,0.1,0.5,0.9,5.1
+113,Seth Curry,SG,33,DAL,36,3,12.7,1.5,4.0,.372,0.8,2.2,.363,0.7,1.8,.385,.472,0.5,0.5,.895,0.4,1.0,1.4,0.8,0.5,0.1,0.4,0.9,4.3
+113,Seth Curry,SG,33,CHO,8,1,19.9,3.3,7.4,.441,1.1,3.5,.321,2.1,3.9,.548,.517,1.4,1.5,.917,0.4,1.6,2.0,1.8,0.6,0.4,0.6,0.9,9.0
+114,Stephen Curry,PG,35,GSW,74,74,32.7,8.8,19.5,.450,4.8,11.8,.408,4.0,7.7,.515,.573,4.0,4.4,.923,0.5,4.0,4.5,5.1,0.7,0.4,2.8,1.6,26.4
+115,Dyson Daniels,PG,20,NOP,61,16,22.3,2.3,5.1,.447,0.7,2.2,.311,1.6,2.9,.551,.514,0.6,0.9,.642,1.0,2.9,3.9,2.7,1.4,0.4,1.0,1.7,5.8
+116,Anthony Davis,C,30,LAL,76,76,35.5,9.4,16.9,.556,0.4,1.4,.271,9.0,15.5,.582,.567,5.5,6.8,.816,3.1,9.5,12.6,3.5,1.2,2.3,2.1,2.3,24.7
+117,Johnny Davis,SG,21,WAS,50,6,12.3,1.2,3.1,.403,0.3,0.8,.350,1.0,2.3,.421,.448,0.3,0.5,.583,0.4,1.1,1.4,0.6,0.4,0.2,0.3,1.1,3.0
+118,JD Davison,SG,21,BOS,8,0,4.9,0.6,1.5,.417,0.4,0.9,.429,0.3,0.6,.400,.542,0.4,0.5,.750,0.3,1.0,1.3,1.3,0.1,0.1,0.3,0.5,2.0
+119,Dexter Dennis,SG,24,DAL,4,0,7.5,2.5,4.8,.526,0.3,2.0,.125,2.3,2.8,.818,.553,0.3,0.8,.333,0.3,2.0,2.3,1.0,0.0,0.3,0.8,0.3,5.5
+120,DeMar DeRozan,SF,34,CHI,79,79,37.8,8.2,17.2,.480,0.9,2.8,.333,7.3,14.3,.509,.507,6.6,7.7,.853,0.5,3.8,4.3,5.3,1.1,0.6,1.7,2.0,24.0
+121,Moussa Diabaté,C,22,LAC,11,0,5.8,0.9,1.7,.526,0.0,0.0,,0.9,1.7,.526,.526,0.8,1.3,.643,1.4,0.8,2.2,0.4,0.5,0.1,0.2,0.4,2.6
+122,Mamadi Diakite,PF,27,TOT,6,0,4.0,0.7,1.0,.667,0.0,0.2,.000,0.7,0.8,.800,.667,0.7,1.0,.667,0.2,0.5,0.7,0.3,0.2,0.2,0.2,0.3,2.0
+122,Mamadi Diakite,PF,27,SAS,3,0,5.3,1.3,1.7,.800,0.0,0.0,,1.3,1.7,.800,.800,1.3,2.0,.667,0.3,0.7,1.0,0.7,0.0,0.3,0.0,0.3,4.0
+122,Mamadi Diakite,PF,27,NYK,3,0,2.7,0.0,0.3,.000,0.0,0.3,.000,0.0,0.0,,.000,0.0,0.0,,0.0,0.3,0.3,0.0,0.3,0.0,0.3,0.3,0.0
+123,Hamidou Diallo,SG,25,WAS,2,0,2.5,0.5,1.0,.500,0.0,0.0,,0.5,1.0,.500,.500,0.0,0.0,,1.0,0.0,1.0,0.5,1.0,0.0,0.5,0.5,1.0
+124,Gradey Dick,SG,20,TOR,60,17,21.1,3.2,7.4,.425,1.5,4.0,.365,1.7,3.4,.495,.524,0.7,0.9,.863,0.5,1.8,2.2,1.1,0.6,0.0,0.8,1.5,8.5
+125,Ousmane Dieng,PF,20,OKC,33,0,11.1,1.5,3.5,.422,0.6,2.1,.300,0.8,1.4,.609,.513,0.4,0.5,.875,0.3,1.2,1.5,1.1,0.2,0.2,0.6,0.7,4.0
+126,Spencer Dinwiddie,PG,30,TOT,76,52,28.3,3.3,8.4,.392,1.6,4.8,.337,1.7,3.6,.467,.489,2.2,2.8,.805,0.4,2.3,2.7,4.7,0.7,0.3,1.2,1.8,10.5
+126,Spencer Dinwiddie,PG,30,BRK,48,48,30.7,4.1,10.4,.391,1.9,5.8,.320,2.2,4.6,.480,.480,2.6,3.3,.781,0.5,2.8,3.3,6.0,0.8,0.2,1.3,1.8,12.6
+126,Spencer Dinwiddie,PG,30,LAL,28,4,24.2,2.0,5.0,.397,1.3,3.2,.389,0.8,1.8,.412,.521,1.6,1.8,.880,0.1,1.5,1.7,2.4,0.5,0.5,1.0,1.9,6.8
+127,Donte DiVincenzo,SG,27,NYK,81,63,29.1,5.5,12.4,.443,3.5,8.7,.401,2.0,3.7,.542,.584,1.1,1.4,.754,0.8,2.9,3.7,2.7,1.3,0.4,1.4,2.0,15.5
+128,Luka Dončić,PG,24,DAL,70,70,37.5,11.5,23.6,.487,4.1,10.6,.382,7.4,13.0,.573,.573,6.8,8.7,.786,0.8,8.4,9.2,9.8,1.4,0.5,4.0,2.1,33.9
+129,Luguentz Dort,SF,24,OKC,79,79,28.4,3.6,8.3,.438,2.0,5.0,.394,1.7,3.3,.506,.557,1.6,2.0,.826,0.9,2.7,3.6,1.4,0.9,0.6,0.9,2.9,10.9
+130,Ayo Dosunmu,SG,24,CHI,76,37,29.1,4.7,9.5,.501,1.6,3.9,.403,3.2,5.6,.568,.583,1.1,1.4,.810,0.7,2.1,2.8,3.2,0.9,0.5,1.4,2.2,12.2
+131,Jeff Dowtin,PG,26,PHI,12,0,11.8,1.7,3.0,.556,0.8,1.6,.474,0.9,1.4,.647,.681,0.3,0.3,.750,0.3,1.4,1.7,2.3,0.6,0.1,0.4,0.1,4.3
+132,Henri Drell,SF,23,CHI,4,0,7.5,1.0,2.5,.400,0.5,1.5,.333,0.5,1.0,.500,.500,0.3,0.5,.500,0.3,0.8,1.0,1.0,0.5,0.3,0.5,1.5,2.8
+133,Andre Drummond,C,30,CHI,79,10,17.1,3.4,6.1,.556,0.0,0.0,.000,3.4,6.0,.560,.556,1.6,2.8,.592,3.4,5.6,9.0,0.5,0.9,0.6,1.0,1.8,8.4
+134,Chris Duarte,SF,26,SAC,59,11,12.2,1.4,3.6,.381,0.7,2.2,.346,0.6,1.4,.434,.486,0.4,0.6,.788,0.4,1.4,1.8,0.7,0.5,0.1,0.4,1.4,3.9
+135,David Duke Jr.,SG,24,SAS,4,0,12.8,2.5,4.5,.556,0.8,1.5,.500,1.8,3.0,.583,.639,0.8,0.8,1.000,0.5,2.0,2.5,1.3,0.5,0.0,0.8,1.0,6.5
+136,Kris Dunn,PG,29,UTA,66,32,18.9,2.2,4.7,.470,0.6,1.7,.369,1.6,3.1,.525,.535,0.3,0.5,.688,0.8,2.1,2.9,3.8,1.0,0.4,1.2,2.0,5.4
+137,Kevin Durant,PF,35,PHO,75,75,37.2,10.0,19.1,.523,2.2,5.4,.413,7.8,13.7,.567,.581,4.8,5.6,.856,0.5,6.1,6.6,5.0,0.9,1.2,3.3,1.8,27.1
+138,Jalen Duren,C,20,DET,61,60,29.1,5.7,9.2,.619,0.0,0.1,.000,5.7,9.2,.623,.619,2.3,3.0,.790,3.1,8.5,11.6,2.4,0.5,0.8,2.1,3.1,13.8
+139,Tari Eason,PF,22,HOU,22,0,21.8,4.0,8.6,.466,0.8,2.3,.360,3.2,6.3,.504,.513,1.0,1.5,.636,2.1,4.9,7.0,1.2,1.4,0.9,0.9,2.3,9.8
+140,Anthony Edwards,SG,22,MIN,79,78,35.1,9.1,19.7,.461,2.4,6.7,.357,6.7,13.0,.515,.522,5.4,6.4,.836,0.7,4.8,5.4,5.1,1.3,0.5,3.1,1.8,25.9
+141,Kessler Edwards,SF,23,SAC,54,0,5.1,0.6,1.5,.415,0.4,1.0,.385,0.3,0.6,.467,.537,0.1,0.2,.556,0.2,0.6,0.8,0.3,0.2,0.1,0.1,0.5,1.7
+142,Keon Ellis,SG,24,SAC,57,21,17.2,1.9,4.1,.461,1.2,2.9,.417,0.6,1.1,.578,.612,0.5,0.6,.743,0.6,1.6,2.2,1.5,0.9,0.5,0.6,2.0,5.4
+143,Joel Embiid,C,29,PHI,39,39,33.6,11.5,21.8,.529,1.4,3.6,.388,10.2,18.3,.556,.561,10.2,11.6,.883,2.4,8.6,11.0,5.6,1.2,1.7,3.8,2.9,34.7
+144,Drew Eubanks,C,26,PHO,75,6,15.6,2.0,3.4,.601,0.0,0.0,1.000,2.0,3.3,.596,.607,1.0,1.2,.774,1.4,2.9,4.3,0.8,0.4,0.8,0.8,1.5,5.1
+145,Tosan Evbuomwan,SF,22,TOT,17,8,21.6,2.1,4.2,.507,0.7,1.9,.375,1.4,2.3,.615,.592,1.0,1.5,.680,1.1,2.5,3.5,0.9,0.4,0.3,0.5,1.4,5.9
+145,Tosan Evbuomwan,SF,22,MEM,4,0,18.5,1.0,3.8,.267,0.5,2.0,.250,0.5,1.8,.286,.333,0.0,0.0,,2.0,1.5,3.5,1.5,0.0,0.3,0.3,1.3,2.5
+145,Tosan Evbuomwan,SF,22,DET,13,8,22.5,2.5,4.3,.571,0.8,1.8,.417,1.7,2.5,.688,.661,1.3,1.9,.680,0.8,2.8,3.5,0.8,0.5,0.3,0.6,1.4,7.0
+146,Dante Exum,PG,28,DAL,55,17,19.8,2.9,5.5,.533,1.0,2.0,.491,2.0,3.5,.557,.621,1.0,1.2,.779,0.5,2.3,2.7,2.9,0.4,0.1,0.9,1.5,7.8
+147,Bruno Fernando,C,25,ATL,45,2,15.2,2.4,4.2,.583,0.0,0.1,.000,2.4,4.1,.596,.583,1.4,2.1,.667,1.3,3.0,4.3,1.0,0.6,0.6,1.0,2.4,6.3
+148,Dorian Finney-Smith,PF,30,BRK,68,56,28.4,3.2,7.5,.421,1.7,4.9,.348,1.4,2.6,.560,.535,0.5,0.7,.717,1.6,3.1,4.7,1.6,0.8,0.6,0.9,2.2,8.5
+149,Adam Flagler,SG,24,OKC,2,0,7.0,0.5,3.5,.143,0.5,3.0,.167,0.0,0.5,.000,.214,0.0,0.0,,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,1.5
+150,Malachi Flynn,PG,25,TOT,69,0,12.7,2.0,4.7,.418,0.8,2.4,.331,1.2,2.3,.513,.505,0.8,1.0,.732,0.3,1.3,1.7,1.9,0.6,0.1,0.9,1.0,5.5
+150,Malachi Flynn,PG,25,TOR,31,0,15.3,1.8,4.4,.409,0.9,2.6,.350,0.9,1.8,.491,.511,0.5,0.7,.773,0.4,1.7,2.1,2.4,0.6,0.2,1.1,1.4,5.1
+150,Malachi Flynn,PG,25,NYK,14,0,4.3,0.6,1.6,.391,0.3,0.9,.308,0.4,0.7,.500,.478,0.6,0.8,.818,0.0,0.4,0.4,0.4,0.1,0.0,0.2,0.2,2.2
+150,Malachi Flynn,PG,25,DET,24,0,14.3,3.0,6.9,.430,1.0,3.2,.316,2.0,3.7,.528,.503,1.1,1.6,.684,0.3,1.5,1.8,2.1,0.8,0.1,1.1,0.9,8.0
+151,Simone Fontecchio,SF,28,TOT,66,43,24.9,3.8,8.3,.460,2.0,5.1,.401,1.8,3.2,.551,.582,0.8,1.0,.818,1.0,2.8,3.7,1.5,0.7,0.3,1.0,1.7,10.5
+151,Simone Fontecchio,SF,28,UTA,50,34,23.2,3.2,7.2,.450,1.8,4.7,.391,1.4,2.5,.560,.577,0.6,0.8,.800,1.0,2.5,3.5,1.5,0.6,0.4,1.0,1.6,8.9
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+152,Jordan Ford,PG,25,SAC,6,0,3.7,0.7,1.2,.571,0.3,0.5,.667,0.3,0.7,.500,.714,0.3,0.3,1.000,0.0,0.3,0.3,0.3,0.0,0.0,0.2,0.0,2.0
+153,Trent Forrest,SG,25,ATL,38,0,10.9,0.9,2.4,.378,0.1,0.3,.200,0.8,2.1,.400,.389,0.3,0.4,.765,0.3,1.1,1.3,2.4,0.3,0.1,0.5,0.7,2.2
+154,Evan Fournier,SG,31,TOT,32,0,18.1,2.5,6.9,.357,1.0,4.1,.254,1.4,2.8,.505,.432,0.9,1.1,.806,0.2,1.7,1.8,1.5,0.9,0.2,0.7,1.5,6.9
+154,Evan Fournier,SG,31,NYK,3,0,13.0,1.3,6.7,.200,0.7,5.0,.133,0.7,1.7,.400,.250,0.7,0.7,1.000,0.0,1.3,1.3,1.0,1.3,0.0,0.3,1.3,4.0
+154,Evan Fournier,SG,31,DET,29,0,18.7,2.6,6.9,.373,1.1,4.0,.270,1.5,3.0,.512,.450,0.9,1.2,.794,0.2,1.7,1.9,1.6,0.9,0.2,0.7,1.5,7.2
+155,De'Aaron Fox,PG,26,SAC,74,74,35.9,9.7,20.9,.465,2.9,7.8,.369,6.8,13.1,.522,.534,4.2,5.7,.738,0.9,3.7,4.6,5.6,2.0,0.4,2.6,2.6,26.6
+156,Javon Freeman-Liberty,PG,24,TOR,22,6,18.3,2.5,5.6,.444,0.5,1.9,.238,2.0,3.7,.549,.484,1.5,1.6,.917,0.7,2.5,3.2,1.8,0.5,0.2,1.1,1.3,7.0
+157,Alex Fudge,SF,20,TOT,6,0,6.7,1.0,2.5,.400,0.2,0.8,.200,0.8,1.7,.500,.433,0.3,0.3,1.000,0.7,0.2,0.8,0.0,0.5,0.0,0.3,0.3,2.5
+157,Alex Fudge,SF,20,LAL,4,0,3.5,0.3,1.5,.167,0.0,0.5,.000,0.3,1.0,.250,.167,0.5,0.5,1.000,0.5,0.0,0.5,0.0,0.0,0.0,0.3,0.3,1.0
+157,Alex Fudge,SF,20,DAL,2,0,13.0,2.5,4.5,.556,0.5,1.5,.333,2.0,3.0,.667,.611,0.0,0.0,,1.0,0.5,1.5,0.0,1.5,0.0,0.5,0.5,5.5
+158,Markelle Fultz,PG,25,ORL,43,18,21.2,3.6,7.6,.472,0.1,0.4,.222,3.5,7.2,.487,.479,0.5,0.8,.697,1.3,1.9,3.2,2.8,1.0,0.3,1.2,1.5,7.8
+159,Andrew Funk,SG,24,CHI,5,0,2.6,0.0,0.8,.000,0.0,0.6,.000,0.0,0.2,.000,.000,0.0,0.0,,0.0,0.0,0.0,0.0,0.2,0.2,0.0,0.0,0.0
+160,Wenyen Gabriel,PF,26,MEM,5,0,16.2,1.6,4.4,.364,0.2,1.2,.167,1.4,3.2,.438,.386,0.0,1.0,.000,1.4,3.6,5.0,0.6,0.4,0.4,1.6,2.0,3.4
+161,Daniel Gafford,C-PF,25,TOT,74,66,24.5,4.7,6.5,.725,0.0,0.0,,4.7,6.5,.725,.725,1.6,2.4,.674,2.6,5.0,7.6,1.6,0.9,2.1,1.0,3.1,11.0
+161,Daniel Gafford,C,25,WAS,45,45,26.5,4.5,6.5,.690,0.0,0.0,,4.5,6.5,.690,.690,1.9,2.6,.706,2.9,5.1,8.0,1.5,1.0,2.2,1.0,3.3,10.9
+161,Daniel Gafford,PF,25,DAL,29,21,21.5,5.0,6.4,.780,0.0,0.0,,5.0,6.4,.780,.780,1.2,1.9,.607,2.2,4.7,6.9,1.6,0.7,1.9,1.0,2.8,11.2
+162,Danilo Gallinari,PF-SF,35,TOT,49,0,12.9,1.7,3.9,.437,0.6,1.9,.323,1.1,2.0,.546,.516,1.7,2.0,.857,0.4,1.8,2.2,1.1,0.3,0.1,0.5,1.0,5.7
+162,Danilo Gallinari,PF,35,WAS,26,0,14.8,2.2,5.0,.435,0.8,2.5,.313,1.4,2.6,.552,.511,1.8,2.2,.839,0.5,2.5,2.9,1.2,0.2,0.1,0.5,1.0,7.0
+162,Danilo Gallinari,SF,35,DET,6,0,15.0,2.0,3.7,.545,1.2,2.0,.583,0.8,1.7,.500,.705,3.5,4.0,.875,0.3,2.0,2.3,2.0,0.3,0.3,0.2,1.5,8.7
+162,Danilo Gallinari,SF,35,MIL,17,0,9.1,0.8,2.2,.378,0.2,1.0,.176,0.6,1.2,.550,.419,0.9,1.1,.889,0.4,0.7,1.1,0.7,0.4,0.1,0.6,0.9,2.8
+163,Darius Garland,PG,24,CLE,57,57,33.4,6.6,14.8,.446,2.3,6.2,.371,4.3,8.5,.501,.524,2.6,3.1,.834,0.5,2.1,2.7,6.5,1.3,0.1,3.1,1.7,18.0
+164,Usman Garuba,PF,21,GSW,6,0,3.0,0.2,1.0,.167,0.0,0.2,.000,0.2,0.8,.200,.167,0.2,0.3,.500,0.5,0.7,1.2,0.2,0.2,0.5,0.3,0.3,0.5
+165,Luka Garza,C,25,MIN,25,0,4.9,1.4,3.0,.480,0.4,1.3,.281,1.1,1.7,.628,.540,0.7,1.0,.720,0.8,0.5,1.2,0.2,0.2,0.0,0.2,1.0,4.0
+166,Kaiser Gates,SF,27,NOP,1,0,7.0,0.0,4.0,.000,0.0,2.0,.000,0.0,2.0,.000,.000,0.0,0.0,,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+167,Keyonte George,PG,20,UTA,75,44,27.0,4.3,10.9,.391,2.0,5.9,.334,2.3,5.0,.459,.482,2.5,3.0,.848,0.2,2.6,2.8,4.4,0.5,0.1,2.5,1.2,13.0
+168,Paul George,PF,33,LAC,74,74,33.8,7.9,16.7,.471,3.3,7.9,.413,4.6,8.8,.523,.569,3.6,3.9,.907,0.5,4.7,5.2,3.5,1.5,0.5,2.1,2.7,22.6
+169,Taj Gibson,PF,38,TOT,20,1,10.2,0.8,1.9,.405,0.1,0.5,.200,0.7,1.4,.481,.432,0.1,0.1,1.000,0.6,1.3,1.9,0.6,0.2,0.4,0.3,1.6,1.7
+169,Taj Gibson,PF,38,NYK,16,1,10.3,0.4,1.4,.304,0.0,0.4,.000,0.4,1.1,.412,.304,0.1,0.1,1.000,0.6,1.1,1.8,0.6,0.1,0.4,0.3,1.8,1.0
+169,Taj Gibson,PF,38,DET,4,0,9.8,2.0,3.5,.571,0.5,1.0,.500,1.5,2.5,.600,.643,0.0,0.0,,0.5,1.8,2.3,0.5,0.3,0.3,0.0,0.8,4.5
+170,Josh Giddey,SG,21,OKC,80,80,25.1,5.0,10.6,.475,1.0,3.0,.337,4.0,7.6,.530,.523,1.3,1.6,.806,1.4,5.0,6.4,4.8,0.6,0.6,2.1,1.4,12.3
+171,Harry Giles,C,25,TOT,23,0,4.3,1.0,2.2,.460,0.1,0.6,.214,0.9,1.6,.556,.490,0.3,0.6,.538,0.5,0.8,1.3,0.3,0.1,0.1,0.3,0.7,2.4
+171,Harry Giles,C,25,BRK,16,0,5.1,1.4,2.8,.500,0.2,0.7,.273,1.2,2.1,.576,.534,0.4,0.8,.538,0.8,0.9,1.6,0.4,0.1,0.2,0.4,0.9,3.4
+171,Harry Giles,C,25,LAL,7,0,2.7,0.1,0.9,.167,0.0,0.4,.000,0.1,0.4,.333,.167,0.0,0.0,,0.0,0.6,0.6,0.0,0.1,0.0,0.0,0.0,0.3
+172,Shai Gilgeous-Alexander,PG,25,OKC,75,75,34.0,10.6,19.8,.535,1.3,3.6,.353,9.3,16.2,.576,.567,7.6,8.7,.874,0.9,4.7,5.5,6.2,2.0,0.9,2.2,2.5,30.1
+173,Anthony Gill,PF,31,WAS,50,3,9.3,1.5,3.2,.469,0.2,0.8,.244,1.3,2.4,.546,.500,0.6,0.7,.806,0.7,1.1,1.9,0.7,0.3,0.2,0.5,1.0,3.8
+174,Collin Gillespie,PG,24,DEN,24,0,9.4,1.3,2.9,.464,0.6,1.6,.395,0.7,1.3,.548,.572,0.3,0.5,.667,0.0,0.8,0.9,1.1,0.5,0.0,0.6,1.0,3.6
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+175,Jacob Gilyard,PG,25,MEM,37,14,17.7,1.6,3.9,.417,1.4,3.2,.425,0.2,0.6,.375,.594,0.1,0.1,1.000,0.2,0.9,1.2,3.5,0.7,0.1,0.8,0.9,4.7
+175,Jacob Gilyard,PG,25,BRK,4,0,11.3,0.0,1.5,.000,0.0,1.5,.000,0.0,0.0,,.000,0.0,0.0,,0.3,0.5,0.8,1.5,1.5,0.0,0.3,1.0,0.0
+176,Rudy Gobert,C,31,MIN,76,76,34.1,5.3,8.1,.661,0.0,0.0,.000,5.3,8.0,.664,.661,3.3,5.1,.638,3.8,9.2,12.9,1.3,0.7,2.1,1.6,3.1,14.0
+177,Jordan Goodwin,PG,25,TOT,57,12,18.5,2.5,6.7,.369,0.7,2.5,.298,1.7,4.2,.411,.424,0.8,1.0,.746,1.5,2.9,4.4,2.7,0.8,0.3,1.0,1.3,6.5
+177,Jordan Goodwin,PG,25,PHO,40,0,14.0,1.9,4.8,.389,0.6,2.0,.288,1.3,2.8,.460,.448,0.6,0.7,.862,1.0,1.9,2.9,2.0,0.6,0.2,0.7,1.1,5.0
+177,Jordan Goodwin,PG,25,MEM,17,12,29.2,3.9,11.1,.349,1.1,3.6,.311,2.8,7.5,.367,.399,1.1,1.8,.633,2.8,5.2,8.0,4.5,1.5,0.5,1.6,1.9,10.0
+178,Aaron Gordon,PF,28,DEN,73,73,31.5,5.5,9.8,.556,0.5,1.9,.290,4.9,7.9,.619,.584,2.4,3.7,.658,2.4,4.1,6.5,3.5,0.8,0.6,1.4,1.9,13.9
+179,Eric Gordon,SG,35,PHO,68,24,27.8,4.0,9.1,.443,2.2,5.8,.378,1.8,3.3,.561,.565,0.8,0.9,.797,0.2,1.6,1.8,2.0,1.0,0.4,1.1,1.2,11.0
+180,Devonte' Graham,PG,28,SAS,23,0,13.6,1.7,4.7,.352,1.1,3.6,.301,0.6,1.1,.520,.468,0.6,0.7,.813,0.2,1.3,1.6,2.1,0.4,0.1,0.7,0.6,5.0
+181,Jerami Grant,PF,29,POR,54,54,33.9,7.1,15.7,.451,2.1,5.1,.402,5.0,10.6,.474,.516,4.7,5.8,.817,0.7,2.9,3.5,2.8,0.8,0.6,2.1,2.2,21.0
+182,RaiQuan Gray,PF,24,SAS,3,0,13.0,3.3,5.7,.588,1.0,2.3,.429,2.3,3.3,.700,.676,0.0,0.0,,0.7,1.7,2.3,2.0,0.3,0.3,0.3,0.0,7.7
+183,A.J. Green,SG,24,MIL,56,0,11.0,1.5,3.5,.423,1.2,3.0,.408,0.3,0.5,.519,.599,0.3,0.3,.895,0.2,1.0,1.1,0.5,0.2,0.1,0.2,0.9,4.5
+184,Danny Green,SG,36,PHI,2,0,9.0,0.0,1.0,.000,0.0,0.5,.000,0.0,0.5,.000,.000,0.0,0.0,,0.0,1.0,1.0,0.5,0.5,0.0,0.0,0.5,0.0
+185,Draymond Green,PF,33,GSW,55,52,27.1,3.4,6.8,.497,0.9,2.3,.395,2.4,4.4,.551,.566,1.0,1.3,.730,1.4,5.9,7.2,6.0,1.0,0.9,2.5,3.0,8.6
+186,Jalen Green,SG,21,HOU,82,82,31.7,6.9,16.2,.423,2.5,7.4,.332,4.4,8.9,.498,.498,3.5,4.3,.804,0.5,4.7,5.2,3.5,0.8,0.3,2.3,1.4,19.6
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+188,Jeff Green,PF,37,HOU,78,6,16.8,2.1,4.7,.456,0.7,2.1,.331,1.5,2.6,.553,.529,1.6,1.9,.819,0.6,1.7,2.3,0.9,0.2,0.4,0.6,1.3,6.5
+189,Josh Green,SG,23,DAL,57,33,26.4,3.1,6.6,.479,1.3,3.3,.385,1.9,3.3,.572,.575,0.7,1.0,.684,0.7,2.5,3.2,2.3,0.8,0.2,1.1,1.9,8.2
+190,AJ Griffin,SF,20,ATL,20,0,8.6,0.9,3.1,.290,0.5,2.0,.256,0.4,1.2,.348,.371,0.1,0.1,1.000,0.1,0.8,0.9,0.3,0.1,0.1,0.4,0.3,2.4
+191,Quentin Grimes,SG,23,TOT,51,18,20.1,2.4,6.4,.372,1.6,4.7,.338,0.8,1.7,.466,.495,0.7,0.9,.756,0.3,1.7,2.0,1.3,0.7,0.1,0.5,1.5,7.0
+191,Quentin Grimes,SG,23,NYK,45,18,20.2,2.5,6.4,.395,1.7,4.7,.363,0.8,1.6,.486,.530,0.5,0.8,.706,0.3,1.6,2.0,1.2,0.7,0.1,0.5,1.5,7.3
+191,Quentin Grimes,SG,23,DET,6,0,19.2,1.5,7.0,.214,0.7,4.7,.143,0.8,2.3,.357,.262,1.7,1.8,.909,0.2,1.8,2.0,2.3,0.8,0.7,1.2,1.5,5.3
+192,Mouhamadou Gueye,PF,25,TOR,11,0,10.9,1.0,3.5,.289,0.0,0.3,.000,1.0,3.2,.314,.289,0.4,0.8,.444,1.1,1.0,2.1,0.5,0.3,1.6,1.1,1.4,2.4
+193,Mouhamed Gueye,PF,21,ATL,6,0,12.2,1.3,3.8,.348,0.5,1.5,.333,0.8,2.3,.357,.413,0.8,1.0,.833,1.7,2.0,3.7,0.7,0.8,0.7,0.2,2.2,4.0
+194,Rui Hachimura,PF,25,LAL,68,39,26.9,5.3,9.9,.537,1.4,3.4,.422,3.9,6.5,.597,.609,1.5,2.0,.739,0.9,3.5,4.3,1.2,0.6,0.4,0.7,1.5,13.6
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+197,R.J. Hampton,PG,22,MIA,8,2,9.5,0.5,1.8,.286,0.1,1.0,.125,0.4,0.8,.500,.321,0.1,0.3,.500,0.3,0.5,0.8,1.0,0.0,0.0,0.4,1.0,1.3
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+201,Ron Harper Jr.,PF,23,TOR,1,0,4.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,,0.0,0.0,,0.0,0.0,0.0,1.0,0.0,0.0,0.0,2.0,0.0
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+205,Tobias Harris,PF,31,PHI,70,70,33.8,6.6,13.6,.487,1.3,3.7,.353,5.3,9.9,.537,.535,2.7,3.0,.878,1.1,5.3,6.5,3.1,1.0,0.7,1.3,1.6,17.2
+206,Shaquille Harrison,SG,30,MEM,3,0,2.0,0.3,0.7,.500,0.0,0.0,,0.3,0.7,.500,.500,0.0,0.0,,0.0,0.7,0.7,0.0,0.0,0.3,0.0,0.0,0.7
+207,Josh Hart,SF,28,NYK,81,42,33.4,3.6,8.4,.434,1.0,3.2,.310,2.7,5.2,.511,.493,1.1,1.4,.791,1.6,6.8,8.3,4.1,0.9,0.3,1.5,2.1,9.4
+208,Isaiah Hartenstein,C,25,NYK,75,49,25.3,3.1,4.9,.644,0.0,0.0,.333,3.1,4.8,.646,.645,1.5,2.1,.707,3.3,5.1,8.3,2.5,1.2,1.1,1.2,2.8,7.8
+209,Sam Hauser,SF,26,BOS,79,13,22.0,3.2,7.1,.446,2.5,5.9,.424,0.7,1.2,.559,.623,0.2,0.2,.895,0.6,2.9,3.5,1.0,0.5,0.3,0.4,1.3,9.0
+210,Jordan Hawkins,SG,21,NOP,67,10,17.3,2.6,6.8,.382,1.6,4.4,.366,1.0,2.4,.411,.501,1.0,1.2,.838,0.4,1.8,2.2,1.0,0.3,0.1,0.6,1.1,7.8
+211,Jaxson Hayes,C,23,LAL,70,5,12.5,1.8,2.5,.720,0.0,0.1,.000,1.8,2.4,.754,.720,0.7,1.2,.622,1.0,2.0,3.0,0.5,0.5,0.4,0.6,1.8,4.3
+212,Killian Hayes,PG,22,DET,42,31,24.0,2.8,6.7,.413,0.5,1.8,.297,2.3,5.0,.455,.452,0.8,1.2,.660,0.3,2.6,2.8,4.9,0.9,0.5,1.2,1.8,6.9
+213,Gordon Hayward,SF,33,TOT,51,28,24.4,3.7,7.9,.464,0.7,1.8,.411,2.9,6.1,.479,.510,1.7,2.4,.742,0.7,2.8,3.5,3.1,0.8,0.3,1.3,1.3,9.8
+213,Gordon Hayward,SF,33,CHO,25,25,31.9,5.6,11.9,.468,0.9,2.4,.361,4.7,9.4,.496,.505,2.5,3.2,.765,1.0,3.7,4.7,4.6,1.1,0.5,2.0,1.8,14.5
+213,Gordon Hayward,SF,33,OKC,26,3,17.2,1.8,4.1,.453,0.6,1.1,.517,1.3,3.0,.429,.524,1.0,1.5,.692,0.5,2.0,2.5,1.6,0.5,0.0,0.6,0.8,5.3
+214,Scoot Henderson,PG,19,POR,62,32,28.5,5.0,12.9,.385,1.4,4.3,.325,3.5,8.5,.416,.440,2.7,3.3,.819,0.9,2.3,3.1,5.4,0.8,0.2,3.4,3.1,14.0
+215,Taylor Hendricks,PF,20,UTA,40,23,21.4,2.7,6.0,.450,1.3,3.5,.379,1.4,2.5,.550,.560,0.6,0.7,.793,1.2,3.5,4.6,0.8,0.7,0.8,0.7,1.9,7.3
+216,Tyler Herro,SG,24,MIA,42,40,33.5,7.7,17.5,.441,3.1,7.9,.396,4.6,9.6,.478,.531,2.3,2.6,.856,0.5,4.8,5.3,4.5,0.7,0.1,2.2,1.5,20.8
+217,Buddy Hield,SF-SG,31,TOT,84,42,25.7,4.4,10.1,.436,2.6,6.8,.386,1.8,3.4,.539,.565,0.6,0.7,.881,0.7,2.5,3.2,2.8,0.8,0.5,1.2,2.0,12.1
+217,Buddy Hield,SF,31,IND,52,28,25.7,4.4,9.9,.443,2.6,6.9,.384,1.8,3.1,.575,.575,0.5,0.6,.848,0.7,2.6,3.2,2.7,0.8,0.6,1.2,2.0,12.0
+217,Buddy Hield,SG,31,PHI,32,14,25.8,4.4,10.4,.426,2.6,6.6,.389,1.9,3.8,.492,.550,0.8,0.8,.923,0.8,2.3,3.2,3.0,0.8,0.3,1.3,2.1,12.2
+218,Haywood Highsmith,PF,27,MIA,66,26,20.7,2.3,5.0,.465,1.2,2.9,.396,1.2,2.1,.562,.581,0.3,0.5,.639,1.0,2.2,3.2,1.1,0.8,0.5,0.5,1.8,6.1
+219,Nate Hinton,SG,24,HOU,15,0,5.0,0.7,1.7,.423,0.5,0.9,.500,0.3,0.8,.333,.558,0.3,0.3,.800,0.5,1.0,1.5,0.7,0.2,0.1,0.2,0.7,2.2
+220,D'Moi Hodge,SG,25,LAL,7,0,5.9,0.7,2.1,.333,0.4,1.7,.250,0.3,0.4,.667,.433,0.1,0.3,.500,0.0,0.0,0.0,0.7,0.1,0.1,0.1,0.6,2.0
+221,Aaron Holiday,PG,27,HOU,78,1,16.3,2.4,5.3,.446,1.1,2.8,.387,1.3,2.6,.510,.547,0.7,0.8,.921,0.3,1.3,1.6,1.8,0.5,0.1,0.7,1.6,6.6
+222,Jrue Holiday,PG,33,BOS,69,69,32.8,4.8,10.0,.480,2.0,4.7,.429,2.8,5.3,.526,.581,0.9,1.0,.833,1.2,4.2,5.4,4.8,0.9,0.8,1.8,1.6,12.5
+223,Justin Holiday,SG,34,DEN,58,9,14.9,1.5,3.3,.454,0.9,2.3,.404,0.6,1.0,.569,.595,0.1,0.1,.750,0.2,1.1,1.2,1.2,0.6,0.2,0.2,1.2,4.0
+224,Richaun Holmes,C-PF,30,TOT,40,10,13.9,2.1,3.7,.558,0.0,0.1,.333,2.0,3.6,.563,.561,0.9,1.2,.723,1.8,2.8,4.6,0.6,0.3,0.5,0.6,1.9,5.0
+224,Richaun Holmes,C,30,DAL,23,2,10.3,1.4,2.6,.559,0.0,0.0,,1.4,2.6,.559,.559,0.5,0.9,.571,1.4,2.0,3.4,0.7,0.1,0.4,0.5,1.4,3.4
+224,Richaun Holmes,PF,30,WAS,17,8,18.7,2.9,5.2,.557,0.1,0.2,.333,2.8,5.0,.565,.563,1.3,1.5,.846,2.2,3.9,6.1,0.6,0.5,0.6,0.7,2.4,7.1
+225,Chet Holmgren,C,21,OKC,82,82,29.4,6.2,11.6,.530,1.6,4.3,.370,4.6,7.4,.623,.598,2.7,3.4,.793,1.6,6.3,7.9,2.4,0.6,2.3,1.6,2.4,16.5
+226,Jalen Hood-Schifino,SG,20,LAL,21,0,5.2,0.5,2.1,.222,0.1,0.7,.133,0.4,1.4,.267,.244,0.6,1.0,.600,0.1,0.5,0.6,0.4,0.1,0.1,0.4,0.6,1.6
+227,Al Horford,C,37,BOS,65,33,26.8,3.3,6.4,.511,1.7,4.0,.419,1.6,2.5,.658,.640,0.4,0.5,.867,1.3,5.1,6.4,2.6,0.6,1.0,0.7,1.4,8.6
+228,Talen Horton-Tucker,PG,23,UTA,51,11,19.8,3.6,9.1,.396,1.2,3.6,.330,2.4,5.5,.438,.460,1.7,2.1,.807,0.4,2.0,2.4,3.5,0.9,0.4,1.3,1.8,10.1
+229,Danuel House Jr.,SF,30,PHI,34,4,15.0,1.4,3.1,.448,0.4,1.5,.300,0.9,1.6,.582,.519,1.0,1.4,.761,0.5,1.2,1.7,0.8,0.4,0.1,0.6,1.3,4.2
+230,Caleb Houstan,SF,21,ORL,59,13,13.8,1.4,3.5,.388,1.2,3.3,.373,0.1,0.2,.615,.563,0.4,0.4,.808,0.5,0.9,1.4,0.5,0.3,0.1,0.3,1.0,4.3
+231,Jett Howard,SF,20,ORL,18,0,3.7,0.6,1.7,.333,0.4,1.4,.280,0.2,0.3,.600,.450,0.1,0.2,.500,0.1,0.3,0.4,0.3,0.1,0.1,0.2,0.2,1.6
+232,Kevin Huerter,SG,25,SAC,64,59,24.4,3.9,8.8,.443,1.9,5.3,.361,2.0,3.4,.571,.553,0.6,0.7,.766,0.6,2.9,3.5,2.6,0.7,0.4,0.8,2.1,10.2
+233,Jay Huff,C,25,DEN,20,0,2.5,0.5,0.8,.600,0.2,0.5,.333,0.3,0.3,1.000,.700,0.1,0.1,1.000,0.1,0.5,0.6,0.1,0.1,0.2,0.1,0.5,1.2
+234,De'Andre Hunter,SF,26,ATL,57,37,29.5,5.3,11.6,.459,2.1,5.3,.385,3.3,6.3,.521,.547,2.9,3.4,.847,0.5,3.4,3.9,1.5,0.7,0.3,1.5,2.6,15.6
+235,Matthew Hurt,PF,23,MEM,8,0,14.1,1.5,4.3,.353,0.6,2.5,.250,0.9,1.8,.500,.426,0.4,0.4,1.000,1.1,0.9,2.0,0.5,0.4,0.4,0.3,1.0,4.0
+236,Bones Hyland,PG,23,LAC,37,5,14.6,2.6,6.8,.386,1.2,3.6,.326,1.4,3.1,.457,.474,0.5,0.6,.783,0.1,1.4,1.5,2.5,0.7,0.1,1.2,1.4,6.9
+237,Joe Ingles,SF,36,ORL,68,0,17.2,1.5,3.3,.436,1.0,2.4,.435,0.4,1.0,.439,.590,0.4,0.5,.824,0.3,1.8,2.1,3.0,0.6,0.1,1.0,1.3,4.4
+238,Brandon Ingram,SF,26,NOP,64,64,32.9,7.8,15.9,.492,1.3,3.8,.355,6.5,12.1,.534,.534,3.8,4.8,.801,0.7,4.4,5.1,5.7,0.8,0.6,2.5,2.3,20.8
+239,Kyrie Irving,SG,31,DAL,58,58,35.0,9.7,19.5,.497,3.0,7.3,.411,6.7,12.2,.548,.573,3.3,3.6,.905,0.8,4.2,5.0,5.2,1.3,0.5,1.8,1.9,25.6
+240,Jonathan Isaac,PF,26,ORL,58,2,15.8,2.5,4.9,.510,0.7,1.9,.375,1.8,3.0,.598,.584,1.0,1.4,.720,1.3,3.2,4.5,0.5,0.7,1.2,0.5,1.1,6.8
+241,Jaden Ivey,SG,21,DET,77,61,28.8,5.4,12.6,.429,1.6,4.8,.336,3.8,7.8,.485,.492,2.9,3.9,.749,1.1,2.3,3.4,3.8,0.7,0.5,2.5,2.6,15.4
+242,Andre Jackson Jr.,SG,22,MIL,57,8,10.0,0.9,1.8,.500,0.3,0.8,.370,0.6,0.9,.611,.585,0.1,0.2,.727,0.8,1.2,2.0,0.9,0.3,0.1,0.5,1.6,2.2
+243,GG Jackson II,PF,19,MEM,48,18,25.7,5.0,11.7,.428,2.1,6.0,.357,2.9,5.8,.502,.519,2.4,3.2,.752,1.1,3.0,4.1,1.2,0.6,0.5,1.4,1.3,14.6
+244,Isaiah Jackson,C,22,IND,59,3,13.1,2.6,3.9,.665,0.0,0.1,.000,2.6,3.9,.677,.665,1.2,1.7,.716,1.6,2.4,4.0,0.8,0.6,1.0,0.7,2.3,6.5
+245,Jaren Jackson Jr.,C,24,MEM,66,66,32.2,7.8,17.6,.444,1.8,5.5,.320,6.0,12.1,.501,.494,5.1,6.3,.808,1.3,4.2,5.5,2.3,1.2,1.6,2.4,3.6,22.5
+246,Justin Jackson,SF,28,MIN,2,0,0.5,0.0,0.0,,0.0,0.0,,0.0,0.0,,,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+247,Quenton Jackson,PG,25,IND,3,0,3.7,0.0,0.3,.000,0.0,0.0,,0.0,0.3,.000,.000,0.7,0.7,1.000,0.0,1.3,1.3,0.7,0.3,0.0,0.0,0.3,0.7
+248,Reggie Jackson,PG,33,DEN,82,23,22.2,4.0,9.2,.431,1.2,3.5,.359,2.7,5.8,.475,.499,1.0,1.2,.806,0.4,1.5,1.9,3.8,0.5,0.2,1.3,1.8,10.2
+249,Trayce Jackson-Davis,PF,23,GSW,68,16,16.6,3.4,4.9,.702,0.0,0.0,.000,3.4,4.9,.704,.702,1.1,1.9,.561,2.0,3.0,5.0,1.2,0.4,1.1,0.7,1.6,7.9
+250,LeBron James,PF,39,LAL,71,71,35.3,9.6,17.9,.540,2.1,5.1,.410,7.5,12.8,.592,.599,4.3,5.7,.750,0.9,6.4,7.3,8.3,1.3,0.5,3.5,1.1,25.7
+251,Jaime Jaquez Jr.,SF,22,MIA,75,20,28.2,4.5,9.3,.489,0.9,2.7,.322,3.7,6.6,.558,.536,1.9,2.3,.811,1.1,2.7,3.8,2.6,1.0,0.3,1.5,2.2,11.9
+252,DeJon Jarreau,SG,26,MEM,9,0,16.7,1.9,5.6,.340,0.4,1.4,.308,1.4,4.1,.351,.380,0.6,1.2,.455,0.9,4.0,4.9,2.9,0.7,0.2,1.0,1.6,4.8
+253,DaQuan Jeffries,SG,26,NYK,17,0,2.7,0.4,1.0,.353,0.1,0.6,.200,0.2,0.4,.571,.412,0.0,0.1,.000,0.0,0.3,0.3,0.3,0.0,0.1,0.2,0.1,0.8
+254,Trey Jemison,C,24,TOT,25,14,22.9,3.0,5.4,.551,0.0,0.0,,3.0,5.4,.551,.551,0.8,1.0,.840,2.6,2.8,5.4,1.1,0.5,1.1,1.4,2.8,6.8
+254,Trey Jemison,C,24,WAS,2,0,0.5,0.0,0.0,,0.0,0.0,,0.0,0.0,,,0.0,0.0,,0.0,0.5,0.5,0.0,0.0,0.0,0.5,0.0,0.0
+254,Trey Jemison,C,24,MEM,23,14,24.9,3.3,5.9,.551,0.0,0.0,,3.3,5.9,.551,.551,0.9,1.1,.840,2.8,3.0,5.8,1.2,0.5,1.2,1.4,3.0,7.4
+255,Ty Jerome,SG,26,CLE,2,0,7.5,1.0,2.0,.500,0.0,0.5,.000,1.0,1.5,.667,.500,0.0,0.0,,0.5,0.0,0.5,1.5,0.0,0.0,0.5,1.0,2.0
+256,Isaiah Joe,SG,24,OKC,78,1,18.5,2.8,6.2,.458,1.9,4.5,.416,1.0,1.7,.568,.609,0.6,0.7,.865,0.4,1.9,2.3,1.3,0.6,0.3,0.6,1.1,8.2
+257,Cameron Johnson,PF,27,BRK,58,47,27.6,4.8,10.7,.446,2.4,6.1,.391,2.4,4.6,.519,.557,1.5,1.9,.789,0.9,3.4,4.3,2.4,0.8,0.3,1.0,1.8,13.4
+258,Jalen Johnson,SF,22,ATL,56,52,33.7,6.4,12.5,.511,1.3,3.6,.355,5.1,9.0,.574,.562,1.9,2.6,.728,1.3,7.4,8.7,3.6,1.2,0.8,1.8,2.4,16.0
+259,James Johnson,PF,36,IND,9,0,5.2,0.3,1.1,.300,0.0,0.2,.000,0.3,0.9,.375,.300,0.2,0.2,1.000,0.1,0.3,0.4,0.9,0.6,0.1,0.6,1.0,0.9
+260,Keldon Johnson,SF,24,SAS,69,27,29.5,5.7,12.5,.454,1.8,5.3,.346,3.8,7.2,.533,.527,2.5,3.2,.792,1.4,4.1,5.5,2.8,0.7,0.3,1.4,2.2,15.7
+261,Keon Johnson,SG,21,BRK,5,0,12.2,1.6,4.2,.381,0.8,2.0,.400,0.8,2.2,.364,.476,2.2,2.4,.917,0.6,0.8,1.4,0.6,0.6,0.2,1.0,1.0,6.2
+262,Keyontae Johnson,SF,23,OKC,9,0,7.3,0.6,1.8,.313,0.1,0.3,.333,0.4,1.4,.308,.344,0.0,0.0,,0.4,0.7,1.1,0.4,0.1,0.0,0.4,0.6,1.2
+263,Nikola Jokić,C,28,DEN,79,79,34.6,10.4,17.9,.583,1.1,2.9,.359,9.4,14.9,.626,.612,4.5,5.5,.817,2.8,9.5,12.4,9.0,1.4,0.9,3.0,2.5,26.4
+264,Colby Jones,SG,21,SAC,30,0,6.4,0.9,2.4,.394,0.1,0.7,.091,0.9,1.6,.531,.408,0.2,0.4,.545,0.6,0.8,1.3,0.7,0.2,0.2,0.3,1.0,2.1
+265,Damian Jones,C,28,CLE,39,0,6.9,1.1,1.8,.597,0.1,0.4,.214,1.0,1.5,.690,.618,0.5,0.5,.857,0.5,1.1,1.6,0.4,0.2,0.3,0.3,0.7,2.7
+266,Derrick Jones Jr.,SF,26,DAL,76,66,23.5,3.1,6.5,.483,1.1,3.1,.343,2.1,3.4,.613,.566,1.3,1.8,.713,1.1,2.2,3.3,1.0,0.7,0.7,0.9,1.6,8.6
+267,Herbert Jones,SF,25,NOP,76,76,30.5,3.9,7.7,.498,1.5,3.6,.418,2.4,4.1,.568,.595,1.8,2.1,.867,1.0,2.6,3.6,2.6,1.4,0.8,1.2,2.9,11.0
+268,Mason Jones,SG,25,SAC,5,0,5.6,0.4,1.6,.250,0.4,1.4,.286,0.0,0.2,.000,.375,0.2,0.4,.500,0.2,0.8,1.0,1.0,0.2,0.0,0.4,0.6,1.4
+269,Tre Jones,PG,24,SAS,77,48,27.8,3.9,7.8,.505,0.8,2.5,.335,3.1,5.3,.584,.558,1.3,1.5,.856,0.8,3.0,3.8,6.2,1.0,0.1,1.5,1.2,10.0
+270,Tyus Jones,PG,27,WAS,66,66,29.3,4.9,10.1,.489,1.6,3.9,.414,3.3,6.2,.537,.569,0.5,0.6,.800,0.3,2.4,2.7,7.3,1.1,0.3,1.0,0.7,12.0
+271,DeAndre Jordan,C,35,DEN,36,2,11.0,1.6,2.6,.624,0.0,0.0,,1.6,2.6,.624,.624,0.7,1.5,.491,1.5,2.9,4.4,0.7,0.2,0.4,0.6,1.6,3.9
+272,Cory Joseph,PG,32,GSW,26,0,11.4,0.9,2.5,.359,0.5,1.6,.310,0.4,0.8,.455,.461,0.2,0.3,.571,0.2,0.9,1.2,1.6,0.2,0.1,0.5,1.0,2.4
+273,Nikola Jović,PF,20,MIA,46,38,19.5,2.8,6.1,.452,1.4,3.5,.399,1.3,2.6,.525,.568,0.7,1.0,.702,0.5,3.6,4.2,2.0,0.5,0.3,0.9,1.8,7.7
+274,Johnny Juzang,SG,22,UTA,20,5,18.6,2.6,5.5,.464,1.6,3.9,.416,1.0,1.7,.576,.609,0.5,0.7,.714,0.4,1.5,1.8,1.2,0.2,0.1,0.7,1.5,7.2
+275,Luke Kennard,SG,27,MEM,39,22,25.6,3.6,8.1,.448,2.7,6.1,.450,0.9,2.0,.442,.617,1.0,1.2,.889,0.3,2.6,2.9,3.5,0.5,0.1,1.4,1.0,11.0
+276,Walker Kessler,C,22,UTA,64,22,23.3,3.6,5.5,.654,0.1,0.3,.211,3.5,5.2,.680,.660,0.9,1.5,.602,2.6,4.9,7.5,0.9,0.5,2.4,1.0,2.1,8.1
+277,Braxton Key,SF,26,DEN,20,0,3.0,0.4,0.9,.412,0.1,0.3,.400,0.3,0.6,.417,.471,0.3,0.4,.750,0.2,0.8,0.9,0.5,0.1,0.1,0.1,0.3,1.1
+278,Corey Kispert,SF,24,WAS,80,22,25.8,4.8,9.9,.486,2.3,6.0,.383,2.5,3.9,.643,.602,1.4,2.0,.726,0.4,2.4,2.8,2.0,0.5,0.2,1.2,1.8,13.4
+279,Maxi Kleber,C,32,DAL,43,7,20.3,1.5,3.4,.432,0.7,2.1,.348,0.7,1.3,.574,.541,0.8,1.1,.702,0.7,2.6,3.3,1.6,0.4,0.7,0.7,1.8,4.4
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+283,Luke Kornet,C,28,BOS,63,7,15.6,2.3,3.2,.700,0.0,0.0,1.000,2.2,3.2,.698,.702,0.8,0.9,.907,1.9,2.3,4.1,1.1,0.4,1.0,0.3,1.2,5.3
+284,Vit Krejci,PG,23,ATL,22,14,24.6,2.3,4.7,.490,1.3,3.1,.412,1.0,1.6,.639,.625,0.2,0.3,.833,0.5,1.9,2.4,2.3,0.6,0.3,0.9,2.2,6.1
+285,Jonathan Kuminga,PF,21,GSW,74,46,26.3,6.2,11.6,.529,0.7,2.2,.321,5.4,9.4,.578,.560,3.0,4.0,.746,1.2,3.6,4.8,2.2,0.7,0.5,1.6,2.3,16.1
+286,Kyle Kuzma,PF,28,WAS,70,70,32.6,8.7,18.8,.463,2.2,6.4,.336,6.5,12.3,.530,.521,2.7,3.4,.775,0.9,5.7,6.6,4.2,0.5,0.7,2.7,2.2,22.2
+287,Jock Landale,C,28,HOU,56,3,13.6,1.9,3.7,.515,0.2,0.7,.250,1.7,3.0,.578,.539,0.9,1.2,.800,1.6,1.5,3.1,1.2,0.4,0.6,0.6,1.3,4.9
+288,Jake LaRavia,PF,22,MEM,35,6,23.0,3.4,8.7,.389,1.5,4.5,.340,1.9,4.2,.442,.477,2.6,3.1,.826,1.5,2.2,3.7,1.7,0.8,0.3,1.3,2.0,10.8
+289,Zach LaVine,SG,28,CHI,25,23,34.9,6.8,15.0,.452,2.4,6.8,.349,4.4,8.3,.536,.531,3.5,4.1,.854,0.3,4.8,5.2,3.9,0.8,0.3,2.1,2.3,19.5
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+291,Saben Lee,PG,24,PHO,24,0,7.7,0.8,2.3,.364,0.1,0.7,.125,0.8,1.6,.462,.382,1.2,1.6,.744,0.4,0.9,1.3,1.3,0.3,0.1,0.3,0.5,3.0
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+293,Kawhi Leonard,SF,32,LAC,68,68,34.3,9.0,17.1,.525,2.1,4.9,.417,6.9,12.1,.569,.585,3.7,4.2,.885,1.2,4.9,6.1,3.6,1.6,0.9,1.8,1.4,23.7
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+295,Kira Lewis Jr.,PG,22,TOT,28,0,9.5,1.2,3.3,.370,0.1,1.2,.121,1.1,2.1,.508,.391,0.6,0.7,.850,0.3,0.6,0.9,1.3,0.3,0.1,0.5,0.5,3.2
+295,Kira Lewis Jr.,PG,22,NOP,15,0,9.6,1.1,3.5,.308,0.1,1.3,.100,0.9,2.1,.438,.327,0.7,0.7,.909,0.3,0.7,0.9,1.2,0.3,0.1,0.5,0.5,2.9
+295,Kira Lewis Jr.,PG,22,TOR,1,0,2.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+295,Kira Lewis Jr.,PG,22,UTA,12,0,9.9,1.5,3.3,.450,0.2,1.1,.154,1.3,2.3,.593,.475,0.6,0.8,.778,0.4,0.6,1.0,1.6,0.3,0.1,0.5,0.3,3.8
+296,Maxwell Lewis,SF,21,LAL,34,0,3.0,0.1,0.6,.190,0.0,0.3,.111,0.1,0.4,.250,.214,0.1,0.1,.667,0.1,0.1,0.1,0.2,0.1,0.0,0.3,0.1,0.3
+297,E.J. Liddell,PF,23,NOP,8,0,2.9,0.1,0.8,.167,0.0,0.6,.000,0.1,0.1,1.000,.167,0.3,0.3,1.000,0.0,0.6,0.6,0.1,0.3,0.3,0.3,0.3,0.5
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+299,Nassir Little,SF,23,PHO,45,2,10.2,1.3,2.8,.460,0.5,1.6,.300,0.8,1.2,.667,.544,0.4,0.4,.850,0.3,1.4,1.7,0.5,0.2,0.2,0.3,0.9,3.4
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+301,Isaiah Livers,PF,25,DET,23,6,20.4,1.7,5.0,.345,1.0,3.3,.286,0.8,1.7,.462,.440,0.5,0.8,.667,0.7,1.4,2.1,1.1,0.6,0.2,0.7,1.9,5.0
+302,Chris Livingston,SF,20,MIL,21,0,4.3,0.5,1.0,.500,0.0,0.2,.200,0.5,0.8,.588,.523,0.1,0.2,.750,0.1,0.9,1.0,0.2,0.1,0.0,0.2,0.7,1.2
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+303,Kenneth Lofton Jr.,PF,21,UTA,4,0,22.8,5.3,8.8,.600,1.0,3.0,.333,4.3,5.8,.739,.657,2.3,2.8,.818,2.5,2.5,5.0,4.8,0.8,0.5,1.5,2.3,13.8
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+311,Théo Maledon,PG,22,PHO,4,0,3.3,0.3,1.0,.250,0.0,0.0,,0.3,1.0,.250,.250,0.8,0.8,1.000,0.0,0.3,0.3,0.0,0.0,0.0,0.8,0.8,1.3
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+314,Tre Mann,PG,22,CHO,28,28,31.0,4.5,9.9,.453,1.4,3.8,.364,3.1,6.0,.509,.524,1.6,2.1,.759,0.6,3.9,4.5,5.2,1.7,0.1,2.0,1.6,11.9
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+326,Miles McBride,PG,23,NYK,68,14,19.5,3.0,6.7,.452,1.6,3.9,.410,1.4,2.8,.511,.572,0.6,0.7,.860,0.5,1.0,1.5,1.7,0.7,0.1,0.4,1.6,8.3
+327,CJ McCollum,PG,32,NOP,66,66,32.7,7.3,16.0,.459,3.6,8.4,.429,3.7,7.5,.492,.572,1.7,2.1,.827,0.6,3.7,4.3,4.6,0.9,0.6,1.7,1.9,20.0
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+331,Doug McDermott,SF,32,IND,18,0,11.3,1.6,3.8,.406,1.0,3.1,.321,0.6,0.7,.769,.536,0.1,0.1,.500,0.1,0.5,0.6,0.7,0.3,0.0,0.2,0.9,4.2
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+333,Bryce McGowens,SG,21,CHO,59,14,14.9,1.7,3.9,.439,0.6,1.7,.333,1.1,2.2,.523,.513,1.1,1.4,.776,0.3,1.4,1.7,0.9,0.4,0.2,0.5,1.3,5.1
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+339,Vasilije Micić,PG,30,OKC,30,0,12.0,1.2,3.0,.407,0.4,1.5,.244,0.9,1.5,.565,.467,0.5,0.6,.737,0.0,0.8,0.8,2.5,0.3,0.1,1.0,0.7,3.3
+339,Vasilije Micić,PG,30,CHO,30,21,27.2,4.1,9.3,.437,1.1,3.6,.294,3.0,5.7,.529,.495,1.6,1.9,.839,0.4,1.7,2.1,6.2,0.7,0.1,2.3,1.5,10.8
+340,Khris Middleton,SF,32,MIL,55,55,27.0,5.8,11.7,.493,1.7,4.5,.381,4.1,7.2,.563,.566,1.8,2.2,.833,0.7,3.9,4.7,5.3,0.9,0.3,2.3,2.6,15.1
+341,Brandon Miller,SF,21,CHO,74,68,32.2,6.4,14.6,.440,2.5,6.7,.373,3.9,8.0,.495,.524,1.9,2.3,.827,0.8,3.4,4.3,2.4,0.9,0.6,1.8,2.5,17.3
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+345,Patty Mills,PG,35,ATL,19,0,10.6,1.0,2.7,.373,0.7,1.8,.382,0.3,0.9,.353,.500,0.0,0.0,,0.3,0.8,1.1,0.7,0.5,0.1,0.4,0.9,2.7
+345,Patty Mills,PG,35,MIA,13,5,16.4,2.1,6.2,.338,0.8,4.1,.208,1.2,2.1,.593,.406,0.8,0.8,1.000,0.3,0.8,1.2,1.5,0.8,0.0,0.6,1.2,5.8
+346,Shake Milton,SG,27,TOT,48,0,12.1,1.6,4.1,.405,0.4,1.3,.281,1.3,2.7,.466,.451,0.8,1.0,.816,0.3,1.3,1.6,1.3,0.4,0.1,0.6,1.1,4.5
+346,Shake Milton,SG,27,MIN,38,0,12.9,1.7,4.2,.400,0.4,1.4,.264,1.3,2.8,.467,.444,0.9,1.2,.818,0.2,1.2,1.3,1.3,0.4,0.1,0.7,1.2,4.7
+346,Shake Milton,SG,27,DET,4,0,15.8,2.8,6.5,.423,0.8,2.3,.333,2.0,4.3,.471,.481,0.5,0.8,.667,1.8,2.8,4.5,1.5,0.5,0.3,0.8,0.8,6.8
+346,Shake Milton,SG,27,NYK,6,0,4.5,0.7,1.5,.444,0.2,0.3,.500,0.5,1.2,.429,.500,0.3,0.3,1.000,0.0,1.0,1.0,0.7,0.2,0.0,0.0,1.2,1.8
+347,Justin Minaya,SF,24,POR,34,1,11.2,0.6,2.2,.297,0.4,1.4,.245,0.3,0.7,.400,.378,0.1,0.3,.556,0.9,0.8,1.6,0.6,0.3,0.3,0.3,1.0,1.8
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+349,Davion Mitchell,PG,25,SAC,72,4,15.3,2.0,4.5,.452,0.8,2.2,.361,1.2,2.3,.539,.540,0.4,0.6,.714,0.3,1.1,1.3,1.9,0.2,0.0,0.6,1.2,5.3
+350,Donovan Mitchell,SG,27,CLE,55,55,35.3,9.1,19.8,.462,3.3,9.0,.368,5.8,10.8,.539,.545,5.0,5.8,.865,0.8,4.3,5.1,6.1,1.8,0.5,2.8,2.1,26.6
+351,Evan Mobley,PF,22,CLE,50,50,30.6,6.4,11.0,.580,0.4,1.2,.373,6.0,9.9,.604,.600,2.5,3.4,.719,2.2,7.1,9.4,3.2,0.9,1.4,1.8,2.7,15.7
+352,Isaiah Mobley,PF,24,CLE,10,0,7.2,1.0,2.4,.417,0.3,1.0,.300,0.7,1.4,.500,.479,0.0,0.1,.000,0.3,0.7,1.0,0.6,0.2,0.0,0.2,0.6,2.3
+353,Malik Monk,SG,25,SAC,72,0,26.0,5.5,12.4,.443,2.1,5.9,.350,3.4,6.5,.528,.526,2.4,2.8,.829,0.4,2.6,2.9,5.1,0.6,0.5,2.1,2.2,15.4
+354,Moses Moody,SG,21,GSW,66,9,17.5,2.9,6.4,.462,1.1,3.0,.360,1.8,3.3,.555,.548,1.1,1.4,.785,1.0,2.0,3.0,0.9,0.6,0.4,0.7,1.4,8.1
+355,Xavier Moon,SG,29,LAC,14,1,8.5,1.1,3.3,.326,0.1,1.2,.118,0.9,2.1,.448,.348,0.1,0.1,.500,0.6,0.7,1.3,1.5,0.2,0.2,0.4,0.6,2.4
+356,Taze Moore,SG,25,POR,4,0,10.0,2.0,4.8,.421,0.3,1.8,.143,1.8,3.0,.583,.447,0.3,0.5,.500,1.0,1.0,2.0,1.3,0.5,0.0,0.8,1.0,4.5
+357,Wendell Moore Jr.,SG,22,MIN,25,0,3.0,0.4,0.7,.500,0.0,0.2,.000,0.4,0.5,.750,.500,0.0,0.0,,0.0,0.5,0.5,0.2,0.2,0.0,0.2,0.2,0.7
+358,Ja Morant,PG,24,MEM,9,9,35.3,8.9,18.9,.471,1.6,5.7,.275,7.3,13.2,.555,.512,5.8,7.1,.813,0.7,4.9,5.6,8.1,0.8,0.6,3.0,2.1,25.1
+359,Marcus Morris,PF,34,TOT,49,7,16.7,2.4,5.4,.439,1.0,2.4,.403,1.4,3.0,.469,.530,0.7,0.9,.818,0.5,2.3,2.7,0.7,0.3,0.3,0.6,1.5,6.4
+359,Marcus Morris,PF,34,PHI,37,7,17.2,2.4,5.5,.439,1.0,2.4,.400,1.5,3.1,.470,.527,0.8,1.0,.861,0.5,2.5,2.9,0.7,0.4,0.3,0.5,1.6,6.7
+359,Marcus Morris,PF,34,CLE,12,0,15.0,2.2,4.9,.441,1.0,2.4,.414,1.2,2.5,.467,.542,0.4,0.7,.625,0.4,1.7,2.1,0.8,0.2,0.2,0.9,1.1,5.8
+360,Markieff Morris,PF,34,DAL,26,1,8.3,0.9,2.6,.338,0.6,1.6,.357,0.3,1.0,.308,.449,0.2,0.2,.833,0.2,1.3,1.5,0.6,0.2,0.1,0.5,0.5,2.5
+361,Monte Morris,PG,28,TOT,33,0,14.4,1.9,4.6,.405,0.8,2.1,.386,1.1,2.5,.422,.493,0.4,0.6,.684,0.2,1.5,1.7,2.1,0.6,0.3,0.2,0.4,5.0
+361,Monte Morris,PG,28,DET,6,0,11.3,2.0,5.5,.364,0.3,1.8,.182,1.7,3.7,.455,.394,0.2,0.3,.500,0.3,1.7,2.0,1.3,0.2,0.2,0.3,0.2,4.5
+361,Monte Morris,PG,28,MIN,27,0,15.1,1.9,4.4,.417,0.9,2.2,.424,0.9,2.3,.410,.521,0.4,0.6,.706,0.2,1.5,1.7,2.3,0.7,0.3,0.2,0.5,5.1
+362,Trey Murphy III,SF,23,NOP,57,23,29.6,4.8,10.9,.443,3.0,7.8,.380,1.9,3.1,.602,.579,2.2,2.6,.815,0.7,4.2,4.9,2.2,0.9,0.5,0.6,1.2,14.8
+363,Dejounte Murray,SG,27,ATL,78,78,35.7,8.6,18.8,.459,2.6,7.1,.363,6.0,11.7,.518,.528,2.7,3.4,.794,0.8,4.5,5.3,6.4,1.4,0.3,2.6,1.8,22.5
+364,Jamal Murray,PG,26,DEN,59,59,31.5,8.0,16.7,.481,2.5,5.8,.425,5.6,10.9,.510,.554,2.7,3.1,.853,0.7,3.4,4.1,6.5,1.0,0.7,2.1,1.8,21.2
+365,Keegan Murray,SF,23,SAC,77,77,33.6,5.8,12.7,.454,2.4,6.6,.358,3.4,6.0,.560,.548,1.3,1.6,.831,1.4,4.1,5.5,1.7,1.0,0.8,0.8,2.7,15.2
+366,Kris Murray,SF,23,POR,62,29,21.7,2.3,5.9,.396,0.8,3.0,.268,1.5,3.0,.525,.463,0.6,0.9,.661,1.4,2.2,3.6,1.3,0.9,0.3,0.9,1.5,6.1
+367,Mike Muscala,C,32,TOT,53,6,11.3,1.1,2.9,.359,0.5,1.8,.292,0.5,1.1,.467,.449,0.3,0.5,.680,0.7,1.6,2.4,0.7,0.2,0.2,0.4,1.0,3.0
+367,Mike Muscala,C,32,WAS,24,2,14.1,1.4,3.8,.367,0.6,2.1,.275,0.8,1.6,.487,.444,0.6,0.8,.750,0.9,2.2,3.1,0.9,0.2,0.3,0.7,1.3,4.0
+367,Mike Muscala,C,32,DET,13,4,13.2,1.2,3.4,.341,1.0,2.6,.382,0.2,0.8,.200,.489,0.2,0.3,.500,0.8,1.5,2.2,0.8,0.2,0.4,0.2,1.0,3.5
+367,Mike Muscala,C,32,OKC,16,0,5.7,0.5,1.4,.364,0.1,0.7,.091,0.4,0.7,.636,.386,0.0,0.1,.000,0.4,0.9,1.3,0.3,0.1,0.1,0.3,0.5,1.1
+368,Svi Mykhailiuk,SF,26,BOS,41,2,10.1,1.4,3.3,.416,1.0,2.6,.389,0.4,0.7,.517,.569,0.1,0.2,.667,0.3,1.0,1.2,0.9,0.3,0.0,0.3,0.5,4.0
+369,Larry Nance Jr.,C,31,NOP,61,0,19.9,2.3,4.0,.573,0.4,1.1,.415,1.8,2.9,.631,.629,0.8,1.0,.770,1.5,3.5,5.0,1.9,1.0,0.3,0.8,1.6,5.7
+370,Pete Nance,PF,23,CLE,8,0,3.4,0.1,0.8,.167,0.1,0.1,1.000,0.0,0.6,.000,.250,0.0,0.3,.000,0.0,0.4,0.4,0.0,0.1,0.0,0.1,0.3,0.4
+371,Andrew Nembhard,PG,24,IND,68,47,25.0,3.8,7.7,.498,0.9,2.5,.357,2.9,5.2,.567,.557,0.7,0.8,.804,0.5,1.5,2.1,4.1,0.9,0.1,1.5,2.1,9.2
+372,Aaron Nesmith,SF,24,IND,72,47,27.7,4.4,8.8,.496,1.9,4.6,.419,2.4,4.2,.581,.606,1.5,1.9,.781,0.9,2.9,3.8,1.5,0.9,0.7,0.9,3.3,12.2
+373,Georges Niang,PF,30,CLE,82,10,22.3,3.5,7.7,.449,1.8,4.8,.376,1.6,2.9,.572,.567,0.6,0.7,.850,0.3,3.1,3.4,1.2,0.4,0.2,0.9,2.3,9.4
+374,Daishen Nix,PG,21,MIN,15,0,3.3,0.6,1.6,.375,0.4,1.1,.353,0.2,0.5,.429,.500,0.2,0.4,.500,0.1,0.1,0.2,0.4,0.3,0.0,0.3,0.1,1.8
+375,Zeke Nnaji,PF,23,DEN,58,0,9.9,1.2,2.6,.463,0.1,0.4,.261,1.1,2.2,.500,.483,0.7,1.1,.677,1.1,1.1,2.2,0.6,0.3,0.7,0.5,1.4,3.2
+376,Jaylen Nowell,SG,24,TOT,13,1,16.5,2.6,6.0,.436,0.5,2.2,.207,2.2,3.8,.571,.474,0.5,0.7,.778,0.5,1.4,1.8,1.5,0.3,0.2,0.5,1.2,6.2
+376,Jaylen Nowell,SG,24,MEM,9,1,17.3,2.4,6.1,.400,0.4,2.6,.174,2.0,3.6,.563,.436,0.3,0.3,1.000,0.3,1.2,1.6,1.8,0.3,0.0,0.6,1.4,5.7
+376,Jaylen Nowell,SG,24,DET,4,0,14.5,3.0,5.8,.522,0.5,1.5,.333,2.5,4.3,.588,.565,1.0,1.5,.667,0.8,1.8,2.5,0.8,0.3,0.5,0.5,0.5,7.5
+377,Markquis Nowell,SG,24,TOR,1,0,4.0,0.0,1.0,.000,0.0,0.0,,0.0,1.0,.000,.000,2.0,2.0,1.000,2.0,0.0,2.0,2.0,1.0,0.0,0.0,0.0,2.0
+378,Frank Ntilikina,PG,25,CHO,5,0,8.6,0.2,1.8,.111,0.2,1.6,.125,0.0,0.2,.000,.167,0.4,0.4,1.000,0.2,1.0,1.2,0.8,0.0,0.0,0.2,0.8,1.0
+379,Jusuf Nurkić,C,29,PHO,76,76,27.3,4.2,8.3,.510,0.3,1.2,.244,3.9,7.1,.555,.528,2.2,3.4,.640,2.9,8.1,11.0,4.0,1.1,1.1,2.3,3.3,10.9
+380,Jordan Nwora,SF,25,TOT,52,1,13.7,2.7,5.8,.462,0.8,2.5,.336,1.8,3.2,.560,.535,0.8,1.0,.830,0.5,2.3,2.9,1.2,0.5,0.3,1.0,0.9,7.0
+380,Jordan Nwora,SF,25,IND,18,0,10.2,2.1,4.6,.451,0.6,2.0,.306,1.4,2.6,.565,.518,0.5,0.6,.818,0.4,1.4,1.8,1.0,0.3,0.1,0.7,1.1,5.2
+380,Jordan Nwora,SF,25,TOR,34,1,15.6,3.0,6.4,.465,1.0,2.8,.347,2.0,3.6,.557,.541,1.0,1.2,.833,0.6,2.8,3.4,1.3,0.6,0.4,1.2,0.9,7.9
+381,Royce O'Neale,SF,30,TOT,79,14,24.7,2.7,6.8,.397,2.0,5.3,.370,0.7,1.5,.496,.542,0.3,0.4,.686,0.8,4.0,4.8,2.8,0.7,0.6,0.9,2.2,7.7
+381,Royce O'Neale,SF,30,BRK,49,6,24.5,2.6,6.6,.388,2.0,5.4,.366,0.6,1.2,.483,.537,0.3,0.4,.682,0.8,3.7,4.5,2.8,0.7,0.6,1.0,2.4,7.4
+381,Royce O'Neale,SF,30,PHO,30,8,25.1,2.9,7.1,.411,2.0,5.2,.376,1.0,1.9,.509,.549,0.3,0.4,.692,0.7,4.5,5.2,2.7,0.9,0.5,0.7,1.8,8.1
+382,Chuma Okeke,SF,25,ORL,47,8,9.2,0.9,2.4,.357,0.5,1.7,.280,0.4,0.6,.567,.460,0.1,0.1,.571,0.4,1.3,1.7,0.4,0.2,0.1,0.3,0.4,2.3
+383,Josh Okogie,SG,25,PHO,60,11,16.0,1.4,3.4,.417,0.5,1.6,.309,1.0,1.9,.509,.488,1.3,1.7,.745,1.2,1.3,2.6,1.1,0.8,0.4,0.6,1.1,4.6
+384,Onyeka Okongwu,C,23,ATL,55,8,25.5,4.1,6.6,.611,0.4,1.3,.333,3.6,5.4,.676,.642,1.7,2.1,.793,2.6,4.2,6.8,1.3,0.5,1.1,0.8,2.9,10.2
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+386,Kelly Olynyk,C,32,UTA,50,8,20.4,2.9,5.2,.562,0.7,1.7,.429,2.2,3.5,.626,.632,1.6,1.9,.842,1.3,3.8,5.1,4.4,0.7,0.2,1.6,2.6,8.1
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+395,Filip Petrušev,C,23,PHI,1,0,3.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,,0.0,0.0,,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
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+402,Aleksej Pokusevski,PF,22,OKC,10,0,6.0,0.4,1.6,.250,0.2,1.1,.182,0.2,0.5,.400,.313,0.2,0.4,.500,0.2,0.8,1.0,0.5,0.1,0.1,0.3,0.5,1.2
+402,Aleksej Pokusevski,PF,22,CHO,18,0,19.2,2.5,5.8,.429,0.9,2.4,.364,1.6,3.4,.475,.505,1.6,2.1,.757,0.8,3.6,4.4,1.7,0.8,0.7,0.9,0.9,7.4
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+407,Otto Porter Jr.,SF,30,TOR,15,1,11.6,0.9,2.2,.424,0.5,1.5,.348,0.4,0.7,.600,.545,0.2,0.2,1.000,0.4,1.5,1.9,0.5,0.3,0.3,0.1,1.3,2.6
+408,Bobby Portis,PF,28,MIL,82,4,24.5,5.7,11.2,.508,1.2,3.0,.407,4.5,8.2,.545,.562,1.2,1.5,.790,1.7,5.7,7.4,1.3,0.8,0.4,1.1,2.3,13.8
+409,Kristaps Porziņģis,C,28,BOS,57,57,29.6,6.8,13.2,.516,1.9,5.1,.375,4.9,8.1,.606,.589,4.5,5.3,.858,1.7,5.5,7.2,2.0,0.7,1.9,1.6,2.7,20.1
+410,Micah Potter,PF,25,UTA,16,0,11.6,1.2,2.5,.475,0.6,1.3,.429,0.6,1.2,.526,.588,0.4,0.5,.750,0.6,2.1,2.7,0.4,0.3,0.4,0.3,0.9,3.3
+411,Dwight Powell,C,32,DAL,63,9,13.3,1.1,1.7,.679,0.0,0.0,.333,1.1,1.6,.689,.684,1.0,1.4,.708,1.5,1.9,3.4,1.3,0.4,0.3,0.5,1.8,3.3
+412,Norman Powell,SG,30,LAC,76,3,26.2,4.9,10.1,.486,2.2,5.1,.435,2.7,5.1,.536,.594,1.9,2.3,.831,0.2,2.4,2.6,1.1,0.6,0.3,0.9,1.8,13.9
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+418,Trevelin Queen,SG,26,ORL,14,0,11.8,1.0,2.7,.368,0.2,1.2,.176,0.8,1.5,.524,.408,0.6,0.9,.750,0.2,1.2,1.4,1.3,0.5,0.4,0.5,1.1,2.9
+419,Neemias Queta,C,24,BOS,28,0,11.9,2.4,3.7,.644,0.0,0.0,,2.4,3.7,.644,.644,0.7,1.0,.714,1.9,2.5,4.4,0.7,0.5,0.8,0.5,1.8,5.5
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+420,Immanuel Quickley,SG,24,NYK,30,0,24.0,5.1,11.2,.454,2.1,5.4,.395,2.9,5.8,.509,.549,2.7,3.1,.872,0.2,2.4,2.6,2.5,0.5,0.1,1.0,1.5,15.0
+420,Immanuel Quickley,PG,24,TOR,38,38,33.3,6.2,14.7,.422,2.8,7.1,.395,3.4,7.6,.448,.518,3.3,4.0,.841,0.3,4.5,4.8,6.8,0.9,0.2,1.8,2.0,18.6
+421,Lester Quinones,SG,23,GSW,37,0,10.6,1.5,3.8,.397,0.9,2.4,.364,0.6,1.4,.453,.511,0.5,0.8,.690,0.6,1.4,1.9,1.0,0.2,0.1,0.5,1.2,4.4
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+461,Ben Sheppard,SG,22,IND,57,1,14.3,1.6,4.0,.393,0.9,2.7,.314,0.7,1.3,.562,.500,0.4,0.5,.885,0.3,1.3,1.6,0.9,0.6,0.0,0.3,1.5,4.4
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+471,Marcus Smart,PG,29,MEM,20,20,30.3,5.1,11.9,.430,2.1,6.7,.313,3.0,5.2,.583,.519,2.2,2.8,.768,0.3,2.4,2.7,4.3,2.1,0.3,3.1,2.7,14.5
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+481,Jaden Springer,SG,21,PHI,32,1,11.8,1.4,3.7,.390,0.3,1.2,.216,1.2,2.5,.469,.424,0.9,1.1,.824,0.8,1.0,1.8,1.1,0.8,0.3,0.8,1.5,4.0
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+482,Lamar Stevens,PF,26,MEM,19,2,23.0,4.6,10.3,.446,0.6,2.0,.289,4.0,8.3,.484,.474,1.8,2.3,.791,1.3,3.8,5.1,1.1,0.9,0.9,0.8,1.6,11.5
+483,Isaiah Stewart,PF,22,DET,46,45,30.9,4.0,8.3,.487,1.5,3.8,.383,2.6,4.5,.575,.575,1.4,1.8,.753,1.6,5.0,6.6,1.6,0.4,0.8,1.4,2.2,10.9
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+487,Cole Swider,SF,24,MIA,18,0,4.8,0.8,2.1,.395,0.6,1.7,.333,0.3,0.4,.625,.526,0.1,0.1,1.000,0.1,0.3,0.4,0.3,0.1,0.1,0.4,0.2,2.3
+488,Jae'Sean Tate,SF,28,HOU,65,9,15.9,1.7,3.5,.472,0.4,1.2,.299,1.3,2.3,.559,.522,0.4,0.6,.667,0.9,2.1,3.0,1.0,0.6,0.2,0.6,2.1,4.1
+489,Jayson Tatum,PF,25,BOS,74,74,35.7,9.1,19.3,.471,3.1,8.2,.376,6.0,11.0,.542,.552,5.6,6.7,.833,0.9,7.2,8.1,4.9,1.0,0.6,2.5,2.0,26.9
+490,Terry Taylor,PF,24,CHI,31,0,6.1,0.6,1.3,.513,0.1,0.3,.222,0.6,1.0,.600,.538,0.1,0.2,.800,0.5,0.6,1.2,0.3,0.2,0.1,0.2,0.5,1.5
+491,Garrett Temple,SG,37,TOR,27,2,10.7,1.1,2.9,.372,0.4,1.5,.300,0.6,1.4,.447,.449,0.7,0.8,.818,0.7,1.0,1.7,1.0,0.4,0.1,0.5,1.0,3.3
+492,Dalen Terry,SG,21,CHI,59,2,11.5,1.2,2.7,.439,0.3,1.3,.230,0.9,1.4,.627,.494,0.4,0.7,.581,0.5,1.4,1.9,1.4,0.5,0.3,0.5,1.4,3.1
+493,Daniel Theis,C,31,TOT,60,3,16.9,2.6,4.9,.532,0.4,1.2,.366,2.2,3.7,.586,.577,0.6,0.8,.760,1.4,2.7,4.1,1.0,0.4,0.9,0.7,2.1,6.3
+493,Daniel Theis,C,31,IND,1,0,8.0,1.0,4.0,.250,0.0,1.0,.000,1.0,3.0,.333,.250,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0
+493,Daniel Theis,C,31,LAC,59,3,17.1,2.6,4.9,.536,0.4,1.2,.371,2.2,3.7,.589,.581,0.6,0.8,.760,1.4,2.7,4.1,1.0,0.4,0.9,0.7,2.1,6.3
+494,Cam Thomas,SG,22,BRK,66,51,31.4,8.0,18.0,.442,2.2,6.0,.364,5.8,12.0,.481,.503,4.3,5.1,.856,0.4,2.8,3.2,2.9,0.7,0.2,1.9,2.1,22.5
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+496,Amen Thompson,SF,21,HOU,62,23,22.4,3.9,7.2,.536,0.1,0.9,.138,3.7,6.3,.595,.545,1.7,2.5,.684,2.4,4.2,6.6,2.6,1.3,0.6,1.5,2.3,9.5
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+498,Klay Thompson,SF,33,GSW,77,63,29.7,6.4,14.7,.432,3.5,9.0,.387,2.9,5.7,.503,.551,1.6,1.8,.927,0.5,2.8,3.3,2.3,0.6,0.5,1.5,1.6,17.9
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+501,Matisse Thybulle,SF,26,POR,65,19,22.9,1.9,4.9,.397,1.2,3.6,.346,0.7,1.3,.535,.524,0.3,0.4,.759,0.5,1.6,2.1,1.4,1.7,0.8,0.6,1.4,5.4
+502,Xavier Tillman Sr.,C-PF,25,TOT,54,15,18.0,2.2,5.2,.434,0.4,1.5,.247,1.9,3.7,.510,.470,0.4,0.9,.440,1.3,2.6,3.9,1.4,0.9,0.8,0.6,1.4,5.3
+502,Xavier Tillman Sr.,C,25,MEM,34,13,20.6,2.6,6.3,.408,0.4,1.6,.226,2.2,4.7,.469,.437,0.5,1.3,.419,1.6,3.0,4.6,1.7,1.2,1.0,0.9,1.8,6.0
+502,Xavier Tillman Sr.,PF,25,BOS,20,2,13.7,1.7,3.3,.515,0.4,1.4,.286,1.3,1.9,.684,.576,0.2,0.4,.571,0.7,2.0,2.7,1.0,0.5,0.5,0.3,0.8,4.0
+503,Jacob Toppin,SF,23,NYK,9,0,4.2,0.6,1.0,.556,0.1,0.4,.250,0.4,0.6,.800,.611,0.2,0.2,1.000,0.3,0.4,0.8,0.3,0.0,0.1,0.1,0.3,1.4
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+506,Karl-Anthony Towns,PF,28,MIN,62,62,32.7,7.7,15.3,.504,2.2,5.3,.416,5.5,10.1,.550,.575,4.1,4.7,.873,1.5,6.8,8.3,3.0,0.7,0.7,2.9,3.3,21.8
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+547,Alondes Williams,SG,24,MIA,7,0,2.3,0.3,1.1,.250,0.0,0.6,.000,0.3,0.6,.500,.250,0.1,0.1,1.000,0.0,0.1,0.1,0.0,0.0,0.1,0.3,0.3,0.7
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+549,Grant Williams,PF,25,DAL,47,33,26.4,2.8,6.7,.413,1.7,4.5,.376,1.1,2.3,.486,.538,0.9,1.2,.745,1.0,2.6,3.6,1.7,0.5,0.6,1.1,2.9,8.1
+549,Grant Williams,PF,25,CHO,29,10,30.6,5.0,9.9,.503,1.6,4.3,.373,3.4,5.6,.605,.585,2.2,2.9,.765,1.4,3.8,5.1,3.2,0.7,0.4,1.9,3.1,13.9
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+552,Jeenathan Williams,SG,24,HOU,22,0,5.9,1.2,2.3,.540,0.1,0.2,.400,1.1,2.0,.556,.560,0.3,0.6,.538,0.5,0.5,1.0,0.3,0.2,0.0,0.2,0.5,2.9
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+556,Patrick Williams,PF,22,CHI,43,30,27.3,3.7,8.3,.443,1.4,3.4,.399,2.3,4.9,.474,.525,1.2,1.5,.788,1.1,2.8,3.9,1.5,0.9,0.8,1.3,2.0,10.0
+557,Robert Williams,C,26,POR,6,0,19.8,2.8,4.3,.654,0.0,0.0,,2.8,4.3,.654,.654,1.2,1.5,.778,1.8,4.5,6.3,0.8,1.2,1.2,1.2,2.8,6.8
+558,Vince Williams Jr.,SG,23,MEM,52,33,27.6,3.3,7.3,.446,1.5,4.0,.378,1.7,3.3,.529,.550,2.0,2.5,.800,1.1,4.4,5.6,3.4,0.9,0.7,1.8,2.6,10.0
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+563,Dylan Windler,SF,27,TOT,17,0,6.4,0.9,1.8,.500,0.8,1.6,.481,0.1,0.2,.667,.717,0.0,0.0,,0.2,0.8,0.9,0.6,0.1,0.0,0.1,0.4,2.5
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diff --git a/exercicios/para-casa/nba-player-data.csv b/exercicios/para-casa/nba-player-data.csv
new file mode 100644
index 0000000..06d1db9
--- /dev/null
+++ b/exercicios/para-casa/nba-player-data.csv
@@ -0,0 +1,737 @@
+Rk,Player,Pos,Age,Tm,G,GS,MP,FG,FGA,FG%,3P,3PA,3P%,2P,2PA,2P%,eFG%,FT,FTA,FT%,ORB,DRB,TRB,AST,STL,BLK,TOV,PF,PTS
+1,Precious Achiuwa,PF-C,24,TOT,74,18,21.9,3.2,6.3,.501,0.4,1.3,.268,2.8,5.0,.562,.529,0.9,1.5,.616,2.6,4.0,6.6,1.3,0.6,0.9,1.1,1.9,7.6
+1,Precious Achiuwa,C,24,TOR,25,0,17.5,3.1,6.8,.459,0.5,1.9,.277,2.6,4.9,.528,.497,1.0,1.7,.571,2.0,3.4,5.4,1.8,0.6,0.5,1.2,1.6,7.7
+1,Precious Achiuwa,PF,24,NYK,49,18,24.2,3.2,6.1,.525,0.3,1.0,.260,2.9,5.1,.578,.547,0.9,1.4,.643,2.9,4.3,7.2,1.1,0.6,1.1,1.1,2.1,7.6
+2,Bam Adebayo,C,26,MIA,71,71,34.0,7.5,14.3,.521,0.2,0.6,.357,7.3,13.7,.528,.529,4.1,5.5,.755,2.2,8.1,10.4,3.9,1.1,0.9,2.3,2.2,19.3
+3,Ochai Agbaji,SG,23,TOT,78,28,21.0,2.3,5.6,.411,0.8,2.7,.294,1.5,2.8,.523,.483,0.5,0.7,.661,0.9,1.8,2.8,1.1,0.6,0.6,0.8,1.5,5.8
+3,Ochai Agbaji,SG,23,UTA,51,10,19.7,2.1,4.9,.426,0.9,2.8,.331,1.2,2.1,.551,.520,0.3,0.4,.750,0.7,1.8,2.5,0.9,0.5,0.6,0.7,1.3,5.4
+3,Ochai Agbaji,SG,23,TOR,27,18,23.6,2.7,6.8,.391,0.6,2.6,.217,2.1,4.3,.496,.432,0.8,1.3,.611,1.4,1.9,3.3,1.3,0.7,0.6,1.1,1.9,6.7
+4,Santi Aldama,PF,23,MEM,61,35,26.5,4.0,9.3,.435,1.7,5.0,.349,2.3,4.3,.534,.528,0.9,1.4,.621,1.2,4.6,5.8,2.3,0.7,0.9,1.1,1.5,10.7
+5,Nickeil Alexander-Walker,SG,25,MIN,82,20,23.4,2.9,6.6,.439,1.6,4.1,.391,1.3,2.5,.517,.560,0.6,0.8,.800,0.4,1.6,2.0,2.5,0.8,0.5,0.9,1.7,8.0
+6,Grayson Allen,SG,28,PHO,75,74,33.5,4.5,9.1,.499,2.7,5.9,.461,1.8,3.2,.570,.649,1.7,2.0,.878,0.6,3.3,3.9,3.0,0.9,0.6,1.3,2.1,13.5
+7,Jarrett Allen,C,25,CLE,77,77,31.7,6.7,10.6,.634,0.0,0.1,.000,6.7,10.6,.638,.634,3.0,4.1,.742,3.2,7.4,10.5,2.7,0.7,1.1,1.6,1.9,16.5
+8,Timmy Allen,SF,24,MEM,5,0,25.0,1.2,4.6,.261,0.0,1.4,.000,1.2,3.2,.375,.261,0.2,0.4,.500,0.8,2.6,3.4,1.0,0.8,0.0,0.4,3.6,2.6
+9,Jose Alvarado,PG,25,NOP,56,0,18.4,2.5,6.2,.412,1.4,3.7,.377,1.1,2.5,.464,.525,0.6,0.9,.673,0.4,1.8,2.3,2.1,1.1,0.3,0.7,1.6,7.1
+10,Kyle Anderson,PF,30,MIN,79,10,22.6,2.5,5.5,.460,0.1,0.6,.229,2.4,4.9,.488,.472,1.2,1.7,.708,0.8,2.7,3.5,4.2,0.9,0.6,1.2,1.6,6.4
+11,Giannis Antetokounmpo,PF,29,MIL,73,73,35.2,11.5,18.8,.611,0.5,1.7,.274,11.0,17.1,.645,.624,7.0,10.7,.657,2.7,8.8,11.5,6.5,1.2,1.1,3.4,2.9,30.4
+12,Thanasis Antetokounmpo,PF,31,MIL,34,0,4.6,0.5,0.9,.533,0.0,0.0,.000,0.5,0.9,.552,.533,0.0,0.1,.000,0.2,0.2,0.4,0.5,0.2,0.1,0.4,0.7,0.9
+13,Cole Anthony,PG,23,ORL,81,0,22.4,4.1,9.4,.435,1.1,3.4,.338,3.0,6.0,.490,.496,2.2,2.7,.826,0.8,3.0,3.8,2.9,0.8,0.5,1.6,2.2,11.6
+14,OG Anunoby,SF,26,TOT,50,50,34.0,5.6,11.5,.489,2.0,5.3,.382,3.6,6.2,.581,.577,1.3,1.8,.753,0.9,3.2,4.2,2.1,1.4,0.7,1.6,2.4,14.7
+14,OG Anunoby,SF,26,TOR,27,27,33.3,5.8,11.9,.489,2.3,6.0,.374,3.6,5.9,.608,.584,1.2,1.7,.717,0.9,3.0,3.9,2.7,1.0,0.5,1.6,2.3,15.1
+14,OG Anunoby,SF,26,NYK,23,23,34.9,5.4,11.1,.488,1.8,4.5,.394,3.7,6.6,.553,.568,1.5,1.9,.791,1.0,3.4,4.4,1.5,1.7,1.0,1.7,2.5,14.1
+15,Ryan Arcidiacono,PG,29,NYK,20,0,2.3,0.0,0.3,.000,0.0,0.3,.000,0.0,0.0,,.000,0.0,0.0,,0.0,0.4,0.4,0.2,0.1,0.0,0.1,0.3,0.0
+16,Deni Avdija,SF,23,WAS,75,75,30.1,5.4,10.7,.506,1.2,3.1,.374,4.2,7.6,.560,.560,2.7,3.6,.740,1.1,6.1,7.2,3.8,0.8,0.5,2.1,2.5,14.7
+17,Deandre Ayton,C,25,POR,55,55,32.4,7.8,13.6,.570,0.0,0.2,.100,7.7,13.4,.576,.571,1.2,1.4,.823,3.2,7.9,11.1,1.6,1.0,0.8,1.8,2.0,16.7
+18,Udoka Azubuike,C,24,PHO,16,0,7.1,1.0,1.4,.696,0.0,0.0,,1.0,1.4,.696,.696,0.2,0.8,.231,0.7,1.3,2.0,0.2,0.1,0.4,0.3,1.1,2.2
+19,Ibou Badji,C,21,POR,22,1,10.3,0.6,1.0,.636,0.0,0.0,,0.6,1.0,.636,.636,0.3,0.5,.500,0.9,1.4,2.3,0.6,0.1,0.9,0.7,2.1,1.5
+20,Marvin Bagley III,C,24,TOT,50,25,21.1,4.8,8.2,.586,0.2,0.5,.391,4.6,7.7,.597,.597,1.9,2.5,.762,2.6,3.6,6.2,1.1,0.4,0.7,1.2,1.6,11.7
+20,Marvin Bagley III,C,24,DET,26,10,18.4,4.1,7.0,.591,0.0,0.2,.167,4.1,6.7,.606,.594,1.9,2.3,.820,1.8,2.8,4.5,1.0,0.2,0.5,1.1,1.5,10.2
+20,Marvin Bagley III,C,24,WAS,24,15,24.0,5.5,9.5,.581,0.3,0.7,.471,5.2,8.8,.590,.599,1.9,2.7,.708,3.5,4.6,8.1,1.2,0.6,0.8,1.3,1.7,13.3
+Rk,Player,Pos,Age,Tm,G,GS,MP,FG,FGA,FG%,3P,3PA,3P%,2P,2PA,2P%,eFG%,FT,FTA,FT%,ORB,DRB,TRB,AST,STL,BLK,TOV,PF,PTS
+21,Amari Bailey,PG,19,CHO,10,0,6.5,0.8,2.4,.333,0.1,0.8,.125,0.7,1.6,.438,.354,0.6,0.7,.857,0.6,0.3,0.9,0.7,0.3,0.0,0.4,0.2,2.3
+22,Patrick Baldwin Jr.,SF,21,WAS,38,7,13.0,1.6,4.1,.381,0.8,2.6,.320,0.7,1.5,.483,.481,0.5,0.7,.679,0.4,2.8,3.2,0.8,0.5,0.4,0.5,1.5,4.4
+23,LaMelo Ball,PG,22,CHO,22,22,32.3,8.3,19.2,.433,3.2,9.0,.355,5.1,10.3,.500,.515,4.1,4.7,.865,1.3,3.8,5.1,8.0,1.8,0.2,3.8,3.6,23.9
+24,Mo Bamba,C,25,PHI,57,17,13.0,1.7,3.5,.490,0.4,1.1,.391,1.3,2.4,.537,.553,0.6,0.9,.680,1.4,2.8,4.2,0.7,0.4,1.1,0.7,1.8,4.4
+25,Paolo Banchero,PF,21,ORL,80,80,35.0,8.0,17.6,.455,1.5,4.4,.339,6.5,13.2,.493,.497,5.1,7.0,.725,1.0,5.9,6.9,5.4,0.9,0.6,3.1,1.9,22.6
+26,Desmond Bane,SG,25,MEM,42,42,34.4,8.6,18.5,.464,3.3,8.6,.381,5.3,9.9,.536,.552,3.3,3.8,.870,0.9,3.5,4.4,5.5,1.0,0.5,2.7,2.9,23.7
+27,Dalano Banton,PG,24,TOT,54,9,19.4,3.7,9.3,.404,1.1,3.7,.296,2.6,5.6,.475,.463,1.7,2.2,.783,0.8,2.5,3.3,2.4,0.6,0.4,1.4,1.2,10.3
+27,Dalano Banton,PG,24,BOS,24,1,7.1,0.8,2.1,.373,0.1,0.7,.125,0.7,1.5,.486,.392,0.7,0.8,.800,0.5,1.0,1.5,0.8,0.2,0.1,0.4,0.8,2.3
+27,Dalano Banton,PG,24,POR,30,8,29.2,6.1,15.0,.408,1.9,6.1,.311,4.2,8.9,.474,.471,2.6,3.3,.780,1.0,3.8,4.8,3.6,0.9,0.6,2.2,1.5,16.7
+28,Dominick Barlow,PF,20,SAS,33,1,12.7,1.7,3.4,.496,0.1,0.3,.333,1.6,3.2,.510,.509,0.9,1.3,.690,1.4,2.1,3.4,1.1,0.4,0.4,0.3,1.4,4.4
+29,Harrison Barnes,PF,31,SAC,82,82,29.0,4.2,8.9,.474,1.8,4.7,.387,2.4,4.2,.571,.576,1.9,2.4,.801,0.8,2.2,3.0,1.2,0.7,0.1,0.7,1.2,12.2
+30,Scottie Barnes,SG,22,TOR,60,60,34.9,7.5,15.7,.475,1.7,4.9,.341,5.8,10.8,.536,.528,3.3,4.2,.781,2.4,5.9,8.2,6.1,1.3,1.5,2.8,2.0,19.9
+31,RJ Barrett,SG,23,TOT,58,58,31.7,7.5,15.2,.495,1.6,4.3,.360,6.0,10.9,.549,.547,3.6,5.0,.715,0.9,4.5,5.4,3.3,0.5,0.4,2.2,2.2,20.2
+31,RJ Barrett,SG,23,NYK,26,26,29.5,6.3,14.9,.423,1.7,5.0,.331,4.7,9.9,.469,.478,4.0,4.8,.831,0.7,3.5,4.3,2.4,0.5,0.3,1.8,1.7,18.2
+31,RJ Barrett,SG,23,TOR,32,32,33.5,8.5,15.4,.553,1.5,3.8,.392,7.0,11.6,.605,.601,3.3,5.2,.629,1.1,5.3,6.4,4.1,0.6,0.4,2.5,2.6,21.8
+32,Charles Bassey,C,23,SAS,19,0,10.8,1.5,2.1,.725,0.0,0.1,.000,1.5,2.1,.744,.725,0.3,0.3,.833,1.2,2.8,4.0,1.1,0.4,0.9,0.8,1.6,3.3
+33,Emoni Bates,SF,20,CLE,15,0,8.9,1.0,3.3,.306,0.7,2.2,.303,0.3,1.1,.313,.408,0.1,0.3,.250,0.1,0.9,0.9,0.7,0.1,0.1,0.5,0.3,2.7
+34,Keita Bates-Diop,SF,28,TOT,53,8,12.5,1.4,3.1,.434,0.4,1.3,.304,1.0,1.8,.526,.497,0.6,0.8,.762,0.6,1.5,2.1,0.7,0.5,0.4,0.3,0.7,3.7
+34,Keita Bates-Diop,SF,28,PHO,39,8,15.3,1.6,3.8,.427,0.5,1.6,.313,1.1,2.2,.512,.493,0.7,0.9,.722,0.8,1.8,2.6,0.9,0.6,0.5,0.3,0.8,4.5
+34,Keita Bates-Diop,SF,28,BRK,14,0,4.9,0.6,1.1,.500,0.1,0.4,.200,0.5,0.8,.636,.531,0.4,0.4,1.000,0.1,0.6,0.6,0.3,0.2,0.1,0.1,0.1,1.6
+35,Nicolas Batum,PF,35,TOT,60,38,25.5,1.9,4.3,.453,1.2,3.1,.395,0.7,1.2,.606,.596,0.3,0.4,.714,1.2,3.0,4.1,2.1,0.8,0.6,0.7,1.8,5.3
+35,Nicolas Batum,PF,35,LAC,3,0,18.0,1.0,2.7,.375,0.7,2.3,.286,0.3,0.3,1.000,.500,0.0,0.0,,0.0,2.3,2.3,1.7,1.0,1.3,0.3,1.7,2.7
+35,Nicolas Batum,PF,35,PHI,57,38,25.9,2.0,4.4,.456,1.2,3.1,.399,0.7,1.2,.600,.599,0.3,0.4,.714,1.2,3.0,4.2,2.2,0.8,0.6,0.7,1.8,5.5
+36,Darius Bazley,PF,23,TOT,9,0,16.9,2.0,3.3,.600,0.2,0.9,.250,1.8,2.4,.727,.633,1.1,1.3,.833,0.9,2.2,3.1,0.8,0.7,0.8,0.9,0.3,5.3
+36,Darius Bazley,PF,23,PHI,3,0,3.3,0.0,0.3,.000,0.0,0.0,,0.0,0.3,.000,.000,0.0,0.0,,0.0,0.3,0.3,0.7,0.0,0.0,0.3,0.0,0.0
+36,Darius Bazley,PF,23,UTA,6,0,23.7,3.0,4.8,.621,0.3,1.3,.250,2.7,3.5,.762,.655,1.7,2.0,.833,1.3,3.2,4.5,0.8,1.0,1.2,1.2,0.5,8.0
+37,Bradley Beal,SG,30,PHO,53,53,33.3,7.1,13.9,.513,1.9,4.4,.430,5.2,9.4,.552,.582,2.1,2.5,.813,1.0,3.4,4.4,5.0,1.0,0.5,2.5,2.4,18.2
+38,Malik Beasley,SG,27,MIL,79,77,29.6,4.1,9.1,.443,2.8,6.9,.413,1.2,2.3,.533,.598,0.3,0.4,.714,0.5,3.2,3.7,1.4,0.7,0.1,0.7,2.0,11.3
+39,MarJon Beauchamp,SF,23,MIL,48,1,12.7,1.7,3.5,.488,0.6,1.6,.400,1.1,1.9,.560,.578,0.4,0.6,.679,0.5,1.6,2.1,0.6,0.3,0.1,0.6,1.1,4.4
+40,Jules Bernard,SG,24,WAS,19,0,7.8,1.5,3.4,.453,0.6,1.5,.379,0.9,1.8,.514,.539,0.3,0.5,.556,0.5,0.8,1.4,0.8,0.2,0.1,0.4,0.7,3.9
+41,Dāvis Bertāns,PF,31,TOT,43,1,15.7,2.1,5.4,.393,1.8,4.7,.380,0.4,0.8,.471,.556,0.7,0.8,.909,0.2,1.2,1.4,0.8,0.6,0.3,0.4,1.3,6.7
+41,Dāvis Bertāns,PF,31,OKC,15,0,6.1,0.7,1.7,.385,0.7,1.6,.417,0.0,0.1,.000,.577,0.9,1.0,.933,0.1,0.5,0.7,0.6,0.2,0.2,0.1,0.7,2.9
+41,Dāvis Bertāns,PF,31,CHO,28,1,20.8,2.9,7.4,.394,2.4,6.3,.375,0.6,1.1,.500,.553,0.6,0.6,.889,0.3,1.6,1.8,0.9,0.8,0.3,0.6,1.6,8.8
+42,Patrick Beverley,PG,35,TOT,73,13,20.0,2.2,5.2,.417,0.8,2.5,.337,1.3,2.7,.490,.497,1.0,1.2,.822,0.7,2.6,3.3,2.9,0.6,0.4,0.9,1.8,6.2
+42,Patrick Beverley,PG,35,PHI,47,5,19.6,2.2,5.1,.432,0.7,2.3,.321,1.5,2.8,.523,.504,1.1,1.3,.810,0.6,2.4,3.1,3.1,0.5,0.4,1.0,1.8,6.3
+42,Patrick Beverley,PG,35,MIL,26,8,20.9,2.1,5.3,.391,1.0,2.8,.361,1.1,2.5,.424,.486,0.9,1.0,.852,0.7,2.9,3.6,2.6,0.7,0.5,0.6,1.7,6.0
+43,Saddiq Bey,SF,24,ATL,63,51,32.7,4.6,11.1,.416,1.8,5.7,.316,2.8,5.4,.522,.497,2.7,3.2,.837,2.7,3.9,6.5,1.5,0.8,0.2,0.9,1.4,13.7
+44,Goga Bitadze,C,24,ORL,62,33,15.4,2.0,3.4,.603,0.0,0.2,.143,2.0,3.1,.636,.608,0.9,1.4,.655,1.8,2.9,4.6,1.3,0.5,1.2,0.7,1.7,5.0
+45,Onuralp Bitim,SG,24,CHI,23,1,11.7,1.4,3.7,.381,0.5,1.9,.273,0.9,1.7,.500,.452,0.2,0.2,.800,0.3,1.1,1.4,0.6,0.1,0.1,0.4,1.0,3.5
+46,Bismack Biyombo,C,31,TOT,40,27,19.8,1.9,3.3,.565,0.0,0.0,,1.9,3.3,.565,.565,0.7,1.4,.481,1.6,3.7,5.2,1.3,0.3,0.9,1.1,2.2,4.4
+46,Bismack Biyombo,C,31,MEM,30,27,23.9,2.2,4.0,.563,0.0,0.0,,2.2,4.0,.563,.563,0.7,1.5,.478,1.9,4.5,6.4,1.7,0.3,1.1,1.3,2.7,5.2
+46,Bismack Biyombo,C,31,OKC,10,0,7.3,0.7,1.2,.583,0.0,0.0,,0.7,1.2,.583,.583,0.4,0.8,.500,0.5,1.3,1.8,0.2,0.1,0.3,0.4,0.7,1.8
+47,Anthony Black,PG,20,ORL,69,33,16.9,1.7,3.6,.466,0.5,1.4,.394,1.1,2.2,.510,.540,0.7,1.2,.613,0.5,1.5,2.0,1.3,0.5,0.3,0.8,1.6,4.6
+48,Leaky Black,SF,24,CHO,26,3,10.9,1.0,2.0,.481,0.3,0.8,.450,0.6,1.2,.500,.567,0.4,0.6,.667,0.3,1.5,1.8,0.9,0.3,0.4,0.2,0.7,2.7
+49,Buddy Boeheim,SF,24,DET,10,0,8.4,0.9,2.9,.310,0.8,2.5,.320,0.1,0.4,.250,.448,0.8,1.0,.800,0.3,0.7,1.0,0.3,0.0,0.1,0.0,0.5,3.4
+50,Bogdan Bogdanović,SG,31,ATL,79,33,30.4,6.0,13.9,.428,3.0,8.1,.374,2.9,5.8,.503,.537,1.9,2.1,.921,0.7,2.8,3.4,3.1,1.2,0.3,1.4,2.3,16.9
+51,Bojan Bogdanović,SF-PF,34,TOT,57,27,25.9,5.4,11.9,.454,2.3,5.9,.398,3.1,6.1,.507,.551,2.1,2.6,.785,0.4,2.3,2.7,1.7,0.5,0.1,1.8,1.7,15.2
+51,Bojan Bogdanović,PF,34,DET,28,27,32.9,7.1,15.2,.468,3.1,7.4,.415,4.0,7.8,.518,.569,2.9,3.7,.779,0.5,2.9,3.4,2.5,0.8,0.1,2.3,2.0,20.2
+51,Bojan Bogdanović,SF,34,NYK,29,0,19.2,3.8,8.8,.430,1.6,4.4,.370,2.2,4.4,.488,.521,1.2,1.6,.800,0.3,1.7,2.0,0.9,0.2,0.0,1.3,1.3,10.4
+52,Bol Bol,PF,24,PHO,43,0,10.9,2.2,3.5,.616,0.5,1.2,.423,1.7,2.3,.717,.689,0.3,0.4,.789,0.7,2.4,3.2,0.4,0.2,0.6,0.4,1.0,5.2
+53,Marques Bolden,C,25,TOT,11,2,11.0,1.5,2.3,.680,0.0,0.1,.000,1.5,2.2,.708,.680,0.3,0.4,.750,0.9,2.2,3.1,0.4,0.3,0.6,0.3,0.9,3.4
+53,Marques Bolden,C,25,MIL,2,0,1.5,0.0,0.0,,0.0,0.0,,0.0,0.0,,,0.0,0.0,,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.5,0.0
+53,Marques Bolden,C,25,CHO,9,2,13.1,1.9,2.8,.680,0.0,0.1,.000,1.9,2.7,.708,.680,0.3,0.4,.750,1.1,2.4,3.6,0.4,0.3,0.8,0.3,1.0,4.1
+54,Devin Booker,PG,27,PHO,68,68,36.0,9.4,19.2,.492,2.2,6.1,.364,7.2,13.0,.552,.550,6.0,6.7,.886,0.8,3.7,4.5,6.9,0.9,0.4,2.6,3.0,27.1
+55,Brandon Boston Jr.,SF,22,LAC,32,0,10.8,2.0,5.0,.404,0.4,1.6,.269,1.6,3.4,.468,.447,0.7,1.0,.697,0.6,1.0,1.6,0.4,0.3,0.3,0.8,1.3,5.2
+56,Chris Boucher,PF,31,TOR,50,0,14.1,2.2,4.3,.507,0.6,1.9,.330,1.6,2.5,.642,.578,1.4,1.8,.772,1.2,2.9,4.1,0.5,0.3,0.5,0.6,1.4,6.4
+57,James Bouknight,SG,23,CHO,14,0,5.8,1.3,2.9,.439,0.9,2.1,.433,0.4,0.8,.455,.598,0.1,0.1,.500,0.1,0.6,0.6,0.4,0.1,0.1,0.1,0.2,3.6
+58,Jamaree Bouyea,PG,24,TOT,9,0,10.6,1.1,3.1,.357,0.1,1.0,.111,1.0,2.1,.474,.375,0.0,0.0,,0.3,1.8,2.1,1.2,0.1,0.0,1.0,1.1,2.3
+58,Jamaree Bouyea,PG,24,POR,6,0,9.5,0.8,3.5,.238,0.0,1.3,.000,0.8,2.2,.385,.238,0.0,0.0,,0.3,1.3,1.7,1.3,0.0,0.0,1.3,0.5,1.7
+58,Jamaree Bouyea,PG,24,SAS,3,0,12.7,1.7,2.3,.714,0.3,0.3,1.000,1.3,2.0,.667,.786,0.0,0.0,,0.3,2.7,3.0,1.0,0.3,0.0,0.3,2.3,3.7
+59,Malaki Branham,PG,20,SAS,75,29,21.3,3.6,8.4,.432,1.2,3.6,.347,2.4,4.8,.494,.506,0.7,0.8,.873,0.4,1.6,2.0,2.1,0.4,0.1,1.2,1.3,9.2
+60,Christian Braun,SG,22,DEN,82,4,20.2,2.7,5.9,.460,0.8,2.0,.384,1.9,3.9,.498,.525,1.1,1.6,.694,1.0,2.8,3.7,1.6,0.5,0.4,0.7,1.7,7.3
+61,Mikal Bridges,SF,27,BRK,82,82,34.8,6.9,15.8,.436,2.7,7.2,.372,4.2,8.6,.490,.521,3.1,3.9,.814,0.8,3.7,4.5,3.6,1.0,0.4,2.0,1.4,19.6
+62,Miles Bridges,SF,25,CHO,69,67,37.4,8.1,17.5,.462,2.3,6.5,.349,5.8,11.0,.529,.527,2.5,3.1,.825,1.0,6.3,7.3,3.3,0.9,0.5,2.0,1.7,21.0
+63,Oshae Brissett,SF,25,BOS,55,1,11.5,1.2,2.8,.444,0.3,1.0,.273,1.0,1.8,.541,.493,0.9,1.5,.602,1.1,1.8,2.9,0.8,0.3,0.1,0.4,1.0,3.7
+64,Izaiah Brockington,PG,24,NOP,1,0,3.0,2.0,5.0,.400,0.0,1.0,.000,2.0,4.0,.500,.400,0.0,0.0,,1.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,4.0
+65,Malcolm Brogdon,PG,31,POR,39,25,28.7,5.6,12.7,.440,2.1,5.1,.412,3.5,7.6,.458,.522,2.4,3.0,.819,0.7,3.1,3.8,5.5,0.7,0.2,1.5,1.5,15.7
+66,Armoni Brooks,PG,25,BRK,10,0,10.4,1.5,4.6,.326,1.1,3.2,.344,0.4,1.4,.286,.446,0.1,0.2,.500,0.7,1.1,1.8,0.5,0.1,0.1,0.3,0.3,4.2
+67,Dillon Brooks,SF,28,HOU,72,72,30.9,4.6,10.7,.428,1.8,5.1,.359,2.7,5.5,.492,.515,1.7,2.0,.844,0.7,2.7,3.4,1.7,0.9,0.1,1.2,3.3,12.7
+68,Bruce Brown,PG-SG,27,TOT,67,44,27.9,4.1,8.7,.478,0.8,2.4,.323,3.4,6.2,.538,.523,1.7,2.1,.824,0.8,3.4,4.2,2.9,0.9,0.3,1.3,2.2,10.8
+68,Bruce Brown,SG,27,IND,33,33,29.7,4.5,9.5,.475,1.0,3.1,.327,3.5,6.5,.545,.527,2.0,2.5,.817,0.8,3.9,4.7,3.0,1.1,0.2,1.2,2.5,12.1
+68,Bruce Brown,PG,27,TOR,34,11,26.0,3.8,7.9,.481,0.6,1.9,.317,3.2,6.0,.532,.519,1.5,1.8,.833,0.8,3.0,3.8,2.7,0.7,0.3,1.3,2.0,9.6
+69,Charlie Brown Jr.,SG,26,NYK,8,0,4.6,0.3,1.3,.200,0.3,0.9,.286,0.0,0.4,.000,.300,0.0,0.0,,0.1,0.1,0.3,0.0,0.0,0.3,0.3,0.6,0.8
+70,Greg Brown III,PF,22,DAL,6,0,6.7,0.8,1.8,.455,0.2,0.5,.333,0.7,1.3,.500,.500,0.7,1.5,.444,0.5,1.0,1.5,0.7,0.0,0.7,0.7,0.8,2.5
+71,Jaylen Brown,SF,27,BOS,70,70,33.5,9.0,17.9,.499,2.1,5.9,.354,6.9,12.1,.570,.557,3.0,4.3,.703,1.2,4.3,5.5,3.6,1.2,0.5,2.4,2.6,23.0
+72,Kendall Brown,SG,20,IND,15,0,4.2,0.5,1.0,.533,0.0,0.1,.000,0.5,0.9,.615,.533,0.3,0.5,.625,0.2,0.1,0.3,0.3,0.0,0.0,0.1,0.6,1.4
+73,Kobe Brown,PF,24,LAC,44,0,9.0,0.8,2.0,.411,0.3,1.1,.292,0.5,1.0,.548,.489,0.0,0.0,.500,0.5,1.0,1.4,0.6,0.3,0.1,0.2,1.1,2.0
+74,Moses Brown,C,24,POR,22,5,9.1,1.5,3.0,.508,0.0,0.0,,1.5,3.0,.508,.508,0.4,1.4,.290,1.7,2.2,3.9,0.3,0.2,0.3,0.5,1.2,3.4
+75,Troy Brown Jr.,SF,24,TOT,59,15,14.0,1.4,3.8,.372,0.8,2.4,.333,0.6,1.4,.435,.476,0.5,0.6,.865,0.4,2.0,2.4,1.0,0.4,0.1,0.5,0.7,4.2
+75,Troy Brown Jr.,SF,24,MIN,37,3,11.1,1.4,3.2,.441,0.8,2.3,.369,0.6,0.9,.618,.572,0.5,0.6,.864,0.4,1.6,1.9,0.9,0.2,0.1,0.5,0.7,4.2
+75,Troy Brown Jr.,SF,24,DET,22,12,19.0,1.5,4.9,.296,0.7,2.6,.281,0.7,2.3,.314,.370,0.6,0.7,.867,0.5,2.8,3.3,1.1,0.7,0.0,0.5,0.7,4.2
+76,Jalen Brunson,PG,27,NYK,77,77,35.4,10.3,21.4,.479,2.7,6.8,.401,7.5,14.6,.516,.543,5.5,6.5,.847,0.6,3.1,3.6,6.7,0.9,0.2,2.4,1.9,28.7
+77,Thomas Bryant,C,26,MIA,38,4,11.6,2.3,3.9,.577,0.1,0.6,.182,2.2,3.3,.646,.591,1.1,1.2,.872,1.1,2.7,3.7,0.6,0.3,0.4,0.6,1.2,5.7
+78,Kobe Bufkin,SG,20,ATL,17,0,11.5,2.0,5.4,.370,0.5,2.4,.225,1.5,3.1,.481,.418,0.2,0.5,.500,0.4,1.6,1.9,1.6,0.4,0.3,0.6,1.2,4.8
+79,Reggie Bullock,SF,32,HOU,44,0,9.5,0.8,1.9,.415,0.6,1.5,.403,0.2,0.3,.467,.579,0.0,0.0,1.000,0.1,1.5,1.7,0.3,0.3,0.1,0.2,0.7,2.2
+80,Alec Burks,SG,32,TOT,66,1,18.4,3.2,8.6,.369,1.9,5.0,.376,1.3,3.6,.360,.478,2.2,2.5,.868,0.4,1.9,2.3,1.3,0.4,0.2,0.7,1.1,10.4
+80,Alec Burks,SG,32,DET,43,0,21.0,3.7,9.4,.394,2.3,5.7,.401,1.4,3.7,.384,.516,2.8,3.1,.903,0.4,2.2,2.6,1.6,0.5,0.3,0.7,1.5,12.6
+80,Alec Burks,SG,32,NYK,23,1,13.5,2.2,7.1,.307,1.1,3.6,.301,1.1,3.5,.313,.383,1.0,1.4,.727,0.3,1.4,1.7,0.9,0.3,0.0,0.7,0.5,6.5
+81,Jared Butler,SG,23,WAS,40,0,14.2,2.5,5.0,.488,0.6,2.0,.308,1.9,3.1,.602,.547,0.8,0.9,.861,0.2,1.2,1.5,3.2,0.7,0.2,1.3,1.5,6.3
+82,Jimmy Butler,PF,34,MIA,60,60,34.0,6.6,13.2,.499,1.0,2.4,.414,5.6,10.8,.519,.537,6.6,7.7,.858,1.8,3.6,5.3,5.0,1.3,0.3,1.7,1.1,20.8
+83,Jamal Cain,PF,24,MIA,26,1,10.0,1.3,3.0,.430,0.4,1.2,.355,0.9,1.8,.479,.500,0.7,0.9,.783,0.6,0.8,1.4,0.4,0.4,0.2,0.3,0.8,3.7
+84,Kentavious Caldwell-Pope,SG,30,DEN,76,76,31.6,3.6,7.7,.460,1.6,4.1,.406,1.9,3.7,.520,.566,1.3,1.5,.894,0.4,2.0,2.4,2.4,1.3,0.6,1.0,2.0,10.1
+85,Toumani Camara,PF,23,POR,70,49,24.8,2.8,6.3,.450,0.8,2.5,.337,2.0,3.8,.523,.516,1.1,1.4,.758,2.0,2.9,4.9,1.2,0.9,0.5,1.2,2.5,7.5
+86,Clint Capela,C,29,ATL,73,73,25.8,4.8,8.5,.571,0.0,0.0,.000,4.8,8.5,.572,.571,1.8,2.8,.631,4.6,6.0,10.6,1.2,0.6,1.5,1.0,2.2,11.5
+87,Jevon Carter,PG,28,CHI,72,0,13.9,1.9,5.1,.378,1.1,3.3,.329,0.8,1.8,.466,.484,0.1,0.1,.571,0.2,0.7,0.8,1.3,0.5,0.2,0.5,1.0,5.0
+88,Wendell Carter Jr.,C,24,ORL,55,48,25.6,4.1,7.7,.525,1.2,3.1,.374,2.9,4.6,.626,.600,1.7,2.4,.694,2.0,4.9,6.9,1.7,0.6,0.5,1.2,2.2,11.0
+89,D.J. Carton,SG,23,TOR,4,0,9.0,0.8,2.0,.375,0.3,1.0,.250,0.5,1.0,.500,.438,1.0,1.3,.800,0.5,0.5,1.0,0.8,0.3,0.0,0.3,0.8,2.8
+90,Alex Caruso,SG,29,CHI,71,57,28.7,3.6,7.6,.468,1.9,4.7,.408,1.6,2.9,.563,.593,1.0,1.4,.760,0.9,3.0,3.8,3.5,1.7,1.0,1.4,2.5,10.1
+91,Colin Castleton,C,23,LAL,16,0,3.7,0.6,1.0,.563,0.0,0.0,,0.6,1.0,.563,.563,0.4,0.4,1.000,0.4,0.4,0.8,0.2,0.1,0.0,0.2,0.7,1.5
+92,Malcolm Cazalon,SG,22,DET,1,0,3.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+93,Julian Champagnie,SF,22,SAS,74,59,19.8,2.2,5.4,.408,1.4,3.7,.365,0.9,1.7,.504,.534,1.0,1.2,.815,0.6,2.2,2.8,1.4,0.6,0.6,0.8,1.0,6.8
+94,Justin Champagnie,SF,22,WAS,15,1,15.7,2.1,5.2,.410,0.9,3.0,.289,1.3,2.2,.576,.494,0.8,1.0,.800,1.3,2.3,3.5,1.3,0.7,0.6,0.7,0.9,5.9
+95,Max Christie,SG,20,LAL,67,7,14.1,1.5,3.6,.427,0.6,1.8,.356,0.9,1.8,.496,.515,0.5,0.7,.783,0.2,1.9,2.1,0.9,0.3,0.3,0.5,0.9,4.2
+96,Sidy Cissoko,SF,19,SAS,12,0,11.8,1.3,2.8,.485,0.1,1.0,.083,1.3,1.8,.714,.500,1.0,1.3,.800,0.7,1.2,1.8,0.8,0.6,0.3,0.3,1.5,3.8
+97,Brandon Clarke,PF,27,MEM,6,1,22.3,5.5,9.8,.559,0.2,1.0,.167,5.3,8.8,.604,.568,0.2,1.2,.143,2.2,3.2,5.3,1.5,0.8,1.0,0.5,1.3,11.3
+98,Jordan Clarkson,SG,31,UTA,55,19,30.6,6.1,14.8,.413,1.7,5.6,.294,4.5,9.2,.485,.469,3.2,3.7,.881,0.9,2.5,3.4,5.0,0.6,0.1,2.7,1.5,17.1
+99,Nic Claxton,C,24,BRK,71,71,29.8,5.2,8.2,.629,0.0,0.1,.200,5.1,8.1,.633,.630,1.5,2.8,.551,2.7,7.2,9.9,2.1,0.6,2.1,1.3,2.5,11.8
+100,Noah Clowney,PF,19,BRK,23,4,16.1,2.2,4.0,.538,0.5,1.4,.364,1.7,2.6,.633,.602,0.9,1.3,.700,1.1,2.4,3.5,0.8,0.3,0.7,0.7,1.1,5.8
+101,Amir Coffey,SF,26,LAC,70,13,20.9,2.4,5.0,.472,1.0,2.6,.380,1.4,2.4,.571,.571,0.9,1.0,.859,0.4,1.7,2.1,1.1,0.6,0.2,0.5,1.5,6.6
+102,John Collins,PF,26,UTA,68,66,28.0,5.8,11.0,.532,1.3,3.4,.371,4.6,7.6,.605,.590,2.2,2.7,.795,2.3,6.1,8.5,1.1,0.6,0.9,1.4,2.8,15.1
+103,Zach Collins,C,26,SAS,69,29,22.1,4.3,8.9,.484,0.8,2.6,.320,3.5,6.3,.552,.531,1.8,2.3,.753,1.7,3.7,5.4,2.8,0.5,0.8,1.9,3.0,11.2
+104,Mike Conley,PG,36,MIN,76,76,28.9,3.9,8.5,.457,2.4,5.3,.442,1.5,3.1,.481,.595,1.3,1.5,.911,0.5,2.4,2.9,5.9,1.2,0.2,1.3,1.7,11.4
+105,Pat Connaughton,SF,31,MIL,76,3,22.1,2.0,4.6,.435,1.1,3.1,.345,0.9,1.4,.633,.553,0.6,0.8,.759,0.7,2.5,3.1,2.1,0.5,0.3,0.6,1.3,5.6
+106,Bilal Coulibaly,SF,19,WAS,63,15,27.2,3.0,6.9,.435,1.0,2.9,.346,2.0,3.9,.502,.509,1.5,2.1,.702,0.9,3.2,4.1,1.7,0.9,0.8,1.4,2.2,8.4
+107,Ricky Council IV,SG,22,PHI,32,0,9.0,1.7,3.5,.482,0.4,1.0,.375,1.3,2.5,.525,.536,1.7,2.2,.746,0.4,1.0,1.4,0.5,0.3,0.0,0.2,0.5,5.4
+108,Robert Covington,SF-PF,33,TOT,29,6,16.8,1.5,3.4,.439,0.7,1.9,.339,0.8,1.4,.571,.536,0.8,0.9,.846,1.1,2.3,3.3,0.8,1.3,0.6,0.4,1.9,4.4
+108,Robert Covington,PF,33,LAC,3,3,23.0,1.0,3.0,.333,0.7,2.7,.250,0.3,0.3,1.000,.444,0.3,0.7,.500,0.7,2.0,2.7,2.3,2.0,0.7,0.0,2.3,3.0
+108,Robert Covington,SF,33,PHI,26,3,16.1,1.5,3.4,.449,0.7,1.8,.354,0.9,1.6,.561,.545,0.8,0.9,.875,1.1,2.3,3.4,0.7,1.3,0.6,0.4,1.9,4.5
+109,Torrey Craig,SF,33,CHI,53,14,19.8,2.0,4.7,.430,1.1,2.9,.392,0.9,1.8,.490,.550,0.5,0.7,.750,1.3,2.8,4.1,1.1,0.6,0.4,0.6,2.1,5.7
+110,Jae Crowder,PF,33,MIL,50,25,23.1,2.3,5.4,.422,1.2,3.3,.349,1.1,2.1,.538,.530,0.5,0.7,.722,0.6,2.6,3.2,1.3,0.8,0.2,0.5,1.5,6.2
+111,Jalen Crutcher,PG,24,NOP,1,0,3.0,0.0,1.0,.000,0.0,0.0,,0.0,1.0,.000,.000,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+112,Cade Cunningham,PG,22,DET,62,62,33.5,8.5,18.8,.449,1.9,5.4,.355,6.5,13.4,.487,.500,3.8,4.4,.869,0.5,3.8,4.3,7.5,0.9,0.4,3.4,2.5,22.7
+113,Seth Curry,SG,33,TOT,44,4,14.0,1.8,4.6,.392,0.9,2.5,.352,1.0,2.2,.438,.485,0.6,0.7,.903,0.4,1.1,1.5,1.0,0.5,0.1,0.5,0.9,5.1
+113,Seth Curry,SG,33,DAL,36,3,12.7,1.5,4.0,.372,0.8,2.2,.363,0.7,1.8,.385,.472,0.5,0.5,.895,0.4,1.0,1.4,0.8,0.5,0.1,0.4,0.9,4.3
+113,Seth Curry,SG,33,CHO,8,1,19.9,3.3,7.4,.441,1.1,3.5,.321,2.1,3.9,.548,.517,1.4,1.5,.917,0.4,1.6,2.0,1.8,0.6,0.4,0.6,0.9,9.0
+114,Stephen Curry,PG,35,GSW,74,74,32.7,8.8,19.5,.450,4.8,11.8,.408,4.0,7.7,.515,.573,4.0,4.4,.923,0.5,4.0,4.5,5.1,0.7,0.4,2.8,1.6,26.4
+115,Dyson Daniels,PG,20,NOP,61,16,22.3,2.3,5.1,.447,0.7,2.2,.311,1.6,2.9,.551,.514,0.6,0.9,.642,1.0,2.9,3.9,2.7,1.4,0.4,1.0,1.7,5.8
+116,Anthony Davis,C,30,LAL,76,76,35.5,9.4,16.9,.556,0.4,1.4,.271,9.0,15.5,.582,.567,5.5,6.8,.816,3.1,9.5,12.6,3.5,1.2,2.3,2.1,2.3,24.7
+117,Johnny Davis,SG,21,WAS,50,6,12.3,1.2,3.1,.403,0.3,0.8,.350,1.0,2.3,.421,.448,0.3,0.5,.583,0.4,1.1,1.4,0.6,0.4,0.2,0.3,1.1,3.0
+118,JD Davison,SG,21,BOS,8,0,4.9,0.6,1.5,.417,0.4,0.9,.429,0.3,0.6,.400,.542,0.4,0.5,.750,0.3,1.0,1.3,1.3,0.1,0.1,0.3,0.5,2.0
+119,Dexter Dennis,SG,24,DAL,4,0,7.5,2.5,4.8,.526,0.3,2.0,.125,2.3,2.8,.818,.553,0.3,0.8,.333,0.3,2.0,2.3,1.0,0.0,0.3,0.8,0.3,5.5
+120,DeMar DeRozan,SF,34,CHI,79,79,37.8,8.2,17.2,.480,0.9,2.8,.333,7.3,14.3,.509,.507,6.6,7.7,.853,0.5,3.8,4.3,5.3,1.1,0.6,1.7,2.0,24.0
+121,Moussa Diabaté,C,22,LAC,11,0,5.8,0.9,1.7,.526,0.0,0.0,,0.9,1.7,.526,.526,0.8,1.3,.643,1.4,0.8,2.2,0.4,0.5,0.1,0.2,0.4,2.6
+122,Mamadi Diakite,PF,27,TOT,6,0,4.0,0.7,1.0,.667,0.0,0.2,.000,0.7,0.8,.800,.667,0.7,1.0,.667,0.2,0.5,0.7,0.3,0.2,0.2,0.2,0.3,2.0
+122,Mamadi Diakite,PF,27,SAS,3,0,5.3,1.3,1.7,.800,0.0,0.0,,1.3,1.7,.800,.800,1.3,2.0,.667,0.3,0.7,1.0,0.7,0.0,0.3,0.0,0.3,4.0
+122,Mamadi Diakite,PF,27,NYK,3,0,2.7,0.0,0.3,.000,0.0,0.3,.000,0.0,0.0,,.000,0.0,0.0,,0.0,0.3,0.3,0.0,0.3,0.0,0.3,0.3,0.0
+123,Hamidou Diallo,SG,25,WAS,2,0,2.5,0.5,1.0,.500,0.0,0.0,,0.5,1.0,.500,.500,0.0,0.0,,1.0,0.0,1.0,0.5,1.0,0.0,0.5,0.5,1.0
+124,Gradey Dick,SG,20,TOR,60,17,21.1,3.2,7.4,.425,1.5,4.0,.365,1.7,3.4,.495,.524,0.7,0.9,.863,0.5,1.8,2.2,1.1,0.6,0.0,0.8,1.5,8.5
+125,Ousmane Dieng,PF,20,OKC,33,0,11.1,1.5,3.5,.422,0.6,2.1,.300,0.8,1.4,.609,.513,0.4,0.5,.875,0.3,1.2,1.5,1.1,0.2,0.2,0.6,0.7,4.0
+126,Spencer Dinwiddie,PG,30,TOT,76,52,28.3,3.3,8.4,.392,1.6,4.8,.337,1.7,3.6,.467,.489,2.2,2.8,.805,0.4,2.3,2.7,4.7,0.7,0.3,1.2,1.8,10.5
+126,Spencer Dinwiddie,PG,30,BRK,48,48,30.7,4.1,10.4,.391,1.9,5.8,.320,2.2,4.6,.480,.480,2.6,3.3,.781,0.5,2.8,3.3,6.0,0.8,0.2,1.3,1.8,12.6
+126,Spencer Dinwiddie,PG,30,LAL,28,4,24.2,2.0,5.0,.397,1.3,3.2,.389,0.8,1.8,.412,.521,1.6,1.8,.880,0.1,1.5,1.7,2.4,0.5,0.5,1.0,1.9,6.8
+127,Donte DiVincenzo,SG,27,NYK,81,63,29.1,5.5,12.4,.443,3.5,8.7,.401,2.0,3.7,.542,.584,1.1,1.4,.754,0.8,2.9,3.7,2.7,1.3,0.4,1.4,2.0,15.5
+128,Luka Dončić,PG,24,DAL,70,70,37.5,11.5,23.6,.487,4.1,10.6,.382,7.4,13.0,.573,.573,6.8,8.7,.786,0.8,8.4,9.2,9.8,1.4,0.5,4.0,2.1,33.9
+129,Luguentz Dort,SF,24,OKC,79,79,28.4,3.6,8.3,.438,2.0,5.0,.394,1.7,3.3,.506,.557,1.6,2.0,.826,0.9,2.7,3.6,1.4,0.9,0.6,0.9,2.9,10.9
+130,Ayo Dosunmu,SG,24,CHI,76,37,29.1,4.7,9.5,.501,1.6,3.9,.403,3.2,5.6,.568,.583,1.1,1.4,.810,0.7,2.1,2.8,3.2,0.9,0.5,1.4,2.2,12.2
+131,Jeff Dowtin,PG,26,PHI,12,0,11.8,1.7,3.0,.556,0.8,1.6,.474,0.9,1.4,.647,.681,0.3,0.3,.750,0.3,1.4,1.7,2.3,0.6,0.1,0.4,0.1,4.3
+132,Henri Drell,SF,23,CHI,4,0,7.5,1.0,2.5,.400,0.5,1.5,.333,0.5,1.0,.500,.500,0.3,0.5,.500,0.3,0.8,1.0,1.0,0.5,0.3,0.5,1.5,2.8
+133,Andre Drummond,C,30,CHI,79,10,17.1,3.4,6.1,.556,0.0,0.0,.000,3.4,6.0,.560,.556,1.6,2.8,.592,3.4,5.6,9.0,0.5,0.9,0.6,1.0,1.8,8.4
+134,Chris Duarte,SF,26,SAC,59,11,12.2,1.4,3.6,.381,0.7,2.2,.346,0.6,1.4,.434,.486,0.4,0.6,.788,0.4,1.4,1.8,0.7,0.5,0.1,0.4,1.4,3.9
+135,David Duke Jr.,SG,24,SAS,4,0,12.8,2.5,4.5,.556,0.8,1.5,.500,1.8,3.0,.583,.639,0.8,0.8,1.000,0.5,2.0,2.5,1.3,0.5,0.0,0.8,1.0,6.5
+136,Kris Dunn,PG,29,UTA,66,32,18.9,2.2,4.7,.470,0.6,1.7,.369,1.6,3.1,.525,.535,0.3,0.5,.688,0.8,2.1,2.9,3.8,1.0,0.4,1.2,2.0,5.4
+137,Kevin Durant,PF,35,PHO,75,75,37.2,10.0,19.1,.523,2.2,5.4,.413,7.8,13.7,.567,.581,4.8,5.6,.856,0.5,6.1,6.6,5.0,0.9,1.2,3.3,1.8,27.1
+138,Jalen Duren,C,20,DET,61,60,29.1,5.7,9.2,.619,0.0,0.1,.000,5.7,9.2,.623,.619,2.3,3.0,.790,3.1,8.5,11.6,2.4,0.5,0.8,2.1,3.1,13.8
+139,Tari Eason,PF,22,HOU,22,0,21.8,4.0,8.6,.466,0.8,2.3,.360,3.2,6.3,.504,.513,1.0,1.5,.636,2.1,4.9,7.0,1.2,1.4,0.9,0.9,2.3,9.8
+140,Anthony Edwards,SG,22,MIN,79,78,35.1,9.1,19.7,.461,2.4,6.7,.357,6.7,13.0,.515,.522,5.4,6.4,.836,0.7,4.8,5.4,5.1,1.3,0.5,3.1,1.8,25.9
+141,Kessler Edwards,SF,23,SAC,54,0,5.1,0.6,1.5,.415,0.4,1.0,.385,0.3,0.6,.467,.537,0.1,0.2,.556,0.2,0.6,0.8,0.3,0.2,0.1,0.1,0.5,1.7
+142,Keon Ellis,SG,24,SAC,57,21,17.2,1.9,4.1,.461,1.2,2.9,.417,0.6,1.1,.578,.612,0.5,0.6,.743,0.6,1.6,2.2,1.5,0.9,0.5,0.6,2.0,5.4
+143,Joel Embiid,C,29,PHI,39,39,33.6,11.5,21.8,.529,1.4,3.6,.388,10.2,18.3,.556,.561,10.2,11.6,.883,2.4,8.6,11.0,5.6,1.2,1.7,3.8,2.9,34.7
+144,Drew Eubanks,C,26,PHO,75,6,15.6,2.0,3.4,.601,0.0,0.0,1.000,2.0,3.3,.596,.607,1.0,1.2,.774,1.4,2.9,4.3,0.8,0.4,0.8,0.8,1.5,5.1
+145,Tosan Evbuomwan,SF,22,TOT,17,8,21.6,2.1,4.2,.507,0.7,1.9,.375,1.4,2.3,.615,.592,1.0,1.5,.680,1.1,2.5,3.5,0.9,0.4,0.3,0.5,1.4,5.9
+145,Tosan Evbuomwan,SF,22,MEM,4,0,18.5,1.0,3.8,.267,0.5,2.0,.250,0.5,1.8,.286,.333,0.0,0.0,,2.0,1.5,3.5,1.5,0.0,0.3,0.3,1.3,2.5
+145,Tosan Evbuomwan,SF,22,DET,13,8,22.5,2.5,4.3,.571,0.8,1.8,.417,1.7,2.5,.688,.661,1.3,1.9,.680,0.8,2.8,3.5,0.8,0.5,0.3,0.6,1.4,7.0
+146,Dante Exum,PG,28,DAL,55,17,19.8,2.9,5.5,.533,1.0,2.0,.491,2.0,3.5,.557,.621,1.0,1.2,.779,0.5,2.3,2.7,2.9,0.4,0.1,0.9,1.5,7.8
+147,Bruno Fernando,C,25,ATL,45,2,15.2,2.4,4.2,.583,0.0,0.1,.000,2.4,4.1,.596,.583,1.4,2.1,.667,1.3,3.0,4.3,1.0,0.6,0.6,1.0,2.4,6.3
+148,Dorian Finney-Smith,PF,30,BRK,68,56,28.4,3.2,7.5,.421,1.7,4.9,.348,1.4,2.6,.560,.535,0.5,0.7,.717,1.6,3.1,4.7,1.6,0.8,0.6,0.9,2.2,8.5
+149,Adam Flagler,SG,24,OKC,2,0,7.0,0.5,3.5,.143,0.5,3.0,.167,0.0,0.5,.000,.214,0.0,0.0,,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,1.5
+150,Malachi Flynn,PG,25,TOT,69,0,12.7,2.0,4.7,.418,0.8,2.4,.331,1.2,2.3,.513,.505,0.8,1.0,.732,0.3,1.3,1.7,1.9,0.6,0.1,0.9,1.0,5.5
+150,Malachi Flynn,PG,25,TOR,31,0,15.3,1.8,4.4,.409,0.9,2.6,.350,0.9,1.8,.491,.511,0.5,0.7,.773,0.4,1.7,2.1,2.4,0.6,0.2,1.1,1.4,5.1
+150,Malachi Flynn,PG,25,NYK,14,0,4.3,0.6,1.6,.391,0.3,0.9,.308,0.4,0.7,.500,.478,0.6,0.8,.818,0.0,0.4,0.4,0.4,0.1,0.0,0.2,0.2,2.2
+150,Malachi Flynn,PG,25,DET,24,0,14.3,3.0,6.9,.430,1.0,3.2,.316,2.0,3.7,.528,.503,1.1,1.6,.684,0.3,1.5,1.8,2.1,0.8,0.1,1.1,0.9,8.0
+151,Simone Fontecchio,SF,28,TOT,66,43,24.9,3.8,8.3,.460,2.0,5.1,.401,1.8,3.2,.551,.582,0.8,1.0,.818,1.0,2.8,3.7,1.5,0.7,0.3,1.0,1.7,10.5
+151,Simone Fontecchio,SF,28,UTA,50,34,23.2,3.2,7.2,.450,1.8,4.7,.391,1.4,2.5,.560,.577,0.6,0.8,.800,1.0,2.5,3.5,1.5,0.6,0.4,1.0,1.6,8.9
+151,Simone Fontecchio,SF,28,DET,16,9,30.3,5.7,11.9,.479,2.7,6.3,.426,3.0,5.6,.539,.592,1.4,1.6,.846,0.8,3.6,4.4,1.8,0.9,0.3,1.1,1.8,15.4
+152,Jordan Ford,PG,25,SAC,6,0,3.7,0.7,1.2,.571,0.3,0.5,.667,0.3,0.7,.500,.714,0.3,0.3,1.000,0.0,0.3,0.3,0.3,0.0,0.0,0.2,0.0,2.0
+153,Trent Forrest,SG,25,ATL,38,0,10.9,0.9,2.4,.378,0.1,0.3,.200,0.8,2.1,.400,.389,0.3,0.4,.765,0.3,1.1,1.3,2.4,0.3,0.1,0.5,0.7,2.2
+154,Evan Fournier,SG,31,TOT,32,0,18.1,2.5,6.9,.357,1.0,4.1,.254,1.4,2.8,.505,.432,0.9,1.1,.806,0.2,1.7,1.8,1.5,0.9,0.2,0.7,1.5,6.9
+154,Evan Fournier,SG,31,NYK,3,0,13.0,1.3,6.7,.200,0.7,5.0,.133,0.7,1.7,.400,.250,0.7,0.7,1.000,0.0,1.3,1.3,1.0,1.3,0.0,0.3,1.3,4.0
+154,Evan Fournier,SG,31,DET,29,0,18.7,2.6,6.9,.373,1.1,4.0,.270,1.5,3.0,.512,.450,0.9,1.2,.794,0.2,1.7,1.9,1.6,0.9,0.2,0.7,1.5,7.2
+155,De'Aaron Fox,PG,26,SAC,74,74,35.9,9.7,20.9,.465,2.9,7.8,.369,6.8,13.1,.522,.534,4.2,5.7,.738,0.9,3.7,4.6,5.6,2.0,0.4,2.6,2.6,26.6
+156,Javon Freeman-Liberty,PG,24,TOR,22,6,18.3,2.5,5.6,.444,0.5,1.9,.238,2.0,3.7,.549,.484,1.5,1.6,.917,0.7,2.5,3.2,1.8,0.5,0.2,1.1,1.3,7.0
+157,Alex Fudge,SF,20,TOT,6,0,6.7,1.0,2.5,.400,0.2,0.8,.200,0.8,1.7,.500,.433,0.3,0.3,1.000,0.7,0.2,0.8,0.0,0.5,0.0,0.3,0.3,2.5
+157,Alex Fudge,SF,20,LAL,4,0,3.5,0.3,1.5,.167,0.0,0.5,.000,0.3,1.0,.250,.167,0.5,0.5,1.000,0.5,0.0,0.5,0.0,0.0,0.0,0.3,0.3,1.0
+157,Alex Fudge,SF,20,DAL,2,0,13.0,2.5,4.5,.556,0.5,1.5,.333,2.0,3.0,.667,.611,0.0,0.0,,1.0,0.5,1.5,0.0,1.5,0.0,0.5,0.5,5.5
+158,Markelle Fultz,PG,25,ORL,43,18,21.2,3.6,7.6,.472,0.1,0.4,.222,3.5,7.2,.487,.479,0.5,0.8,.697,1.3,1.9,3.2,2.8,1.0,0.3,1.2,1.5,7.8
+159,Andrew Funk,SG,24,CHI,5,0,2.6,0.0,0.8,.000,0.0,0.6,.000,0.0,0.2,.000,.000,0.0,0.0,,0.0,0.0,0.0,0.0,0.2,0.2,0.0,0.0,0.0
+160,Wenyen Gabriel,PF,26,MEM,5,0,16.2,1.6,4.4,.364,0.2,1.2,.167,1.4,3.2,.438,.386,0.0,1.0,.000,1.4,3.6,5.0,0.6,0.4,0.4,1.6,2.0,3.4
+161,Daniel Gafford,C-PF,25,TOT,74,66,24.5,4.7,6.5,.725,0.0,0.0,,4.7,6.5,.725,.725,1.6,2.4,.674,2.6,5.0,7.6,1.6,0.9,2.1,1.0,3.1,11.0
+161,Daniel Gafford,C,25,WAS,45,45,26.5,4.5,6.5,.690,0.0,0.0,,4.5,6.5,.690,.690,1.9,2.6,.706,2.9,5.1,8.0,1.5,1.0,2.2,1.0,3.3,10.9
+161,Daniel Gafford,PF,25,DAL,29,21,21.5,5.0,6.4,.780,0.0,0.0,,5.0,6.4,.780,.780,1.2,1.9,.607,2.2,4.7,6.9,1.6,0.7,1.9,1.0,2.8,11.2
+162,Danilo Gallinari,PF-SF,35,TOT,49,0,12.9,1.7,3.9,.437,0.6,1.9,.323,1.1,2.0,.546,.516,1.7,2.0,.857,0.4,1.8,2.2,1.1,0.3,0.1,0.5,1.0,5.7
+162,Danilo Gallinari,PF,35,WAS,26,0,14.8,2.2,5.0,.435,0.8,2.5,.313,1.4,2.6,.552,.511,1.8,2.2,.839,0.5,2.5,2.9,1.2,0.2,0.1,0.5,1.0,7.0
+162,Danilo Gallinari,SF,35,DET,6,0,15.0,2.0,3.7,.545,1.2,2.0,.583,0.8,1.7,.500,.705,3.5,4.0,.875,0.3,2.0,2.3,2.0,0.3,0.3,0.2,1.5,8.7
+162,Danilo Gallinari,SF,35,MIL,17,0,9.1,0.8,2.2,.378,0.2,1.0,.176,0.6,1.2,.550,.419,0.9,1.1,.889,0.4,0.7,1.1,0.7,0.4,0.1,0.6,0.9,2.8
+163,Darius Garland,PG,24,CLE,57,57,33.4,6.6,14.8,.446,2.3,6.2,.371,4.3,8.5,.501,.524,2.6,3.1,.834,0.5,2.1,2.7,6.5,1.3,0.1,3.1,1.7,18.0
+164,Usman Garuba,PF,21,GSW,6,0,3.0,0.2,1.0,.167,0.0,0.2,.000,0.2,0.8,.200,.167,0.2,0.3,.500,0.5,0.7,1.2,0.2,0.2,0.5,0.3,0.3,0.5
+165,Luka Garza,C,25,MIN,25,0,4.9,1.4,3.0,.480,0.4,1.3,.281,1.1,1.7,.628,.540,0.7,1.0,.720,0.8,0.5,1.2,0.2,0.2,0.0,0.2,1.0,4.0
+166,Kaiser Gates,SF,27,NOP,1,0,7.0,0.0,4.0,.000,0.0,2.0,.000,0.0,2.0,.000,.000,0.0,0.0,,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+167,Keyonte George,PG,20,UTA,75,44,27.0,4.3,10.9,.391,2.0,5.9,.334,2.3,5.0,.459,.482,2.5,3.0,.848,0.2,2.6,2.8,4.4,0.5,0.1,2.5,1.2,13.0
+168,Paul George,PF,33,LAC,74,74,33.8,7.9,16.7,.471,3.3,7.9,.413,4.6,8.8,.523,.569,3.6,3.9,.907,0.5,4.7,5.2,3.5,1.5,0.5,2.1,2.7,22.6
+169,Taj Gibson,PF,38,TOT,20,1,10.2,0.8,1.9,.405,0.1,0.5,.200,0.7,1.4,.481,.432,0.1,0.1,1.000,0.6,1.3,1.9,0.6,0.2,0.4,0.3,1.6,1.7
+169,Taj Gibson,PF,38,NYK,16,1,10.3,0.4,1.4,.304,0.0,0.4,.000,0.4,1.1,.412,.304,0.1,0.1,1.000,0.6,1.1,1.8,0.6,0.1,0.4,0.3,1.8,1.0
+169,Taj Gibson,PF,38,DET,4,0,9.8,2.0,3.5,.571,0.5,1.0,.500,1.5,2.5,.600,.643,0.0,0.0,,0.5,1.8,2.3,0.5,0.3,0.3,0.0,0.8,4.5
+170,Josh Giddey,SG,21,OKC,80,80,25.1,5.0,10.6,.475,1.0,3.0,.337,4.0,7.6,.530,.523,1.3,1.6,.806,1.4,5.0,6.4,4.8,0.6,0.6,2.1,1.4,12.3
+171,Harry Giles,C,25,TOT,23,0,4.3,1.0,2.2,.460,0.1,0.6,.214,0.9,1.6,.556,.490,0.3,0.6,.538,0.5,0.8,1.3,0.3,0.1,0.1,0.3,0.7,2.4
+171,Harry Giles,C,25,BRK,16,0,5.1,1.4,2.8,.500,0.2,0.7,.273,1.2,2.1,.576,.534,0.4,0.8,.538,0.8,0.9,1.6,0.4,0.1,0.2,0.4,0.9,3.4
+171,Harry Giles,C,25,LAL,7,0,2.7,0.1,0.9,.167,0.0,0.4,.000,0.1,0.4,.333,.167,0.0,0.0,,0.0,0.6,0.6,0.0,0.1,0.0,0.0,0.0,0.3
+172,Shai Gilgeous-Alexander,PG,25,OKC,75,75,34.0,10.6,19.8,.535,1.3,3.6,.353,9.3,16.2,.576,.567,7.6,8.7,.874,0.9,4.7,5.5,6.2,2.0,0.9,2.2,2.5,30.1
+173,Anthony Gill,PF,31,WAS,50,3,9.3,1.5,3.2,.469,0.2,0.8,.244,1.3,2.4,.546,.500,0.6,0.7,.806,0.7,1.1,1.9,0.7,0.3,0.2,0.5,1.0,3.8
+174,Collin Gillespie,PG,24,DEN,24,0,9.4,1.3,2.9,.464,0.6,1.6,.395,0.7,1.3,.548,.572,0.3,0.5,.667,0.0,0.8,0.9,1.1,0.5,0.0,0.6,1.0,3.6
+175,Jacob Gilyard,PG,25,TOT,41,14,17.0,1.5,3.7,.400,1.2,3.1,.405,0.2,0.6,.375,.570,0.0,0.0,1.000,0.2,0.9,1.1,3.3,0.8,0.1,0.7,1.0,4.2
+175,Jacob Gilyard,PG,25,MEM,37,14,17.7,1.6,3.9,.417,1.4,3.2,.425,0.2,0.6,.375,.594,0.1,0.1,1.000,0.2,0.9,1.2,3.5,0.7,0.1,0.8,0.9,4.7
+175,Jacob Gilyard,PG,25,BRK,4,0,11.3,0.0,1.5,.000,0.0,1.5,.000,0.0,0.0,,.000,0.0,0.0,,0.3,0.5,0.8,1.5,1.5,0.0,0.3,1.0,0.0
+176,Rudy Gobert,C,31,MIN,76,76,34.1,5.3,8.1,.661,0.0,0.0,.000,5.3,8.0,.664,.661,3.3,5.1,.638,3.8,9.2,12.9,1.3,0.7,2.1,1.6,3.1,14.0
+177,Jordan Goodwin,PG,25,TOT,57,12,18.5,2.5,6.7,.369,0.7,2.5,.298,1.7,4.2,.411,.424,0.8,1.0,.746,1.5,2.9,4.4,2.7,0.8,0.3,1.0,1.3,6.5
+177,Jordan Goodwin,PG,25,PHO,40,0,14.0,1.9,4.8,.389,0.6,2.0,.288,1.3,2.8,.460,.448,0.6,0.7,.862,1.0,1.9,2.9,2.0,0.6,0.2,0.7,1.1,5.0
+177,Jordan Goodwin,PG,25,MEM,17,12,29.2,3.9,11.1,.349,1.1,3.6,.311,2.8,7.5,.367,.399,1.1,1.8,.633,2.8,5.2,8.0,4.5,1.5,0.5,1.6,1.9,10.0
+178,Aaron Gordon,PF,28,DEN,73,73,31.5,5.5,9.8,.556,0.5,1.9,.290,4.9,7.9,.619,.584,2.4,3.7,.658,2.4,4.1,6.5,3.5,0.8,0.6,1.4,1.9,13.9
+179,Eric Gordon,SG,35,PHO,68,24,27.8,4.0,9.1,.443,2.2,5.8,.378,1.8,3.3,.561,.565,0.8,0.9,.797,0.2,1.6,1.8,2.0,1.0,0.4,1.1,1.2,11.0
+180,Devonte' Graham,PG,28,SAS,23,0,13.6,1.7,4.7,.352,1.1,3.6,.301,0.6,1.1,.520,.468,0.6,0.7,.813,0.2,1.3,1.6,2.1,0.4,0.1,0.7,0.6,5.0
+181,Jerami Grant,PF,29,POR,54,54,33.9,7.1,15.7,.451,2.1,5.1,.402,5.0,10.6,.474,.516,4.7,5.8,.817,0.7,2.9,3.5,2.8,0.8,0.6,2.1,2.2,21.0
+182,RaiQuan Gray,PF,24,SAS,3,0,13.0,3.3,5.7,.588,1.0,2.3,.429,2.3,3.3,.700,.676,0.0,0.0,,0.7,1.7,2.3,2.0,0.3,0.3,0.3,0.0,7.7
+183,A.J. Green,SG,24,MIL,56,0,11.0,1.5,3.5,.423,1.2,3.0,.408,0.3,0.5,.519,.599,0.3,0.3,.895,0.2,1.0,1.1,0.5,0.2,0.1,0.2,0.9,4.5
+184,Danny Green,SG,36,PHI,2,0,9.0,0.0,1.0,.000,0.0,0.5,.000,0.0,0.5,.000,.000,0.0,0.0,,0.0,1.0,1.0,0.5,0.5,0.0,0.0,0.5,0.0
+185,Draymond Green,PF,33,GSW,55,52,27.1,3.4,6.8,.497,0.9,2.3,.395,2.4,4.4,.551,.566,1.0,1.3,.730,1.4,5.9,7.2,6.0,1.0,0.9,2.5,3.0,8.6
+186,Jalen Green,SG,21,HOU,82,82,31.7,6.9,16.2,.423,2.5,7.4,.332,4.4,8.9,.498,.498,3.5,4.3,.804,0.5,4.7,5.2,3.5,0.8,0.3,2.3,1.4,19.6
+187,Javonte Green,SF,30,CHI,9,5,25.6,5.0,8.3,.600,1.1,3.0,.370,3.9,5.3,.729,.667,1.1,1.4,.769,2.2,5.2,7.4,0.6,1.1,0.9,0.8,2.1,12.2
+188,Jeff Green,PF,37,HOU,78,6,16.8,2.1,4.7,.456,0.7,2.1,.331,1.5,2.6,.553,.529,1.6,1.9,.819,0.6,1.7,2.3,0.9,0.2,0.4,0.6,1.3,6.5
+189,Josh Green,SG,23,DAL,57,33,26.4,3.1,6.6,.479,1.3,3.3,.385,1.9,3.3,.572,.575,0.7,1.0,.684,0.7,2.5,3.2,2.3,0.8,0.2,1.1,1.9,8.2
+190,AJ Griffin,SF,20,ATL,20,0,8.6,0.9,3.1,.290,0.5,2.0,.256,0.4,1.2,.348,.371,0.1,0.1,1.000,0.1,0.8,0.9,0.3,0.1,0.1,0.4,0.3,2.4
+191,Quentin Grimes,SG,23,TOT,51,18,20.1,2.4,6.4,.372,1.6,4.7,.338,0.8,1.7,.466,.495,0.7,0.9,.756,0.3,1.7,2.0,1.3,0.7,0.1,0.5,1.5,7.0
+191,Quentin Grimes,SG,23,NYK,45,18,20.2,2.5,6.4,.395,1.7,4.7,.363,0.8,1.6,.486,.530,0.5,0.8,.706,0.3,1.6,2.0,1.2,0.7,0.1,0.5,1.5,7.3
+191,Quentin Grimes,SG,23,DET,6,0,19.2,1.5,7.0,.214,0.7,4.7,.143,0.8,2.3,.357,.262,1.7,1.8,.909,0.2,1.8,2.0,2.3,0.8,0.7,1.2,1.5,5.3
+192,Mouhamadou Gueye,PF,25,TOR,11,0,10.9,1.0,3.5,.289,0.0,0.3,.000,1.0,3.2,.314,.289,0.4,0.8,.444,1.1,1.0,2.1,0.5,0.3,1.6,1.1,1.4,2.4
+193,Mouhamed Gueye,PF,21,ATL,6,0,12.2,1.3,3.8,.348,0.5,1.5,.333,0.8,2.3,.357,.413,0.8,1.0,.833,1.7,2.0,3.7,0.7,0.8,0.7,0.2,2.2,4.0
+194,Rui Hachimura,PF,25,LAL,68,39,26.9,5.3,9.9,.537,1.4,3.4,.422,3.9,6.5,.597,.609,1.5,2.0,.739,0.9,3.5,4.3,1.2,0.6,0.4,0.7,1.5,13.6
+195,Ashton Hagans,PG,24,POR,19,1,16.6,1.5,3.7,.408,0.4,1.3,.320,1.1,2.4,.457,.465,0.7,0.9,.824,0.7,1.6,2.4,2.8,0.6,0.5,1.6,2.1,4.2
+196,Tyrese Haliburton,PG,23,IND,69,68,32.2,7.2,15.2,.477,2.8,7.8,.364,4.4,7.4,.595,.570,2.8,3.3,.855,0.5,3.4,3.9,10.9,1.2,0.7,2.3,1.1,20.1
+197,R.J. Hampton,PG,22,MIA,8,2,9.5,0.5,1.8,.286,0.1,1.0,.125,0.4,0.8,.500,.321,0.1,0.3,.500,0.3,0.5,0.8,1.0,0.0,0.0,0.4,1.0,1.3
+198,Tim Hardaway Jr.,SF,31,DAL,79,12,26.8,5.0,12.4,.402,2.7,7.6,.353,2.3,4.8,.478,.510,1.8,2.1,.852,0.3,2.9,3.2,1.8,0.5,0.1,0.9,1.3,14.4
+199,James Harden,PG,34,LAC,72,72,34.3,4.9,11.4,.428,2.6,6.8,.381,2.3,4.6,.497,.541,4.2,4.8,.878,0.5,4.7,5.1,8.5,1.1,0.8,2.6,1.8,16.6
+200,Jaden Hardy,SG,21,DAL,73,7,13.5,2.7,6.6,.407,1.1,3.1,.362,1.6,3.5,.447,.492,0.8,1.0,.776,0.3,1.5,1.8,1.5,0.3,0.1,0.8,1.1,7.3
+201,Ron Harper Jr.,PF,23,TOR,1,0,4.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,,0.0,0.0,,0.0,0.0,0.0,1.0,0.0,0.0,0.0,2.0,0.0
+202,Gary Harris,SG,29,ORL,54,27,24.0,2.4,5.5,.441,1.4,3.8,.371,1.0,1.7,.598,.569,0.6,0.8,.756,0.4,1.2,1.7,1.6,0.9,0.3,0.6,1.7,6.9
+203,Joe Harris,SG,32,DET,16,0,10.6,0.9,2.4,.359,0.6,1.9,.333,0.3,0.6,.444,.487,0.1,0.1,.500,0.2,0.6,0.8,0.6,0.2,0.1,0.3,1.2,2.4
+204,Kevon Harris,SG,26,ORL,2,0,3.0,1.0,1.5,.667,0.0,0.0,,1.0,1.5,.667,.667,0.0,0.0,,0.0,1.0,1.0,0.5,0.0,0.0,0.5,1.5,2.0
+205,Tobias Harris,PF,31,PHI,70,70,33.8,6.6,13.6,.487,1.3,3.7,.353,5.3,9.9,.537,.535,2.7,3.0,.878,1.1,5.3,6.5,3.1,1.0,0.7,1.3,1.6,17.2
+206,Shaquille Harrison,SG,30,MEM,3,0,2.0,0.3,0.7,.500,0.0,0.0,,0.3,0.7,.500,.500,0.0,0.0,,0.0,0.7,0.7,0.0,0.0,0.3,0.0,0.0,0.7
+207,Josh Hart,SF,28,NYK,81,42,33.4,3.6,8.4,.434,1.0,3.2,.310,2.7,5.2,.511,.493,1.1,1.4,.791,1.6,6.8,8.3,4.1,0.9,0.3,1.5,2.1,9.4
+208,Isaiah Hartenstein,C,25,NYK,75,49,25.3,3.1,4.9,.644,0.0,0.0,.333,3.1,4.8,.646,.645,1.5,2.1,.707,3.3,5.1,8.3,2.5,1.2,1.1,1.2,2.8,7.8
+209,Sam Hauser,SF,26,BOS,79,13,22.0,3.2,7.1,.446,2.5,5.9,.424,0.7,1.2,.559,.623,0.2,0.2,.895,0.6,2.9,3.5,1.0,0.5,0.3,0.4,1.3,9.0
+210,Jordan Hawkins,SG,21,NOP,67,10,17.3,2.6,6.8,.382,1.6,4.4,.366,1.0,2.4,.411,.501,1.0,1.2,.838,0.4,1.8,2.2,1.0,0.3,0.1,0.6,1.1,7.8
+211,Jaxson Hayes,C,23,LAL,70,5,12.5,1.8,2.5,.720,0.0,0.1,.000,1.8,2.4,.754,.720,0.7,1.2,.622,1.0,2.0,3.0,0.5,0.5,0.4,0.6,1.8,4.3
+212,Killian Hayes,PG,22,DET,42,31,24.0,2.8,6.7,.413,0.5,1.8,.297,2.3,5.0,.455,.452,0.8,1.2,.660,0.3,2.6,2.8,4.9,0.9,0.5,1.2,1.8,6.9
+213,Gordon Hayward,SF,33,TOT,51,28,24.4,3.7,7.9,.464,0.7,1.8,.411,2.9,6.1,.479,.510,1.7,2.4,.742,0.7,2.8,3.5,3.1,0.8,0.3,1.3,1.3,9.8
+213,Gordon Hayward,SF,33,CHO,25,25,31.9,5.6,11.9,.468,0.9,2.4,.361,4.7,9.4,.496,.505,2.5,3.2,.765,1.0,3.7,4.7,4.6,1.1,0.5,2.0,1.8,14.5
+213,Gordon Hayward,SF,33,OKC,26,3,17.2,1.8,4.1,.453,0.6,1.1,.517,1.3,3.0,.429,.524,1.0,1.5,.692,0.5,2.0,2.5,1.6,0.5,0.0,0.6,0.8,5.3
+214,Scoot Henderson,PG,19,POR,62,32,28.5,5.0,12.9,.385,1.4,4.3,.325,3.5,8.5,.416,.440,2.7,3.3,.819,0.9,2.3,3.1,5.4,0.8,0.2,3.4,3.1,14.0
+215,Taylor Hendricks,PF,20,UTA,40,23,21.4,2.7,6.0,.450,1.3,3.5,.379,1.4,2.5,.550,.560,0.6,0.7,.793,1.2,3.5,4.6,0.8,0.7,0.8,0.7,1.9,7.3
+216,Tyler Herro,SG,24,MIA,42,40,33.5,7.7,17.5,.441,3.1,7.9,.396,4.6,9.6,.478,.531,2.3,2.6,.856,0.5,4.8,5.3,4.5,0.7,0.1,2.2,1.5,20.8
+217,Buddy Hield,SF-SG,31,TOT,84,42,25.7,4.4,10.1,.436,2.6,6.8,.386,1.8,3.4,.539,.565,0.6,0.7,.881,0.7,2.5,3.2,2.8,0.8,0.5,1.2,2.0,12.1
+217,Buddy Hield,SF,31,IND,52,28,25.7,4.4,9.9,.443,2.6,6.9,.384,1.8,3.1,.575,.575,0.5,0.6,.848,0.7,2.6,3.2,2.7,0.8,0.6,1.2,2.0,12.0
+217,Buddy Hield,SG,31,PHI,32,14,25.8,4.4,10.4,.426,2.6,6.6,.389,1.9,3.8,.492,.550,0.8,0.8,.923,0.8,2.3,3.2,3.0,0.8,0.3,1.3,2.1,12.2
+218,Haywood Highsmith,PF,27,MIA,66,26,20.7,2.3,5.0,.465,1.2,2.9,.396,1.2,2.1,.562,.581,0.3,0.5,.639,1.0,2.2,3.2,1.1,0.8,0.5,0.5,1.8,6.1
+219,Nate Hinton,SG,24,HOU,15,0,5.0,0.7,1.7,.423,0.5,0.9,.500,0.3,0.8,.333,.558,0.3,0.3,.800,0.5,1.0,1.5,0.7,0.2,0.1,0.2,0.7,2.2
+220,D'Moi Hodge,SG,25,LAL,7,0,5.9,0.7,2.1,.333,0.4,1.7,.250,0.3,0.4,.667,.433,0.1,0.3,.500,0.0,0.0,0.0,0.7,0.1,0.1,0.1,0.6,2.0
+221,Aaron Holiday,PG,27,HOU,78,1,16.3,2.4,5.3,.446,1.1,2.8,.387,1.3,2.6,.510,.547,0.7,0.8,.921,0.3,1.3,1.6,1.8,0.5,0.1,0.7,1.6,6.6
+222,Jrue Holiday,PG,33,BOS,69,69,32.8,4.8,10.0,.480,2.0,4.7,.429,2.8,5.3,.526,.581,0.9,1.0,.833,1.2,4.2,5.4,4.8,0.9,0.8,1.8,1.6,12.5
+223,Justin Holiday,SG,34,DEN,58,9,14.9,1.5,3.3,.454,0.9,2.3,.404,0.6,1.0,.569,.595,0.1,0.1,.750,0.2,1.1,1.2,1.2,0.6,0.2,0.2,1.2,4.0
+224,Richaun Holmes,C-PF,30,TOT,40,10,13.9,2.1,3.7,.558,0.0,0.1,.333,2.0,3.6,.563,.561,0.9,1.2,.723,1.8,2.8,4.6,0.6,0.3,0.5,0.6,1.9,5.0
+224,Richaun Holmes,C,30,DAL,23,2,10.3,1.4,2.6,.559,0.0,0.0,,1.4,2.6,.559,.559,0.5,0.9,.571,1.4,2.0,3.4,0.7,0.1,0.4,0.5,1.4,3.4
+224,Richaun Holmes,PF,30,WAS,17,8,18.7,2.9,5.2,.557,0.1,0.2,.333,2.8,5.0,.565,.563,1.3,1.5,.846,2.2,3.9,6.1,0.6,0.5,0.6,0.7,2.4,7.1
+225,Chet Holmgren,C,21,OKC,82,82,29.4,6.2,11.6,.530,1.6,4.3,.370,4.6,7.4,.623,.598,2.7,3.4,.793,1.6,6.3,7.9,2.4,0.6,2.3,1.6,2.4,16.5
+226,Jalen Hood-Schifino,SG,20,LAL,21,0,5.2,0.5,2.1,.222,0.1,0.7,.133,0.4,1.4,.267,.244,0.6,1.0,.600,0.1,0.5,0.6,0.4,0.1,0.1,0.4,0.6,1.6
+227,Al Horford,C,37,BOS,65,33,26.8,3.3,6.4,.511,1.7,4.0,.419,1.6,2.5,.658,.640,0.4,0.5,.867,1.3,5.1,6.4,2.6,0.6,1.0,0.7,1.4,8.6
+228,Talen Horton-Tucker,PG,23,UTA,51,11,19.8,3.6,9.1,.396,1.2,3.6,.330,2.4,5.5,.438,.460,1.7,2.1,.807,0.4,2.0,2.4,3.5,0.9,0.4,1.3,1.8,10.1
+229,Danuel House Jr.,SF,30,PHI,34,4,15.0,1.4,3.1,.448,0.4,1.5,.300,0.9,1.6,.582,.519,1.0,1.4,.761,0.5,1.2,1.7,0.8,0.4,0.1,0.6,1.3,4.2
+230,Caleb Houstan,SF,21,ORL,59,13,13.8,1.4,3.5,.388,1.2,3.3,.373,0.1,0.2,.615,.563,0.4,0.4,.808,0.5,0.9,1.4,0.5,0.3,0.1,0.3,1.0,4.3
+231,Jett Howard,SF,20,ORL,18,0,3.7,0.6,1.7,.333,0.4,1.4,.280,0.2,0.3,.600,.450,0.1,0.2,.500,0.1,0.3,0.4,0.3,0.1,0.1,0.2,0.2,1.6
+232,Kevin Huerter,SG,25,SAC,64,59,24.4,3.9,8.8,.443,1.9,5.3,.361,2.0,3.4,.571,.553,0.6,0.7,.766,0.6,2.9,3.5,2.6,0.7,0.4,0.8,2.1,10.2
+233,Jay Huff,C,25,DEN,20,0,2.5,0.5,0.8,.600,0.2,0.5,.333,0.3,0.3,1.000,.700,0.1,0.1,1.000,0.1,0.5,0.6,0.1,0.1,0.2,0.1,0.5,1.2
+234,De'Andre Hunter,SF,26,ATL,57,37,29.5,5.3,11.6,.459,2.1,5.3,.385,3.3,6.3,.521,.547,2.9,3.4,.847,0.5,3.4,3.9,1.5,0.7,0.3,1.5,2.6,15.6
+235,Matthew Hurt,PF,23,MEM,8,0,14.1,1.5,4.3,.353,0.6,2.5,.250,0.9,1.8,.500,.426,0.4,0.4,1.000,1.1,0.9,2.0,0.5,0.4,0.4,0.3,1.0,4.0
+236,Bones Hyland,PG,23,LAC,37,5,14.6,2.6,6.8,.386,1.2,3.6,.326,1.4,3.1,.457,.474,0.5,0.6,.783,0.1,1.4,1.5,2.5,0.7,0.1,1.2,1.4,6.9
+237,Joe Ingles,SF,36,ORL,68,0,17.2,1.5,3.3,.436,1.0,2.4,.435,0.4,1.0,.439,.590,0.4,0.5,.824,0.3,1.8,2.1,3.0,0.6,0.1,1.0,1.3,4.4
+238,Brandon Ingram,SF,26,NOP,64,64,32.9,7.8,15.9,.492,1.3,3.8,.355,6.5,12.1,.534,.534,3.8,4.8,.801,0.7,4.4,5.1,5.7,0.8,0.6,2.5,2.3,20.8
+239,Kyrie Irving,SG,31,DAL,58,58,35.0,9.7,19.5,.497,3.0,7.3,.411,6.7,12.2,.548,.573,3.3,3.6,.905,0.8,4.2,5.0,5.2,1.3,0.5,1.8,1.9,25.6
+240,Jonathan Isaac,PF,26,ORL,58,2,15.8,2.5,4.9,.510,0.7,1.9,.375,1.8,3.0,.598,.584,1.0,1.4,.720,1.3,3.2,4.5,0.5,0.7,1.2,0.5,1.1,6.8
+241,Jaden Ivey,SG,21,DET,77,61,28.8,5.4,12.6,.429,1.6,4.8,.336,3.8,7.8,.485,.492,2.9,3.9,.749,1.1,2.3,3.4,3.8,0.7,0.5,2.5,2.6,15.4
+242,Andre Jackson Jr.,SG,22,MIL,57,8,10.0,0.9,1.8,.500,0.3,0.8,.370,0.6,0.9,.611,.585,0.1,0.2,.727,0.8,1.2,2.0,0.9,0.3,0.1,0.5,1.6,2.2
+243,GG Jackson II,PF,19,MEM,48,18,25.7,5.0,11.7,.428,2.1,6.0,.357,2.9,5.8,.502,.519,2.4,3.2,.752,1.1,3.0,4.1,1.2,0.6,0.5,1.4,1.3,14.6
+244,Isaiah Jackson,C,22,IND,59,3,13.1,2.6,3.9,.665,0.0,0.1,.000,2.6,3.9,.677,.665,1.2,1.7,.716,1.6,2.4,4.0,0.8,0.6,1.0,0.7,2.3,6.5
+245,Jaren Jackson Jr.,C,24,MEM,66,66,32.2,7.8,17.6,.444,1.8,5.5,.320,6.0,12.1,.501,.494,5.1,6.3,.808,1.3,4.2,5.5,2.3,1.2,1.6,2.4,3.6,22.5
+246,Justin Jackson,SF,28,MIN,2,0,0.5,0.0,0.0,,0.0,0.0,,0.0,0.0,,,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+247,Quenton Jackson,PG,25,IND,3,0,3.7,0.0,0.3,.000,0.0,0.0,,0.0,0.3,.000,.000,0.7,0.7,1.000,0.0,1.3,1.3,0.7,0.3,0.0,0.0,0.3,0.7
+248,Reggie Jackson,PG,33,DEN,82,23,22.2,4.0,9.2,.431,1.2,3.5,.359,2.7,5.8,.475,.499,1.0,1.2,.806,0.4,1.5,1.9,3.8,0.5,0.2,1.3,1.8,10.2
+249,Trayce Jackson-Davis,PF,23,GSW,68,16,16.6,3.4,4.9,.702,0.0,0.0,.000,3.4,4.9,.704,.702,1.1,1.9,.561,2.0,3.0,5.0,1.2,0.4,1.1,0.7,1.6,7.9
+250,LeBron James,PF,39,LAL,71,71,35.3,9.6,17.9,.540,2.1,5.1,.410,7.5,12.8,.592,.599,4.3,5.7,.750,0.9,6.4,7.3,8.3,1.3,0.5,3.5,1.1,25.7
+251,Jaime Jaquez Jr.,SF,22,MIA,75,20,28.2,4.5,9.3,.489,0.9,2.7,.322,3.7,6.6,.558,.536,1.9,2.3,.811,1.1,2.7,3.8,2.6,1.0,0.3,1.5,2.2,11.9
+252,DeJon Jarreau,SG,26,MEM,9,0,16.7,1.9,5.6,.340,0.4,1.4,.308,1.4,4.1,.351,.380,0.6,1.2,.455,0.9,4.0,4.9,2.9,0.7,0.2,1.0,1.6,4.8
+253,DaQuan Jeffries,SG,26,NYK,17,0,2.7,0.4,1.0,.353,0.1,0.6,.200,0.2,0.4,.571,.412,0.0,0.1,.000,0.0,0.3,0.3,0.3,0.0,0.1,0.2,0.1,0.8
+254,Trey Jemison,C,24,TOT,25,14,22.9,3.0,5.4,.551,0.0,0.0,,3.0,5.4,.551,.551,0.8,1.0,.840,2.6,2.8,5.4,1.1,0.5,1.1,1.4,2.8,6.8
+254,Trey Jemison,C,24,WAS,2,0,0.5,0.0,0.0,,0.0,0.0,,0.0,0.0,,,0.0,0.0,,0.0,0.5,0.5,0.0,0.0,0.0,0.5,0.0,0.0
+254,Trey Jemison,C,24,MEM,23,14,24.9,3.3,5.9,.551,0.0,0.0,,3.3,5.9,.551,.551,0.9,1.1,.840,2.8,3.0,5.8,1.2,0.5,1.2,1.4,3.0,7.4
+255,Ty Jerome,SG,26,CLE,2,0,7.5,1.0,2.0,.500,0.0,0.5,.000,1.0,1.5,.667,.500,0.0,0.0,,0.5,0.0,0.5,1.5,0.0,0.0,0.5,1.0,2.0
+256,Isaiah Joe,SG,24,OKC,78,1,18.5,2.8,6.2,.458,1.9,4.5,.416,1.0,1.7,.568,.609,0.6,0.7,.865,0.4,1.9,2.3,1.3,0.6,0.3,0.6,1.1,8.2
+257,Cameron Johnson,PF,27,BRK,58,47,27.6,4.8,10.7,.446,2.4,6.1,.391,2.4,4.6,.519,.557,1.5,1.9,.789,0.9,3.4,4.3,2.4,0.8,0.3,1.0,1.8,13.4
+258,Jalen Johnson,SF,22,ATL,56,52,33.7,6.4,12.5,.511,1.3,3.6,.355,5.1,9.0,.574,.562,1.9,2.6,.728,1.3,7.4,8.7,3.6,1.2,0.8,1.8,2.4,16.0
+259,James Johnson,PF,36,IND,9,0,5.2,0.3,1.1,.300,0.0,0.2,.000,0.3,0.9,.375,.300,0.2,0.2,1.000,0.1,0.3,0.4,0.9,0.6,0.1,0.6,1.0,0.9
+260,Keldon Johnson,SF,24,SAS,69,27,29.5,5.7,12.5,.454,1.8,5.3,.346,3.8,7.2,.533,.527,2.5,3.2,.792,1.4,4.1,5.5,2.8,0.7,0.3,1.4,2.2,15.7
+261,Keon Johnson,SG,21,BRK,5,0,12.2,1.6,4.2,.381,0.8,2.0,.400,0.8,2.2,.364,.476,2.2,2.4,.917,0.6,0.8,1.4,0.6,0.6,0.2,1.0,1.0,6.2
+262,Keyontae Johnson,SF,23,OKC,9,0,7.3,0.6,1.8,.313,0.1,0.3,.333,0.4,1.4,.308,.344,0.0,0.0,,0.4,0.7,1.1,0.4,0.1,0.0,0.4,0.6,1.2
+263,Nikola Jokić,C,28,DEN,79,79,34.6,10.4,17.9,.583,1.1,2.9,.359,9.4,14.9,.626,.612,4.5,5.5,.817,2.8,9.5,12.4,9.0,1.4,0.9,3.0,2.5,26.4
+264,Colby Jones,SG,21,SAC,30,0,6.4,0.9,2.4,.394,0.1,0.7,.091,0.9,1.6,.531,.408,0.2,0.4,.545,0.6,0.8,1.3,0.7,0.2,0.2,0.3,1.0,2.1
+265,Damian Jones,C,28,CLE,39,0,6.9,1.1,1.8,.597,0.1,0.4,.214,1.0,1.5,.690,.618,0.5,0.5,.857,0.5,1.1,1.6,0.4,0.2,0.3,0.3,0.7,2.7
+266,Derrick Jones Jr.,SF,26,DAL,76,66,23.5,3.1,6.5,.483,1.1,3.1,.343,2.1,3.4,.613,.566,1.3,1.8,.713,1.1,2.2,3.3,1.0,0.7,0.7,0.9,1.6,8.6
+267,Herbert Jones,SF,25,NOP,76,76,30.5,3.9,7.7,.498,1.5,3.6,.418,2.4,4.1,.568,.595,1.8,2.1,.867,1.0,2.6,3.6,2.6,1.4,0.8,1.2,2.9,11.0
+268,Mason Jones,SG,25,SAC,5,0,5.6,0.4,1.6,.250,0.4,1.4,.286,0.0,0.2,.000,.375,0.2,0.4,.500,0.2,0.8,1.0,1.0,0.2,0.0,0.4,0.6,1.4
+269,Tre Jones,PG,24,SAS,77,48,27.8,3.9,7.8,.505,0.8,2.5,.335,3.1,5.3,.584,.558,1.3,1.5,.856,0.8,3.0,3.8,6.2,1.0,0.1,1.5,1.2,10.0
+270,Tyus Jones,PG,27,WAS,66,66,29.3,4.9,10.1,.489,1.6,3.9,.414,3.3,6.2,.537,.569,0.5,0.6,.800,0.3,2.4,2.7,7.3,1.1,0.3,1.0,0.7,12.0
+271,DeAndre Jordan,C,35,DEN,36,2,11.0,1.6,2.6,.624,0.0,0.0,,1.6,2.6,.624,.624,0.7,1.5,.491,1.5,2.9,4.4,0.7,0.2,0.4,0.6,1.6,3.9
+272,Cory Joseph,PG,32,GSW,26,0,11.4,0.9,2.5,.359,0.5,1.6,.310,0.4,0.8,.455,.461,0.2,0.3,.571,0.2,0.9,1.2,1.6,0.2,0.1,0.5,1.0,2.4
+273,Nikola Jović,PF,20,MIA,46,38,19.5,2.8,6.1,.452,1.4,3.5,.399,1.3,2.6,.525,.568,0.7,1.0,.702,0.5,3.6,4.2,2.0,0.5,0.3,0.9,1.8,7.7
+274,Johnny Juzang,SG,22,UTA,20,5,18.6,2.6,5.5,.464,1.6,3.9,.416,1.0,1.7,.576,.609,0.5,0.7,.714,0.4,1.5,1.8,1.2,0.2,0.1,0.7,1.5,7.2
+275,Luke Kennard,SG,27,MEM,39,22,25.6,3.6,8.1,.448,2.7,6.1,.450,0.9,2.0,.442,.617,1.0,1.2,.889,0.3,2.6,2.9,3.5,0.5,0.1,1.4,1.0,11.0
+276,Walker Kessler,C,22,UTA,64,22,23.3,3.6,5.5,.654,0.1,0.3,.211,3.5,5.2,.680,.660,0.9,1.5,.602,2.6,4.9,7.5,0.9,0.5,2.4,1.0,2.1,8.1
+277,Braxton Key,SF,26,DEN,20,0,3.0,0.4,0.9,.412,0.1,0.3,.400,0.3,0.6,.417,.471,0.3,0.4,.750,0.2,0.8,0.9,0.5,0.1,0.1,0.1,0.3,1.1
+278,Corey Kispert,SF,24,WAS,80,22,25.8,4.8,9.9,.486,2.3,6.0,.383,2.5,3.9,.643,.602,1.4,2.0,.726,0.4,2.4,2.8,2.0,0.5,0.2,1.2,1.8,13.4
+279,Maxi Kleber,C,32,DAL,43,7,20.3,1.5,3.4,.432,0.7,2.1,.348,0.7,1.3,.574,.541,0.8,1.1,.702,0.7,2.6,3.3,1.6,0.4,0.7,0.7,1.8,4.4
+280,Kevin Knox,SF,24,DET,31,11,18.1,2.8,6.0,.462,1.0,3.0,.330,1.8,3.0,.598,.546,0.6,0.7,.909,0.6,1.8,2.4,0.7,0.4,0.2,0.7,1.6,7.2
+281,John Konchar,SF,27,MEM,55,23,21.3,1.6,3.8,.423,0.7,2.2,.317,0.9,1.6,.568,.514,0.4,0.5,.840,1.3,3.4,4.7,2.0,0.9,0.9,0.8,1.4,4.3
+282,Furkan Korkmaz,SG,26,PHI,35,0,8.6,0.9,2.2,.395,0.4,1.1,.350,0.5,1.0,.444,.487,0.4,0.6,.700,0.1,0.7,0.9,0.7,0.4,0.1,0.3,0.5,2.5
+283,Luke Kornet,C,28,BOS,63,7,15.6,2.3,3.2,.700,0.0,0.0,1.000,2.2,3.2,.698,.702,0.8,0.9,.907,1.9,2.3,4.1,1.1,0.4,1.0,0.3,1.2,5.3
+284,Vit Krejci,PG,23,ATL,22,14,24.6,2.3,4.7,.490,1.3,3.1,.412,1.0,1.6,.639,.625,0.2,0.3,.833,0.5,1.9,2.4,2.3,0.6,0.3,0.9,2.2,6.1
+285,Jonathan Kuminga,PF,21,GSW,74,46,26.3,6.2,11.6,.529,0.7,2.2,.321,5.4,9.4,.578,.560,3.0,4.0,.746,1.2,3.6,4.8,2.2,0.7,0.5,1.6,2.3,16.1
+286,Kyle Kuzma,PF,28,WAS,70,70,32.6,8.7,18.8,.463,2.2,6.4,.336,6.5,12.3,.530,.521,2.7,3.4,.775,0.9,5.7,6.6,4.2,0.5,0.7,2.7,2.2,22.2
+287,Jock Landale,C,28,HOU,56,3,13.6,1.9,3.7,.515,0.2,0.7,.250,1.7,3.0,.578,.539,0.9,1.2,.800,1.6,1.5,3.1,1.2,0.4,0.6,0.6,1.3,4.9
+288,Jake LaRavia,PF,22,MEM,35,6,23.0,3.4,8.7,.389,1.5,4.5,.340,1.9,4.2,.442,.477,2.6,3.1,.826,1.5,2.2,3.7,1.7,0.8,0.3,1.3,2.0,10.8
+289,Zach LaVine,SG,28,CHI,25,23,34.9,6.8,15.0,.452,2.4,6.8,.349,4.4,8.3,.536,.531,3.5,4.1,.854,0.3,4.8,5.2,3.9,0.8,0.3,2.1,2.3,19.5
+290,A.J. Lawson,SG,23,DAL,42,0,7.4,1.3,2.9,.446,0.3,1.2,.260,1.0,1.7,.577,.500,0.4,0.5,.652,0.3,0.9,1.2,0.5,0.2,0.1,0.3,0.5,3.2
+291,Saben Lee,PG,24,PHO,24,0,7.7,0.8,2.3,.364,0.1,0.7,.125,0.8,1.6,.462,.382,1.2,1.6,.744,0.4,0.9,1.3,1.3,0.3,0.1,0.3,0.5,3.0
+292,Alex Len,C,30,SAC,48,0,9.3,1.0,1.7,.617,0.0,0.1,.000,1.0,1.6,.641,.617,0.4,0.7,.588,1.0,1.7,2.7,1.0,0.2,0.7,0.6,1.4,2.5
+293,Kawhi Leonard,SF,32,LAC,68,68,34.3,9.0,17.1,.525,2.1,4.9,.417,6.9,12.1,.569,.585,3.7,4.2,.885,1.2,4.9,6.1,3.6,1.6,0.9,1.8,1.4,23.7
+294,Caris LeVert,SG,29,CLE,68,10,28.8,5.1,12.1,.421,1.6,4.9,.325,3.5,7.2,.486,.487,2.3,3.0,.766,0.6,3.5,4.1,5.1,1.1,0.5,1.7,1.8,14.0
+295,Kira Lewis Jr.,PG,22,TOT,28,0,9.5,1.2,3.3,.370,0.1,1.2,.121,1.1,2.1,.508,.391,0.6,0.7,.850,0.3,0.6,0.9,1.3,0.3,0.1,0.5,0.5,3.2
+295,Kira Lewis Jr.,PG,22,NOP,15,0,9.6,1.1,3.5,.308,0.1,1.3,.100,0.9,2.1,.438,.327,0.7,0.7,.909,0.3,0.7,0.9,1.2,0.3,0.1,0.5,0.5,2.9
+295,Kira Lewis Jr.,PG,22,TOR,1,0,2.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
+295,Kira Lewis Jr.,PG,22,UTA,12,0,9.9,1.5,3.3,.450,0.2,1.1,.154,1.3,2.3,.593,.475,0.6,0.8,.778,0.4,0.6,1.0,1.6,0.3,0.1,0.5,0.3,3.8
+296,Maxwell Lewis,SF,21,LAL,34,0,3.0,0.1,0.6,.190,0.0,0.3,.111,0.1,0.4,.250,.214,0.1,0.1,.667,0.1,0.1,0.1,0.2,0.1,0.0,0.3,0.1,0.3
+297,E.J. Liddell,PF,23,NOP,8,0,2.9,0.1,0.8,.167,0.0,0.6,.000,0.1,0.1,1.000,.167,0.3,0.3,1.000,0.0,0.6,0.6,0.1,0.3,0.3,0.3,0.3,0.5
+298,Damian Lillard,PG,33,MIL,73,73,35.3,7.4,17.5,.424,3.0,8.5,.354,4.4,9.0,.490,.510,6.5,7.0,.920,0.5,3.9,4.4,7.0,1.0,0.2,2.6,1.8,24.3
+299,Nassir Little,SF,23,PHO,45,2,10.2,1.3,2.8,.460,0.5,1.6,.300,0.8,1.2,.667,.544,0.4,0.4,.850,0.3,1.4,1.7,0.5,0.2,0.2,0.3,0.9,3.4
+300,Dereck Lively II,C,19,DAL,55,42,23.5,4.0,5.4,.747,0.0,0.0,.000,4.0,5.3,.752,.747,0.7,1.5,.506,2.5,4.4,6.9,1.1,0.7,1.4,0.9,2.6,8.8
+301,Isaiah Livers,PF,25,DET,23,6,20.4,1.7,5.0,.345,1.0,3.3,.286,0.8,1.7,.462,.440,0.5,0.8,.667,0.7,1.4,2.1,1.1,0.6,0.2,0.7,1.9,5.0
+302,Chris Livingston,SF,20,MIL,21,0,4.3,0.5,1.0,.500,0.0,0.2,.200,0.5,0.8,.588,.523,0.1,0.2,.750,0.1,0.9,1.0,0.2,0.1,0.0,0.2,0.7,1.2
+303,Kenneth Lofton Jr.,PF,21,TOT,21,0,9.5,1.7,3.7,.462,0.3,1.1,.292,1.4,2.6,.537,.506,0.8,1.2,.654,0.6,1.2,1.8,1.6,0.3,0.2,0.7,1.1,4.6
+303,Kenneth Lofton Jr.,PF,21,MEM,15,0,6.6,0.9,2.5,.378,0.2,0.7,.300,0.7,1.8,.407,.419,0.5,1.0,.533,0.1,0.9,1.0,0.9,0.2,0.2,0.4,1.0,2.6
+303,Kenneth Lofton Jr.,PF,21,PHI,2,0,4.5,0.5,3.0,.167,0.0,1.0,.000,0.5,2.0,.250,.167,0.0,0.0,,0.0,1.5,1.5,0.0,0.0,0.0,1.0,0.0,1.0
+303,Kenneth Lofton Jr.,PF,21,UTA,4,0,22.8,5.3,8.8,.600,1.0,3.0,.333,4.3,5.8,.739,.657,2.3,2.8,.818,2.5,2.5,5.0,4.8,0.8,0.5,1.5,2.3,13.8
+304,Kevon Looney,C,27,GSW,74,36,16.1,1.9,3.1,.597,0.0,0.0,.000,1.9,3.1,.599,.597,0.8,1.1,.675,1.9,3.7,5.7,1.8,0.4,0.4,0.7,2.1,4.5
+305,Brook Lopez,C,35,MIL,79,79,30.5,4.6,9.6,.485,1.9,5.1,.366,2.8,4.4,.621,.583,1.4,1.7,.821,1.5,3.7,5.2,1.6,0.5,2.4,1.0,2.4,12.5
+306,Robin Lopez,C,35,MIL,16,2,4.1,0.4,1.2,.368,0.1,0.5,.250,0.3,0.7,.455,.421,0.1,0.1,1.000,0.2,0.1,0.3,0.3,0.1,0.2,0.1,0.4,1.1
+307,Kevin Love,PF,35,MIA,55,5,16.8,2.9,6.5,.440,1.5,4.4,.344,1.3,2.1,.643,.557,1.5,2.0,.787,1.1,5.1,6.1,2.1,0.3,0.2,0.9,1.8,8.8
+308,Kyle Lowry,PG,37,TOT,60,55,28.2,2.7,6.3,.432,1.6,4.2,.392,1.1,2.1,.512,.562,1.1,1.3,.840,0.6,2.7,3.2,4.2,1.0,0.4,1.4,2.4,8.1
+308,Kyle Lowry,PG,37,MIA,37,35,28.0,2.8,6.5,.426,1.7,4.4,.385,1.1,2.2,.506,.554,0.9,1.1,.833,0.7,2.8,3.5,4.0,1.1,0.4,1.4,2.5,8.2
+308,Kyle Lowry,PG,37,PHI,23,20,28.4,2.6,5.9,.444,1.6,3.9,.404,1.0,2.0,.522,.578,1.2,1.4,.848,0.4,2.4,2.8,4.6,0.9,0.3,1.4,2.3,8.0
+309,Seth Lundy,SF,23,ATL,9,0,5.8,0.4,1.9,.235,0.3,1.4,.231,0.1,0.4,.250,.324,0.3,0.4,.750,0.1,0.7,0.8,0.0,0.0,0.0,0.1,0.7,1.6
+310,Trey Lyles,PF,28,SAC,58,0,20.0,2.5,5.5,.445,1.4,3.8,.384,1.0,1.8,.578,.576,0.8,1.2,.700,1.1,3.4,4.4,1.2,0.3,0.3,0.7,1.5,7.2
+311,Théo Maledon,PG,22,TOT,17,1,12.5,1.2,4.1,.286,0.3,1.8,.167,0.9,2.4,.375,.321,0.8,0.9,.933,0.2,1.2,1.4,1.6,0.4,0.0,1.1,1.2,3.5
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+311,Théo Maledon,PG,22,PHO,4,0,3.3,0.3,1.0,.250,0.0,0.0,,0.3,1.0,.250,.250,0.8,0.8,1.000,0.0,0.3,0.3,0.0,0.0,0.0,0.8,0.8,1.3
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+314,Tre Mann,PG,22,TOT,41,28,24.1,3.5,7.7,.459,1.1,3.1,.373,2.4,4.6,.516,.533,1.1,1.4,.763,0.5,3.1,3.6,4.0,1.2,0.1,1.6,1.2,9.3
+314,Tre Mann,PG,22,OKC,13,0,9.2,1.5,3.1,.500,0.6,1.5,.421,0.9,1.6,.571,.600,0.1,0.1,1.000,0.3,1.5,1.8,1.5,0.2,0.0,0.7,0.2,3.8
+314,Tre Mann,PG,22,CHO,28,28,31.0,4.5,9.9,.453,1.4,3.8,.364,3.1,6.0,.509,.524,1.6,2.1,.759,0.6,3.9,4.5,5.2,1.7,0.1,2.0,1.6,11.9
+315,Boban Marjanović,C,35,HOU,14,0,5.1,1.3,2.4,.529,0.0,0.1,.000,1.3,2.3,.563,.529,0.6,1.0,.643,0.5,1.8,2.3,0.4,0.1,0.1,0.4,0.4,3.2
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+318,Caleb Martin,SF,28,MIA,64,23,27.4,3.8,8.7,.431,1.3,3.6,.349,2.5,5.1,.489,.504,1.2,1.5,.778,1.2,3.2,4.4,2.2,0.7,0.5,1.2,2.0,10.0
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+320,KJ Martin,SF,23,LAC,2,0,15.5,2.0,5.0,.400,0.5,2.5,.200,1.5,2.5,.600,.450,0.5,1.0,.500,0.5,1.0,1.5,0.5,1.0,0.5,1.0,1.5,5.0
+320,KJ Martin,SF,23,PHI,58,2,12.3,1.6,2.9,.544,0.1,0.4,.304,1.5,2.6,.581,.564,0.4,0.7,.538,0.7,1.5,2.2,0.9,0.3,0.2,0.5,1.7,3.7
+321,Garrison Mathews,SG,27,ATL,66,5,15.0,1.6,3.4,.456,1.3,2.9,.440,0.3,0.5,.545,.644,0.5,0.6,.810,0.3,1.1,1.4,0.6,0.3,0.1,0.3,1.7,4.9
+322,Bennedict Mathurin,SF,21,IND,59,19,26.1,5.0,11.2,.446,1.4,3.6,.374,3.7,7.6,.481,.507,3.1,3.8,.821,1.0,2.9,4.0,2.0,0.6,0.2,1.6,2.0,14.5
+323,Wesley Matthews,SG,37,ATL,36,3,11.5,0.9,2.7,.351,0.7,1.9,.348,0.3,0.8,.357,.474,0.5,0.7,.750,0.3,1.2,1.5,0.6,0.4,0.3,0.2,1.2,3.1
+324,Tyrese Maxey,PG,23,PHI,70,70,37.5,9.1,20.3,.450,3.0,8.1,.373,6.1,12.1,.501,.524,4.7,5.4,.868,0.5,3.2,3.7,6.2,1.0,0.5,1.7,2.2,25.9
+325,Skylar Mays,PG-SG,26,TOT,38,5,11.4,1.7,4.2,.396,0.4,1.4,.296,1.2,2.8,.448,.447,0.3,0.4,.765,0.3,0.9,1.1,2.2,0.6,0.1,0.5,0.7,4.1
+325,Skylar Mays,PG,26,POR,21,5,17.0,2.5,6.6,.384,0.7,2.3,.286,1.9,4.2,.438,.435,0.6,0.8,.765,0.4,1.3,1.8,3.6,0.7,0.1,0.9,1.0,6.3
+325,Skylar Mays,SG,26,LAL,17,0,4.5,0.6,1.2,.476,0.1,0.3,.400,0.5,0.9,.500,.524,0.0,0.0,,0.1,0.3,0.4,0.6,0.4,0.1,0.1,0.4,1.3
+326,Miles McBride,PG,23,NYK,68,14,19.5,3.0,6.7,.452,1.6,3.9,.410,1.4,2.8,.511,.572,0.6,0.7,.860,0.5,1.0,1.5,1.7,0.7,0.1,0.4,1.6,8.3
+327,CJ McCollum,PG,32,NOP,66,66,32.7,7.3,16.0,.459,3.6,8.4,.429,3.7,7.5,.492,.572,1.7,2.1,.827,0.6,3.7,4.3,4.6,0.9,0.6,1.7,1.9,20.0
+328,T.J. McConnell,PG,31,IND,71,4,18.2,4.6,8.4,.556,0.3,0.6,.409,4.4,7.7,.568,.572,0.7,0.9,.790,0.5,2.2,2.7,5.5,1.0,0.1,1.5,1.1,10.2
+329,Jaden McDaniels,SF,23,MIN,72,71,29.2,4.3,8.7,.489,1.2,3.5,.337,3.1,5.2,.592,.557,0.8,1.1,.722,0.8,2.3,3.1,1.4,0.9,0.6,1.2,3.0,10.5
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+331,Doug McDermott,SF,32,TOT,64,0,14.1,1.9,4.4,.433,1.5,3.6,.410,0.5,0.9,.527,.599,0.2,0.3,.579,0.2,0.8,0.9,1.1,0.3,0.0,0.4,1.0,5.5
+331,Doug McDermott,SF,32,SAS,46,0,15.2,2.1,4.7,.442,1.7,3.8,.439,0.4,0.9,.452,.619,0.2,0.4,.588,0.2,0.9,1.0,1.2,0.2,0.0,0.5,1.0,6.0
+331,Doug McDermott,SF,32,IND,18,0,11.3,1.6,3.8,.406,1.0,3.1,.321,0.6,0.7,.769,.536,0.1,0.1,.500,0.1,0.5,0.6,0.7,0.3,0.0,0.2,0.9,4.2
+332,JaVale McGee,C,36,SAC,46,0,7.4,1.7,2.9,.598,0.0,0.2,.143,1.7,2.7,.624,.602,0.6,1.0,.578,0.9,1.8,2.7,0.4,0.3,0.4,0.7,1.0,4.0
+333,Bryce McGowens,SG,21,CHO,59,14,14.9,1.7,3.9,.439,0.6,1.7,.333,1.1,2.2,.523,.513,1.1,1.4,.776,0.3,1.4,1.7,0.9,0.4,0.2,0.5,1.3,5.1
+334,Jordan McLaughlin,PG,27,MIN,56,0,11.2,1.3,2.6,.483,0.8,1.6,.472,0.5,1.0,.500,.628,0.2,0.3,.722,0.3,1.0,1.3,2.0,0.6,0.1,0.3,0.6,3.5
+335,De'Anthony Melton,SG,25,PHI,38,33,26.9,3.7,9.5,.386,2.0,5.6,.360,1.6,3.8,.425,.493,1.7,2.1,.835,0.7,3.0,3.7,3.0,1.6,0.4,1.0,2.2,11.1
+336,Nathan Mensah,C,25,CHO,25,0,12.3,0.5,1.1,.429,0.0,0.0,,0.5,1.1,.429,.429,0.4,0.5,.750,1.2,1.4,2.6,0.4,0.2,0.6,0.6,2.6,1.3
+337,Sam Merrill,SG,27,CLE,61,1,17.5,2.6,6.4,.402,2.3,5.8,.404,0.2,0.6,.385,.584,0.4,0.5,.929,0.4,1.6,2.0,1.8,0.3,0.1,0.3,1.2,8.0
+338,Chimezie Metu,C,26,TOT,51,12,16.8,2.4,4.8,.504,0.5,1.8,.298,1.9,2.9,.633,.561,1.1,1.3,.906,0.8,3.0,3.8,0.9,0.8,0.3,1.0,1.4,6.5
+338,Chimezie Metu,C,26,PHO,37,5,12.1,1.8,3.5,.508,0.4,1.4,.294,1.4,2.1,.646,.565,1.0,1.2,.884,0.6,2.3,3.0,0.5,0.5,0.2,0.7,1.1,5.0
+338,Chimezie Metu,C,26,DET,14,7,29.4,4.1,8.1,.500,0.9,3.1,.302,3.1,5.1,.620,.557,1.4,1.5,.952,1.2,4.8,6.0,1.9,1.7,0.5,1.8,2.3,10.5
+339,Vasilije Micić,PG,30,TOT,60,21,19.6,2.7,6.2,.430,0.7,2.6,.279,1.9,3.6,.537,.488,1.0,1.3,.813,0.2,1.2,1.5,4.4,0.5,0.1,1.7,1.1,7.0
+339,Vasilije Micić,PG,30,OKC,30,0,12.0,1.2,3.0,.407,0.4,1.5,.244,0.9,1.5,.565,.467,0.5,0.6,.737,0.0,0.8,0.8,2.5,0.3,0.1,1.0,0.7,3.3
+339,Vasilije Micić,PG,30,CHO,30,21,27.2,4.1,9.3,.437,1.1,3.6,.294,3.0,5.7,.529,.495,1.6,1.9,.839,0.4,1.7,2.1,6.2,0.7,0.1,2.3,1.5,10.8
+340,Khris Middleton,SF,32,MIL,55,55,27.0,5.8,11.7,.493,1.7,4.5,.381,4.1,7.2,.563,.566,1.8,2.2,.833,0.7,3.9,4.7,5.3,0.9,0.3,2.3,2.6,15.1
+341,Brandon Miller,SF,21,CHO,74,68,32.2,6.4,14.6,.440,2.5,6.7,.373,3.9,8.0,.495,.524,1.9,2.3,.827,0.8,3.4,4.3,2.4,0.9,0.6,1.8,2.5,17.3
+342,Jordan Miller,SF,24,LAC,8,0,3.5,0.6,1.1,.556,0.1,0.3,.500,0.5,0.9,.571,.611,0.3,0.3,1.000,0.4,0.3,0.6,0.0,0.0,0.0,0.1,0.3,1.6
+343,Leonard Miller,SF,20,MIN,17,0,3.1,0.8,1.2,.650,0.1,0.3,.400,0.6,0.9,.733,.700,0.1,0.1,.500,0.3,0.9,1.2,0.5,0.1,0.1,0.1,0.3,1.7
+344,Dariq Whitehead,SF,19,BRK,2,0,12.0,0.5,2.5,.200,0.0,1.5,.000,0.5,1.0,.500,.200,0.5,1.0,.500,0.0,2.0,2.0,1.5,0.0,0.5,0.0,0.0,1.5
+345,Patty Mills,PG,35,TOT,32,5,13.0,1.4,4.1,.351,0.8,2.7,.276,0.7,1.4,.500,.443,0.3,0.3,1.000,0.3,0.8,1.1,1.1,0.6,0.0,0.5,1.0,4.0
+345,Patty Mills,PG,35,ATL,19,0,10.6,1.0,2.7,.373,0.7,1.8,.382,0.3,0.9,.353,.500,0.0,0.0,,0.3,0.8,1.1,0.7,0.5,0.1,0.4,0.9,2.7
+345,Patty Mills,PG,35,MIA,13,5,16.4,2.1,6.2,.338,0.8,4.1,.208,1.2,2.1,.593,.406,0.8,0.8,1.000,0.3,0.8,1.2,1.5,0.8,0.0,0.6,1.2,5.8
+346,Shake Milton,SG,27,TOT,48,0,12.1,1.6,4.1,.405,0.4,1.3,.281,1.3,2.7,.466,.451,0.8,1.0,.816,0.3,1.3,1.6,1.3,0.4,0.1,0.6,1.1,4.5
+346,Shake Milton,SG,27,MIN,38,0,12.9,1.7,4.2,.400,0.4,1.4,.264,1.3,2.8,.467,.444,0.9,1.2,.818,0.2,1.2,1.3,1.3,0.4,0.1,0.7,1.2,4.7
+346,Shake Milton,SG,27,DET,4,0,15.8,2.8,6.5,.423,0.8,2.3,.333,2.0,4.3,.471,.481,0.5,0.8,.667,1.8,2.8,4.5,1.5,0.5,0.3,0.8,0.8,6.8
+346,Shake Milton,SG,27,NYK,6,0,4.5,0.7,1.5,.444,0.2,0.3,.500,0.5,1.2,.429,.500,0.3,0.3,1.000,0.0,1.0,1.0,0.7,0.2,0.0,0.0,1.2,1.8
+347,Justin Minaya,SF,24,POR,34,1,11.2,0.6,2.2,.297,0.4,1.4,.245,0.3,0.7,.400,.378,0.1,0.3,.556,0.9,0.8,1.6,0.6,0.3,0.3,0.3,1.0,1.8
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+349,Davion Mitchell,PG,25,SAC,72,4,15.3,2.0,4.5,.452,0.8,2.2,.361,1.2,2.3,.539,.540,0.4,0.6,.714,0.3,1.1,1.3,1.9,0.2,0.0,0.6,1.2,5.3
+350,Donovan Mitchell,SG,27,CLE,55,55,35.3,9.1,19.8,.462,3.3,9.0,.368,5.8,10.8,.539,.545,5.0,5.8,.865,0.8,4.3,5.1,6.1,1.8,0.5,2.8,2.1,26.6
+351,Evan Mobley,PF,22,CLE,50,50,30.6,6.4,11.0,.580,0.4,1.2,.373,6.0,9.9,.604,.600,2.5,3.4,.719,2.2,7.1,9.4,3.2,0.9,1.4,1.8,2.7,15.7
+352,Isaiah Mobley,PF,24,CLE,10,0,7.2,1.0,2.4,.417,0.3,1.0,.300,0.7,1.4,.500,.479,0.0,0.1,.000,0.3,0.7,1.0,0.6,0.2,0.0,0.2,0.6,2.3
+353,Malik Monk,SG,25,SAC,72,0,26.0,5.5,12.4,.443,2.1,5.9,.350,3.4,6.5,.528,.526,2.4,2.8,.829,0.4,2.6,2.9,5.1,0.6,0.5,2.1,2.2,15.4
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+358,Ja Morant,PG,24,MEM,9,9,35.3,8.9,18.9,.471,1.6,5.7,.275,7.3,13.2,.555,.512,5.8,7.1,.813,0.7,4.9,5.6,8.1,0.8,0.6,3.0,2.1,25.1
+359,Marcus Morris,PF,34,TOT,49,7,16.7,2.4,5.4,.439,1.0,2.4,.403,1.4,3.0,.469,.530,0.7,0.9,.818,0.5,2.3,2.7,0.7,0.3,0.3,0.6,1.5,6.4
+359,Marcus Morris,PF,34,PHI,37,7,17.2,2.4,5.5,.439,1.0,2.4,.400,1.5,3.1,.470,.527,0.8,1.0,.861,0.5,2.5,2.9,0.7,0.4,0.3,0.5,1.6,6.7
+359,Marcus Morris,PF,34,CLE,12,0,15.0,2.2,4.9,.441,1.0,2.4,.414,1.2,2.5,.467,.542,0.4,0.7,.625,0.4,1.7,2.1,0.8,0.2,0.2,0.9,1.1,5.8
+360,Markieff Morris,PF,34,DAL,26,1,8.3,0.9,2.6,.338,0.6,1.6,.357,0.3,1.0,.308,.449,0.2,0.2,.833,0.2,1.3,1.5,0.6,0.2,0.1,0.5,0.5,2.5
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+362,Trey Murphy III,SF,23,NOP,57,23,29.6,4.8,10.9,.443,3.0,7.8,.380,1.9,3.1,.602,.579,2.2,2.6,.815,0.7,4.2,4.9,2.2,0.9,0.5,0.6,1.2,14.8
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+367,Mike Muscala,C,32,WAS,24,2,14.1,1.4,3.8,.367,0.6,2.1,.275,0.8,1.6,.487,.444,0.6,0.8,.750,0.9,2.2,3.1,0.9,0.2,0.3,0.7,1.3,4.0
+367,Mike Muscala,C,32,DET,13,4,13.2,1.2,3.4,.341,1.0,2.6,.382,0.2,0.8,.200,.489,0.2,0.3,.500,0.8,1.5,2.2,0.8,0.2,0.4,0.2,1.0,3.5
+367,Mike Muscala,C,32,OKC,16,0,5.7,0.5,1.4,.364,0.1,0.7,.091,0.4,0.7,.636,.386,0.0,0.1,.000,0.4,0.9,1.3,0.3,0.1,0.1,0.3,0.5,1.1
+368,Svi Mykhailiuk,SF,26,BOS,41,2,10.1,1.4,3.3,.416,1.0,2.6,.389,0.4,0.7,.517,.569,0.1,0.2,.667,0.3,1.0,1.2,0.9,0.3,0.0,0.3,0.5,4.0
+369,Larry Nance Jr.,C,31,NOP,61,0,19.9,2.3,4.0,.573,0.4,1.1,.415,1.8,2.9,.631,.629,0.8,1.0,.770,1.5,3.5,5.0,1.9,1.0,0.3,0.8,1.6,5.7
+370,Pete Nance,PF,23,CLE,8,0,3.4,0.1,0.8,.167,0.1,0.1,1.000,0.0,0.6,.000,.250,0.0,0.3,.000,0.0,0.4,0.4,0.0,0.1,0.0,0.1,0.3,0.4
+371,Andrew Nembhard,PG,24,IND,68,47,25.0,3.8,7.7,.498,0.9,2.5,.357,2.9,5.2,.567,.557,0.7,0.8,.804,0.5,1.5,2.1,4.1,0.9,0.1,1.5,2.1,9.2
+372,Aaron Nesmith,SF,24,IND,72,47,27.7,4.4,8.8,.496,1.9,4.6,.419,2.4,4.2,.581,.606,1.5,1.9,.781,0.9,2.9,3.8,1.5,0.9,0.7,0.9,3.3,12.2
+373,Georges Niang,PF,30,CLE,82,10,22.3,3.5,7.7,.449,1.8,4.8,.376,1.6,2.9,.572,.567,0.6,0.7,.850,0.3,3.1,3.4,1.2,0.4,0.2,0.9,2.3,9.4
+374,Daishen Nix,PG,21,MIN,15,0,3.3,0.6,1.6,.375,0.4,1.1,.353,0.2,0.5,.429,.500,0.2,0.4,.500,0.1,0.1,0.2,0.4,0.3,0.0,0.3,0.1,1.8
+375,Zeke Nnaji,PF,23,DEN,58,0,9.9,1.2,2.6,.463,0.1,0.4,.261,1.1,2.2,.500,.483,0.7,1.1,.677,1.1,1.1,2.2,0.6,0.3,0.7,0.5,1.4,3.2
+376,Jaylen Nowell,SG,24,TOT,13,1,16.5,2.6,6.0,.436,0.5,2.2,.207,2.2,3.8,.571,.474,0.5,0.7,.778,0.5,1.4,1.8,1.5,0.3,0.2,0.5,1.2,6.2
+376,Jaylen Nowell,SG,24,MEM,9,1,17.3,2.4,6.1,.400,0.4,2.6,.174,2.0,3.6,.563,.436,0.3,0.3,1.000,0.3,1.2,1.6,1.8,0.3,0.0,0.6,1.4,5.7
+376,Jaylen Nowell,SG,24,DET,4,0,14.5,3.0,5.8,.522,0.5,1.5,.333,2.5,4.3,.588,.565,1.0,1.5,.667,0.8,1.8,2.5,0.8,0.3,0.5,0.5,0.5,7.5
+377,Markquis Nowell,SG,24,TOR,1,0,4.0,0.0,1.0,.000,0.0,0.0,,0.0,1.0,.000,.000,2.0,2.0,1.000,2.0,0.0,2.0,2.0,1.0,0.0,0.0,0.0,2.0
+378,Frank Ntilikina,PG,25,CHO,5,0,8.6,0.2,1.8,.111,0.2,1.6,.125,0.0,0.2,.000,.167,0.4,0.4,1.000,0.2,1.0,1.2,0.8,0.0,0.0,0.2,0.8,1.0
+379,Jusuf Nurkić,C,29,PHO,76,76,27.3,4.2,8.3,.510,0.3,1.2,.244,3.9,7.1,.555,.528,2.2,3.4,.640,2.9,8.1,11.0,4.0,1.1,1.1,2.3,3.3,10.9
+380,Jordan Nwora,SF,25,TOT,52,1,13.7,2.7,5.8,.462,0.8,2.5,.336,1.8,3.2,.560,.535,0.8,1.0,.830,0.5,2.3,2.9,1.2,0.5,0.3,1.0,0.9,7.0
+380,Jordan Nwora,SF,25,IND,18,0,10.2,2.1,4.6,.451,0.6,2.0,.306,1.4,2.6,.565,.518,0.5,0.6,.818,0.4,1.4,1.8,1.0,0.3,0.1,0.7,1.1,5.2
+380,Jordan Nwora,SF,25,TOR,34,1,15.6,3.0,6.4,.465,1.0,2.8,.347,2.0,3.6,.557,.541,1.0,1.2,.833,0.6,2.8,3.4,1.3,0.6,0.4,1.2,0.9,7.9
+381,Royce O'Neale,SF,30,TOT,79,14,24.7,2.7,6.8,.397,2.0,5.3,.370,0.7,1.5,.496,.542,0.3,0.4,.686,0.8,4.0,4.8,2.8,0.7,0.6,0.9,2.2,7.7
+381,Royce O'Neale,SF,30,BRK,49,6,24.5,2.6,6.6,.388,2.0,5.4,.366,0.6,1.2,.483,.537,0.3,0.4,.682,0.8,3.7,4.5,2.8,0.7,0.6,1.0,2.4,7.4
+381,Royce O'Neale,SF,30,PHO,30,8,25.1,2.9,7.1,.411,2.0,5.2,.376,1.0,1.9,.509,.549,0.3,0.4,.692,0.7,4.5,5.2,2.7,0.9,0.5,0.7,1.8,8.1
+382,Chuma Okeke,SF,25,ORL,47,8,9.2,0.9,2.4,.357,0.5,1.7,.280,0.4,0.6,.567,.460,0.1,0.1,.571,0.4,1.3,1.7,0.4,0.2,0.1,0.3,0.4,2.3
+383,Josh Okogie,SG,25,PHO,60,11,16.0,1.4,3.4,.417,0.5,1.6,.309,1.0,1.9,.509,.488,1.3,1.7,.745,1.2,1.3,2.6,1.1,0.8,0.4,0.6,1.1,4.6
+384,Onyeka Okongwu,C,23,ATL,55,8,25.5,4.1,6.6,.611,0.4,1.3,.333,3.6,5.4,.676,.642,1.7,2.1,.793,2.6,4.2,6.8,1.3,0.5,1.1,0.8,2.9,10.2
+385,Isaac Okoro,SF,23,CLE,69,42,27.3,3.4,7.0,.490,1.2,3.1,.391,2.2,3.8,.570,.577,1.3,2.0,.679,1.0,2.0,3.0,1.9,0.8,0.5,0.8,2.0,9.4
+386,Kelly Olynyk,C,32,TOT,78,27,22.6,3.5,6.4,.555,0.8,2.0,.387,2.8,4.4,.632,.616,1.9,2.3,.833,1.4,3.9,5.3,4.4,0.9,0.4,1.9,2.9,9.8
+386,Kelly Olynyk,C,32,UTA,50,8,20.4,2.9,5.2,.562,0.7,1.7,.429,2.2,3.5,.626,.632,1.6,1.9,.842,1.3,3.8,5.1,4.4,0.7,0.2,1.6,2.6,8.1
+386,Kelly Olynyk,C,32,TOR,28,19,26.4,4.7,8.5,.548,0.9,2.5,.338,3.8,6.0,.637,.598,2.5,3.0,.824,1.6,4.0,5.6,4.6,1.3,0.6,2.5,3.5,12.7
+387,Eugene Omoruyi,SF,26,WAS,43,0,9.1,1.9,3.9,.485,0.3,1.1,.283,1.6,2.8,.562,.524,0.7,1.1,.653,0.8,1.2,2.0,0.8,0.6,0.1,0.5,1.4,4.8
+388,Cedi Osman,SF,28,SAS,72,3,17.6,2.6,5.3,.479,1.2,3.1,.389,1.4,2.3,.601,.591,0.5,0.7,.673,0.3,2.2,2.5,1.7,0.5,0.2,0.7,1.5,6.8
+389,Kelly Oubre Jr.,SF,28,PHI,68,52,30.2,5.7,12.9,.441,1.5,4.8,.311,4.2,8.1,.517,.498,2.6,3.4,.750,1.4,3.6,5.0,1.5,1.1,0.7,1.3,2.9,15.4
+390,Chris Paul,PG,38,GSW,58,18,26.4,3.6,8.1,.441,1.3,3.6,.371,2.2,4.4,.498,.525,0.7,0.9,.827,0.3,3.6,3.9,6.8,1.2,0.1,1.3,1.9,9.2
+391,Cameron Payne,PG,29,TOT,78,10,16.7,2.7,6.2,.432,1.3,3.4,.390,1.3,2.7,.486,.541,0.7,0.9,.866,0.3,1.2,1.5,2.6,0.5,0.1,0.8,1.6,7.4
+391,Cameron Payne,PG,29,MIL,47,2,14.9,2.1,4.7,.455,1.1,2.8,.397,1.0,1.9,.538,.572,0.8,0.9,.841,0.1,1.2,1.3,2.3,0.5,0.1,0.7,1.6,6.2
+391,Cameron Payne,PG,29,PHI,31,8,19.4,3.5,8.4,.413,1.7,4.4,.382,1.8,4.0,.447,.514,0.7,0.7,.913,0.5,1.3,1.8,3.1,0.6,0.3,1.1,1.7,9.3
+392,Gary Payton II,SG,31,GSW,44,0,15.5,2.3,4.2,.563,0.5,1.3,.364,1.9,2.9,.648,.617,0.3,0.5,.609,1.0,1.5,2.6,1.1,0.9,0.4,0.6,1.8,5.5
+393,Mãozinha Pereira,SF,23,MEM,7,1,17.4,2.6,5.0,.514,0.7,1.9,.385,1.9,3.1,.591,.586,1.0,1.4,.700,1.6,3.7,5.3,0.3,0.9,0.6,0.7,1.4,6.9
+394,Drew Peterson,PF,24,BOS,3,0,7.7,1.3,2.0,.667,1.0,1.7,.600,0.3,0.3,1.000,.917,0.0,0.0,,0.0,0.3,0.3,0.3,0.7,0.0,0.3,0.3,3.7
+395,Filip Petrušev,C,23,TOT,3,0,3.3,0.3,0.7,.500,0.0,0.0,,0.3,0.7,.500,.500,0.3,0.7,.500,0.0,0.3,0.3,0.0,0.0,0.0,0.0,0.3,1.0
+395,Filip Petrušev,C,23,PHI,1,0,3.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,,0.0,0.0,,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+395,Filip Petrušev,C,23,SAC,2,0,3.5,0.5,1.0,.500,0.0,0.0,,0.5,1.0,.500,.500,0.5,1.0,.500,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,1.5
+396,Julian Phillips,SF,20,CHI,40,0,8.1,0.8,1.9,.416,0.3,1.0,.316,0.5,1.0,.513,.494,0.3,0.5,.684,0.3,0.6,0.9,0.3,0.2,0.2,0.3,1.0,2.2
+397,Jalen Pickett,SG,24,DEN,27,0,4.5,0.6,1.3,.429,0.3,0.9,.360,0.2,0.4,.600,.557,0.1,0.1,.750,0.0,0.5,0.5,0.8,0.1,0.0,0.3,0.5,1.6
+398,Scotty Pippen Jr.,PG,23,MEM,21,16,25.1,4.8,9.7,.493,1.4,3.4,.417,3.3,6.2,.534,.567,2.0,2.6,.745,1.0,2.1,3.2,4.7,1.7,0.5,2.7,2.9,12.9
+399,Mason Plumlee,C,33,LAC,46,11,14.7,1.9,3.3,.569,0.0,0.1,.000,1.9,3.2,.592,.569,1.5,2.2,.707,1.3,3.8,5.1,1.2,0.3,0.4,0.8,1.5,5.3
+400,Brandin Podziemski,SG,20,GSW,74,28,26.6,3.7,8.1,.454,1.2,3.2,.385,2.4,4.9,.499,.529,0.7,1.1,.633,1.6,4.2,5.8,3.7,0.8,0.2,1.2,1.6,9.2
+401,Jakob Poeltl,C,28,TOR,50,50,26.4,5.0,7.6,.656,0.0,0.0,,5.0,7.6,.656,.656,1.2,2.1,.551,2.9,5.7,8.6,2.5,0.7,1.5,1.5,3.0,11.1
+402,Aleksej Pokusevski,PF,22,TOT,28,0,14.5,1.8,4.3,.405,0.6,2.0,.327,1.1,2.4,.470,.479,1.1,1.5,.732,0.6,2.6,3.2,1.3,0.5,0.5,0.7,0.8,5.2
+402,Aleksej Pokusevski,PF,22,OKC,10,0,6.0,0.4,1.6,.250,0.2,1.1,.182,0.2,0.5,.400,.313,0.2,0.4,.500,0.2,0.8,1.0,0.5,0.1,0.1,0.3,0.5,1.2
+402,Aleksej Pokusevski,PF,22,CHO,18,0,19.2,2.5,5.8,.429,0.9,2.4,.364,1.6,3.4,.475,.505,1.6,2.1,.757,0.8,3.6,4.4,1.7,0.8,0.7,0.9,0.9,7.4
+403,Jordan Poole,SG,24,WAS,78,66,30.1,6.3,15.2,.413,2.4,7.2,.326,3.9,8.0,.492,.491,2.5,2.8,.877,0.4,2.3,2.7,4.4,1.1,0.3,2.4,3.1,17.4
+404,Craig Porter Jr.,PG,23,CLE,51,6,12.7,2.3,4.5,.509,0.2,0.7,.353,2.0,3.8,.536,.535,0.8,1.1,.732,0.4,1.7,2.1,2.3,0.4,0.3,0.9,0.8,5.6
+405,Jontay Porter,PF,24,TOR,26,5,13.8,1.5,4.0,.385,0.8,2.3,.333,0.8,1.7,.455,.481,0.6,0.7,.833,1.0,2.2,3.2,2.3,0.8,0.8,0.8,1.3,4.4
+406,Michael Porter Jr.,SF,25,DEN,81,81,31.7,6.4,13.3,.484,2.7,6.8,.397,3.7,6.5,.575,.586,1.1,1.4,.836,1.3,5.7,7.0,1.5,0.5,0.7,1.1,1.8,16.7
+407,Otto Porter Jr.,SF,30,TOR,15,1,11.6,0.9,2.2,.424,0.5,1.5,.348,0.4,0.7,.600,.545,0.2,0.2,1.000,0.4,1.5,1.9,0.5,0.3,0.3,0.1,1.3,2.6
+408,Bobby Portis,PF,28,MIL,82,4,24.5,5.7,11.2,.508,1.2,3.0,.407,4.5,8.2,.545,.562,1.2,1.5,.790,1.7,5.7,7.4,1.3,0.8,0.4,1.1,2.3,13.8
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+410,Micah Potter,PF,25,UTA,16,0,11.6,1.2,2.5,.475,0.6,1.3,.429,0.6,1.2,.526,.588,0.4,0.5,.750,0.6,2.1,2.7,0.4,0.3,0.4,0.3,0.9,3.3
+411,Dwight Powell,C,32,DAL,63,9,13.3,1.1,1.7,.679,0.0,0.0,.333,1.1,1.6,.689,.684,1.0,1.4,.708,1.5,1.9,3.4,1.3,0.4,0.3,0.5,1.8,3.3
+412,Norman Powell,SG,30,LAC,76,3,26.2,4.9,10.1,.486,2.2,5.1,.435,2.7,5.1,.536,.594,1.9,2.3,.831,0.2,2.4,2.6,1.1,0.6,0.3,0.9,1.8,13.9
+413,Jason Preston,PG,24,UTA,7,0,10.1,0.9,2.7,.316,0.0,0.4,.000,0.9,2.3,.375,.316,0.0,0.0,,0.9,1.6,2.4,2.3,0.3,0.1,0.3,0.4,1.7
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+415,Taurean Prince,PF,29,LAL,78,49,27.0,3.2,7.3,.442,1.8,4.6,.396,1.4,2.8,.519,.566,0.6,0.9,.735,0.3,2.7,2.9,1.5,0.7,0.4,0.9,1.9,8.9
+416,Payton Pritchard,PG,26,BOS,82,5,22.3,3.6,7.7,.468,1.8,4.7,.385,1.8,3.1,.593,.583,0.6,0.7,.821,0.9,2.4,3.2,3.4,0.5,0.1,0.7,1.3,9.6
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+418,Trevelin Queen,SG,26,ORL,14,0,11.8,1.0,2.7,.368,0.2,1.2,.176,0.8,1.5,.524,.408,0.6,0.9,.750,0.2,1.2,1.4,1.3,0.5,0.4,0.5,1.1,2.9
+419,Neemias Queta,C,24,BOS,28,0,11.9,2.4,3.7,.644,0.0,0.0,,2.4,3.7,.644,.644,0.7,1.0,.714,1.9,2.5,4.4,0.7,0.5,0.8,0.5,1.8,5.5
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+420,Immanuel Quickley,SG,24,NYK,30,0,24.0,5.1,11.2,.454,2.1,5.4,.395,2.9,5.8,.509,.549,2.7,3.1,.872,0.2,2.4,2.6,2.5,0.5,0.1,1.0,1.5,15.0
+420,Immanuel Quickley,PG,24,TOR,38,38,33.3,6.2,14.7,.422,2.8,7.1,.395,3.4,7.6,.448,.518,3.3,4.0,.841,0.3,4.5,4.8,6.8,0.9,0.2,1.8,2.0,18.6
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+425,Austin Reaves,SG,25,LAL,82,57,32.1,5.6,11.5,.486,1.9,5.1,.367,3.7,6.3,.583,.568,2.8,3.3,.853,0.7,3.6,4.3,5.5,0.8,0.3,2.1,1.9,15.9
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+427,Paul Reed,C,24,PHI,82,24,19.4,3.2,5.9,.540,0.3,0.7,.368,2.9,5.2,.563,.561,0.7,1.0,.718,2.4,3.6,6.0,1.3,0.8,1.0,0.8,2.4,7.3
+428,Naz Reid,C,24,MIN,81,14,24.2,5.0,10.5,.477,2.1,5.0,.414,2.9,5.5,.535,.576,1.3,1.8,.736,0.9,4.3,5.2,1.3,0.8,0.9,1.4,2.1,13.5
+429,Jared Rhoden,SG,24,DET,17,0,14.4,1.9,3.9,.500,0.7,1.8,.387,1.2,2.1,.600,.591,0.3,0.5,.625,0.2,1.8,1.9,0.8,0.2,0.8,0.4,1.6,4.9
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+431,Josh Richardson,SG,30,MIA,43,6,25.7,3.6,8.1,.444,1.5,4.4,.347,2.0,3.7,.561,.539,1.2,1.3,.944,0.4,2.4,2.8,2.4,0.6,0.3,0.8,1.8,9.9
+432,Duncan Robinson,SF,29,MIA,68,36,28.0,4.5,9.9,.450,2.8,7.0,.395,1.7,3.0,.579,.589,1.2,1.3,.889,0.2,2.3,2.5,2.8,0.7,0.2,1.4,2.4,12.9
+433,Jerome Robinson,SG,26,GSW,22,0,3.7,0.5,1.5,.333,0.1,0.8,.118,0.4,0.7,.563,.364,0.3,0.5,.636,0.0,0.2,0.3,0.2,0.0,0.1,0.1,0.2,1.4
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+436,Jeremiah Robinson-Earl,PF,23,NOP,39,1,8.6,1.2,2.4,.474,0.3,1.0,.333,0.8,1.4,.571,.542,0.3,0.4,.750,0.6,1.4,1.9,0.5,0.3,0.1,0.4,0.6,2.9
+437,David Roddy,PF,22,TOT,65,13,18.1,2.5,6.3,.403,0.7,2.5,.293,1.8,3.8,.478,.462,0.7,1.0,.691,0.8,2.4,3.2,1.2,0.4,0.2,0.9,1.1,6.5
+437,David Roddy,PF,22,MEM,48,13,23.2,3.2,8.0,.402,1.0,3.3,.301,2.3,4.8,.470,.462,1.0,1.4,.687,1.1,3.1,4.2,1.6,0.5,0.2,1.1,1.4,8.4
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+438,Ryan Rollins,PG,21,MIL,3,0,4.0,0.3,0.7,.500,0.3,0.3,1.000,0.0,0.3,.000,.750,0.0,0.0,,0.0,0.7,0.7,1.0,0.7,0.0,0.3,0.3,1.0
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+440,Terry Rozier,PG-SG,29,TOT,61,60,33.4,7.2,16.4,.443,2.4,6.7,.363,4.8,9.6,.498,.518,2.8,3.2,.869,0.6,3.5,4.0,5.6,1.0,0.3,1.7,1.7,19.8
+440,Terry Rozier,SG,29,CHO,30,30,35.5,8.4,18.3,.459,2.8,7.7,.358,5.6,10.6,.533,.535,3.6,4.3,.845,0.6,3.3,3.9,6.6,1.1,0.4,2.2,1.7,23.2
+440,Terry Rozier,PG,29,MIA,31,30,31.5,6.1,14.5,.423,2.1,5.7,.371,4.0,8.7,.458,.497,2.0,2.2,.913,0.6,3.6,4.2,4.6,1.0,0.3,1.3,1.6,16.4
+441,Rayan Rupert,SG,19,POR,39,12,16.2,1.4,4.1,.335,0.7,2.0,.359,0.7,2.1,.313,.422,0.5,0.6,.760,0.6,1.7,2.4,1.6,0.3,0.1,0.8,1.4,4.0
+442,D'Angelo Russell,PG,27,LAL,76,69,32.7,6.5,14.2,.456,3.0,7.2,.415,3.5,7.0,.497,.560,2.1,2.5,.828,0.4,2.7,3.1,6.3,0.9,0.5,2.1,2.1,18.0
+443,Matt Ryan,SF,26,NOP,28,1,13.9,1.8,4.0,.434,1.5,3.3,.451,0.3,0.8,.364,.615,0.5,0.5,.929,0.2,1.2,1.4,0.6,0.2,0.0,0.7,1.1,5.4
+444,Domantas Sabonis,C,27,SAC,82,82,35.7,7.7,13.0,.594,0.4,1.1,.379,7.3,12.0,.613,.609,3.6,5.1,.704,3.6,10.1,13.7,8.2,0.9,0.6,3.3,3.0,19.4
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+446,Jermaine Samuels,SF,25,HOU,14,0,4.3,0.6,1.0,.643,0.0,0.1,.000,0.6,0.9,.750,.643,0.1,0.1,1.000,0.2,0.7,0.9,0.2,0.1,0.1,0.0,0.4,1.4
+447,Adama Sanogo,PF,21,CHI,9,0,7.3,1.6,3.0,.519,0.0,0.0,,1.6,3.0,.519,.519,0.9,1.3,.667,2.1,1.9,4.0,0.0,0.1,0.0,0.6,0.6,4.0
+448,Gui Santos,PF,21,GSW,23,0,8.3,1.2,2.4,.509,0.4,1.2,.370,0.8,1.2,.643,.600,0.7,0.7,.941,0.7,1.4,2.1,0.6,0.2,0.1,0.3,1.0,3.6
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+451,Marcus Sasser,PG,23,DET,71,11,19.0,3.1,7.2,.428,1.3,3.4,.375,1.8,3.8,.474,.516,0.8,0.9,.879,0.2,1.6,1.8,3.3,0.6,0.2,1.3,1.5,8.3
+452,Admiral Schofield,PF,26,ORL,23,0,3.7,0.4,1.1,.385,0.3,0.7,.375,0.2,0.4,.400,.500,0.0,0.1,.000,0.1,0.6,0.7,0.3,0.0,0.0,0.2,0.4,1.1
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+453,Dennis Schröder,PG,30,TOR,51,33,30.6,4.8,11.0,.442,1.5,4.3,.350,3.3,6.6,.501,.511,2.5,2.9,.852,0.4,2.3,2.7,6.1,0.9,0.2,1.6,2.1,13.7
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+457,Collin Sexton,SG,25,UTA,78,51,26.6,6.5,13.3,.487,1.6,4.2,.394,4.8,9.1,.530,.549,4.1,4.7,.859,0.9,1.7,2.6,4.9,0.8,0.2,2.1,2.0,18.7
+458,Landry Shamet,SG,26,WAS,46,5,15.8,2.6,6.0,.431,1.2,3.4,.338,1.4,2.5,.556,.527,0.8,1.0,.826,0.3,1.0,1.3,1.2,0.5,0.2,0.7,1.7,7.1
+459,Day'Ron Sharpe,C,22,BRK,61,1,15.1,2.8,4.8,.571,0.1,0.2,.267,2.7,4.6,.588,.578,1.2,2.0,.610,2.6,3.7,6.4,1.4,0.4,0.7,1.1,2.1,6.8
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+461,Ben Sheppard,SG,22,IND,57,1,14.3,1.6,4.0,.393,0.9,2.7,.314,0.7,1.3,.562,.500,0.4,0.5,.885,0.3,1.3,1.6,0.9,0.6,0.0,0.3,1.5,4.4
+462,Pascal Siakam,PF,29,TOT,80,80,33.2,8.5,15.9,.536,1.1,3.1,.346,7.5,12.8,.581,.569,3.6,5.0,.732,1.7,5.3,7.1,4.3,0.8,0.3,1.8,2.4,21.7
+462,Pascal Siakam,PF,29,TOR,39,39,34.7,8.3,16.0,.522,1.2,3.7,.317,7.2,12.3,.584,.559,4.3,5.7,.758,1.4,4.9,6.3,4.9,0.8,0.3,2.1,2.2,22.2
+462,Pascal Siakam,PF,29,IND,41,41,31.8,8.7,15.8,.549,1.0,2.5,.386,7.7,13.3,.580,.579,3.0,4.2,.699,2.1,5.7,7.8,3.7,0.8,0.4,1.4,2.6,21.3
+463,Ben Simmons,PG,27,BRK,15,12,23.9,2.9,4.9,.581,0.0,0.0,,2.9,4.9,.581,.581,0.4,1.0,.400,2.1,5.9,7.9,5.7,0.8,0.6,1.8,2.4,6.1
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+467,Jericho Sims,C,25,NYK,45,11,13.0,0.8,1.2,.691,0.0,0.0,,0.8,1.2,.691,.691,0.4,0.5,.667,1.2,2.0,3.3,0.6,0.2,0.4,0.4,1.0,2.0
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+502,Xavier Tillman Sr.,C,25,MEM,34,13,20.6,2.6,6.3,.408,0.4,1.6,.226,2.2,4.7,.469,.437,0.5,1.3,.419,1.6,3.0,4.6,1.7,1.2,1.0,0.9,1.8,6.0
+502,Xavier Tillman Sr.,PF,25,BOS,20,2,13.7,1.7,3.3,.515,0.4,1.4,.286,1.3,1.9,.684,.576,0.2,0.4,.571,0.7,2.0,2.7,1.0,0.5,0.5,0.3,0.8,4.0
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+524,Ish Wainright,SF,29,POR,7,0,6.6,0.9,2.6,.333,0.9,2.3,.375,0.0,0.3,.000,.500,0.3,0.3,1.000,0.0,1.3,1.3,0.0,0.3,0.1,0.1,0.6,2.9
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+553,Kenrich Williams,PF,29,OKC,69,1,14.9,1.9,4.1,.468,0.8,2.0,.397,1.1,2.0,.540,.568,0.1,0.2,.500,0.9,2.1,3.0,1.3,0.6,0.1,0.5,1.5,4.7
+554,Malik Williams,C,25,TOR,7,2,15.3,1.3,4.9,.265,0.1,0.7,.200,1.1,4.1,.276,.279,0.0,0.3,.000,2.1,3.3,5.4,0.3,0.4,0.6,0.4,2.1,2.7
+555,Mark Williams,C,22,CHO,19,19,26.7,5.2,7.9,.649,0.0,0.0,,5.2,7.9,.649,.649,2.4,3.4,.719,4.0,5.7,9.7,1.2,0.8,1.1,0.9,2.6,12.7
+556,Patrick Williams,PF,22,CHI,43,30,27.3,3.7,8.3,.443,1.4,3.4,.399,2.3,4.9,.474,.525,1.2,1.5,.788,1.1,2.8,3.9,1.5,0.9,0.8,1.3,2.0,10.0
+557,Robert Williams,C,26,POR,6,0,19.8,2.8,4.3,.654,0.0,0.0,,2.8,4.3,.654,.654,1.2,1.5,.778,1.8,4.5,6.3,0.8,1.2,1.2,1.2,2.8,6.8
+558,Vince Williams Jr.,SG,23,MEM,52,33,27.6,3.3,7.3,.446,1.5,4.0,.378,1.7,3.3,.529,.550,2.0,2.5,.800,1.1,4.4,5.6,3.4,0.9,0.7,1.8,2.6,10.0
+559,Ziaire Williams,SF,22,MEM,51,15,20.4,2.9,7.4,.397,1.1,3.7,.307,1.8,3.7,.487,.474,1.2,1.5,.827,0.7,2.8,3.5,1.5,0.7,0.2,1.3,1.7,8.2
+560,Zion Williamson,PF,23,NOP,70,70,31.5,8.9,15.6,.570,0.1,0.3,.333,8.8,15.4,.574,.573,5.0,7.1,.702,1.7,4.1,5.8,5.0,1.1,0.7,2.8,2.3,22.9
+561,D.J. Wilson,PF,27,PHI,2,0,7.5,2.0,3.0,.667,1.0,1.0,1.000,1.0,2.0,.500,.833,0.0,0.0,,0.5,0.5,1.0,1.0,0.0,1.0,0.0,0.5,5.0
+562,Jalen Wilson,PF,23,BRK,43,3,15.4,1.8,4.2,.425,0.6,1.7,.324,1.2,2.4,.495,.492,0.9,1.1,.826,1.1,2.0,3.0,1.0,0.3,0.1,0.4,1.1,5.0
+563,Dylan Windler,SF,27,TOT,17,0,6.4,0.9,1.8,.500,0.8,1.6,.481,0.1,0.2,.667,.717,0.0,0.0,,0.2,0.8,0.9,0.6,0.1,0.0,0.1,0.4,2.5
+563,Dylan Windler,SF,27,NYK,3,0,2.3,0.3,0.7,.500,0.3,0.7,.500,0.0,0.0,,.750,0.0,0.0,,0.0,0.3,0.3,0.3,0.0,0.0,0.0,0.3,1.0
+563,Dylan Windler,SF,27,LAL,8,0,3.5,0.5,1.1,.444,0.5,1.0,.500,0.0,0.1,.000,.667,0.0,0.0,,0.0,0.4,0.4,0.8,0.0,0.0,0.0,0.3,1.5
+563,Dylan Windler,SF,27,ATL,6,0,12.2,1.7,3.2,.526,1.3,2.8,.471,0.3,0.3,1.000,.737,0.0,0.0,,0.5,1.5,2.0,0.5,0.3,0.0,0.3,0.5,4.7
+564,James Wiseman,C,22,DET,63,6,17.3,3.0,4.8,.613,0.0,0.0,.000,3.0,4.8,.617,.613,1.1,1.6,.706,1.7,3.6,5.3,0.9,0.2,0.6,1.0,2.2,7.1
+565,Isaiah Wong,SG,23,IND,1,0,4.0,1.0,3.0,.333,0.0,1.0,.000,1.0,2.0,.500,.333,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0
+566,Christian Wood,PF,28,LAL,50,1,17.4,2.5,5.3,.466,0.7,2.3,.307,1.8,3.0,.587,.532,1.3,1.9,.702,0.8,4.3,5.1,1.0,0.3,0.7,1.0,1.4,6.9
+567,Delon Wright,PG,31,TOT,47,1,15.8,1.6,4.0,.394,0.5,1.4,.368,1.0,2.6,.408,.460,0.8,1.0,.822,0.4,1.4,1.8,2.5,1.1,0.2,0.3,0.7,4.5
+567,Delon Wright,PG,31,WAS,33,0,13.8,1.5,3.7,.393,0.4,1.2,.368,1.0,2.5,.405,.451,0.7,0.9,.828,0.4,1.4,1.8,2.5,1.1,0.2,0.3,0.7,4.1
+567,Delon Wright,PG,31,MIA,14,1,20.4,1.9,4.7,.394,0.8,2.1,.367,1.1,2.6,.417,.477,0.9,1.1,.813,0.4,1.5,1.9,2.6,1.4,0.2,0.2,0.7,5.4
+568,Thaddeus Young,PF,35,TOT,33,6,13.3,2.0,3.3,.602,0.0,0.2,.143,1.9,3.1,.634,.606,0.2,0.5,.400,1.4,1.7,3.1,1.7,0.7,0.2,0.5,1.5,4.2
+568,Thaddeus Young,PF,35,TOR,23,6,15.2,2.3,3.8,.621,0.0,0.3,.167,2.3,3.5,.654,.626,0.2,0.5,.417,1.3,2.0,3.3,2.2,0.8,0.1,0.5,1.7,5.0
+568,Thaddeus Young,PF,35,PHO,10,0,8.9,1.1,2.1,.524,0.0,0.1,.000,1.1,2.0,.550,.524,0.1,0.3,.333,1.7,1.1,2.8,0.7,0.5,0.2,0.4,1.1,2.3
+569,Trae Young,PG,25,ATL,54,54,36.0,8.0,18.7,.430,3.2,8.7,.373,4.8,10.0,.479,.516,6.4,7.5,.855,0.4,2.3,2.8,10.8,1.3,0.2,4.4,2.0,25.7
+570,Omer Yurtseven,C,25,UTA,48,12,11.4,2.1,3.8,.538,0.1,0.5,.208,2.0,3.3,.588,.552,0.4,0.6,.679,1.5,2.8,4.3,0.6,0.2,0.4,0.8,1.1,4.6
+571,Cody Zeller,C,31,NOP,43,0,7.4,0.6,1.4,.419,0.0,0.1,.333,0.6,1.4,.424,.427,0.5,0.9,.605,1.1,1.5,2.6,0.9,0.2,0.1,0.4,1.0,1.8
+572,Ivica Zubac,C,26,LAC,68,68,26.4,5.0,7.6,.649,0.0,0.0,,5.0,7.6,.649,.649,1.8,2.4,.723,2.9,6.3,9.2,1.4,0.3,1.2,1.2,2.6,11.7
diff --git a/exercicios/para-sala/para_sala.ipynb b/exercicios/para-sala/para_sala.ipynb
new file mode 100644
index 0000000..22f7331
--- /dev/null
+++ b/exercicios/para-sala/para_sala.ipynb
@@ -0,0 +1,1047 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#ETL    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: matplotlib in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (3.9.2)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (1.2.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (4.53.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (1.4.5)\n",
+      "Requirement already satisfied: numpy>=1.23 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (2.1.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\colaborador\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (24.1)\n",
+      "Requirement already satisfied: pillow>=8 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (10.4.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (3.1.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\colaborador\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (2.9.0.post0)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\colaborador\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importar bibliotecas\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#leitura do nosso arquivo csv\n",
+    "df = pd.read_csv(\"titanic.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>IdPassageiro</th>\n",
+       "      <th>Sobreviveu</th>\n",
+       "      <th>Classe</th>\n",
+       "      <th>Nome</th>\n",
+       "      <th>Genero</th>\n",
+       "      <th>Idade</th>\n",
+       "      <th>Bilhete</th>\n",
+       "      <th>Tarifa</th>\n",
+       "      <th>Cabine</th>\n",
+       "      <th>Embarque</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   IdPassageiro  Sobreviveu  Classe  \\\n",
+       "0             1           0       3   \n",
+       "1             2           1       1   \n",
+       "2             3           1       3   \n",
+       "3             4           1       1   \n",
+       "4             5           0       3   \n",
+       "\n",
+       "                                                Nome  Genero  Idade  \\\n",
+       "0                            Braund, Mr. Owen Harris    male   22.0   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38.0   \n",
+       "2                             Heikkinen, Miss. Laina  female   26.0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35.0   \n",
+       "4                           Allen, Mr. William Henry    male   35.0   \n",
+       "\n",
+       "            Bilhete   Tarifa Cabine Embarque  \n",
+       "0         A/5 21171   7.2500    NaN        S  \n",
+       "1          PC 17599  71.2833    C85        C  \n",
+       "2  STON/O2. 3101282   7.9250    NaN        S  \n",
+       "3            113803  53.1000   C123        S  \n",
+       "4            373450   8.0500    NaN        S  "
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(891, 12)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#quantidades de linhas e colunas\n",
+    "df.shape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#backup\n",
+    "df_backup = df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PassengerId      0\n",
+      "Survived         0\n",
+      "Pclass           0\n",
+      "Name             0\n",
+      "Sex              0\n",
+      "Age            177\n",
+      "SibSp            0\n",
+      "Parch            0\n",
+      "Ticket           0\n",
+      "Fare             0\n",
+      "Cabin          687\n",
+      "Embarked         2\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#contar dados nulos em cada coluna \n",
+    "nulos_por_coluna = df.isnull().sum()\n",
+    "print(nulos_por_coluna)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0      1\n",
+      "1      0\n",
+      "2      1\n",
+      "3      0\n",
+      "4      1\n",
+      "      ..\n",
+      "886    1\n",
+      "887    0\n",
+      "888    2\n",
+      "889    0\n",
+      "890    1\n",
+      "Length: 891, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#contar dados nulos por linhas\n",
+    "nulos_por_linhas = df.isnull().sum(axis=1)\n",
+    "print(nulos_por_linhas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>714.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.383838</td>\n",
+       "      <td>2.308642</td>\n",
+       "      <td>29.699118</td>\n",
+       "      <td>0.523008</td>\n",
+       "      <td>0.381594</td>\n",
+       "      <td>32.204208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>257.353842</td>\n",
+       "      <td>0.486592</td>\n",
+       "      <td>0.836071</td>\n",
+       "      <td>14.526497</td>\n",
+       "      <td>1.102743</td>\n",
+       "      <td>0.806057</td>\n",
+       "      <td>49.693429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>223.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>20.125000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.910400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.454200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>668.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>512.329200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
+       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
+       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
+       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
+       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
+       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
+       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
+       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
+       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
+       "\n",
+       "            Parch        Fare  \n",
+       "count  891.000000  891.000000  \n",
+       "mean     0.381594   32.204208  \n",
+       "std      0.806057   49.693429  \n",
+       "min      0.000000    0.000000  \n",
+       "25%      0.000000    7.910400  \n",
+       "50%      0.000000   14.454200  \n",
+       "75%      0.000000   31.000000  \n",
+       "max      6.000000  512.329200  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#descrição de dados\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 891 entries, 0 to 890\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   PassengerId  891 non-null    int64  \n",
+      " 1   Survived     891 non-null    int64  \n",
+      " 2   Pclass       891 non-null    int64  \n",
+      " 3   Name         891 non-null    object \n",
+      " 4   Sex          891 non-null    object \n",
+      " 5   Age          714 non-null    float64\n",
+      " 6   SibSp        891 non-null    int64  \n",
+      " 7   Parch        891 non-null    int64  \n",
+      " 8   Ticket       891 non-null    object \n",
+      " 9   Fare         891 non-null    float64\n",
+      " 10  Cabin        204 non-null    object \n",
+      " 11  Embarked     889 non-null    object \n",
+      "dtypes: float64(2), int64(5), object(5)\n",
+      "memory usage: 83.7+ KB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "#verificar informações  \n",
+    "info_df =df.info()\n",
+    "print(info_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#remover linhas duplicadas\n",
+    "df = df.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#apagar linhas duplicadas da coluna \n",
+    "df_teste = df.drop_duplicates([\"PassengerId\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
+      "Index: []\n"
+     ]
+    }
+   ],
+   "source": [
+    "#função das linhas duplicadas \n",
+    "def visualizar_as_duplicadas(df):\n",
+    "    duplicados = df[df.duplicated(keep=False)]\n",
+    "    return duplicados\n",
+    "\n",
+    "linhas_duplicadas = visualizar_as_duplicadas(df)\n",
+    "print(linhas_duplicadas)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#apagar colunas do df\n",
+    "df = df.drop(columns=[\"SibSp\", \"Parch\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#apagar as informações NAN\n",
+    "df_teste = df.dropna(subset=[\"Cabin\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(204, 10)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_teste.shape  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#reset index\n",
+    "df = df.reset_index (drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'Ticket',\n",
+       "       'Fare', 'Cabin', 'Embarked'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# renomear as colunas\n",
+    "df.rename(columns={\n",
+    "    'PassengerId': 'IdPassageiro',\n",
+    "    'Survived': 'Sobreviveu', \n",
+    "    'Pclass': 'Classe',\n",
+    "    'Name': 'Nome',\n",
+    "    'Sex': 'Genero', \n",
+    "    'Age':'Idade', \n",
+    "    'Ticket':'Bilhete',\n",
+    "    'Fare': 'Tarifa',\n",
+    "    'Cabin': 'Cabine', \n",
+    "    'Embarked': 'Embarque'\n",
+    "}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#salve no csv\n",
+    "df.to_csv('titanic_tratado.csv', index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Análises"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Quantos passageiros estavam em cada classe do Titanic?\n",
+    "#contagem do número de passageiros\n",
+    "contagem_passeiros = df[\"Classe\"].value_counts()\n",
+    "#criação do grafico\n",
+    "contagem_passeiros.plot(kind=\"bar\", edgecolor=\"blue\", color=\"pink\")\n",
+    "#configurações\n",
+    "plt.xlabel(\"Classe do Passageiro\")\n",
+    "plt.ylabel(\"Quantidade\")\n",
+    "plt.title(\"Número de Passageiros por Classe\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Qual é taxa de sobreviventes por genero? \n",
+    "# agrupamento de genero por sobreviventes\n",
+    "taxa_sob_genero = df.groupby(\"Genero\")[\"Sobreviveu\"].mean()\n",
+    "#cores para barras\n",
+    "cores = [\"lightpink\",\"lightblue\"]\n",
+    "# plotagem\n",
+    "barras = taxa_sob_genero.plot.bar(edgecolor = \"blue\", color= cores)\n",
+    "# os rotulos\n",
+    "plt.xlabel(\"Genero\")\n",
+    "plt.ylabel(\"Taxa Sobreviventes\")\n",
+    "plt.title(\"Taxa de Sobreviventes por Genero\")\n",
+    "#adicionar legendas\n",
+    "plt.legend([\"Taxa de Sobrevivencia\"])\n",
+    "#adicionar rotulos nos graficos\n",
+    "for i, v in enumerate(taxa_sob_genero):\n",
+    "    barras.text(i, v + 0.01, f'{v:.2f}', color = 'black', ha = 'center')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Qual é a distribuição de idades?\n",
+    "#plotagem\n",
+    "df[\"Idade\"].plot.hist(bins=5, edgecolor= \"blue\", color=\"pink\")\n",
+    "\n",
+    "plt.xlabel(\"Idade\")\n",
+    "plt.ylabel(\"Quantidade\")\n",
+    "plt.title(\"Distribuição de Idade\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting seaborn\n",
+      "  Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n",
+      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from seaborn) (2.1.0)\n",
+      "Requirement already satisfied: pandas>=1.2 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from seaborn) (2.2.2)\n",
+      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from seaborn) (3.9.2)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.53.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\colaborador\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.1)\n",
+      "Requirement already satisfied: pillow>=8 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.4.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\colaborador\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\colaborador\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\colaborador\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
+      "Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
+      "Installing collected packages: seaborn\n",
+      "Successfully installed seaborn-0.13.2\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import ttest_ind\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test T de idade\n",
+      "Estatistica T : -2.06668694625381\n",
+      "Valor P: 0.03912465401348249\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rejeitamos a hipotese nula\n"
+     ]
+    }
+   ],
+   "source": [
+    "#amostras\n",
+    "\n",
+    "idade_sobreviventes = df[df['Sobreviveu']==1]['Idade'].dropna()\n",
+    "idade_nao_sobreviveu = df[df['Sobreviveu']==0]['Idade'].dropna()\n",
+    "\n",
+    "#teste t \n",
+    "\n",
+    "estatistica_t, valor_p = ttest_ind(idade_sobreviventes, idade_nao_sobreviveu)\n",
+    "\n",
+    "print(\"Test T de idade\")\n",
+    "print(f\"Estatistica T : {estatistica_t}\")\n",
+    "print(f\"Valor P: {valor_p}\")\n",
+    "\n",
+    "#grafico\n",
+    "\n",
+    "sns.histplot(idade_sobreviventes,color= 'blue', label='Sobreviventes',bins=20)\n",
+    "sns.histplot(idade_nao_sobreviveu,color= 'red', label='Não Sobreviventes',bins=20)\n",
+    "\n",
+    "#Rotulo\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.title(\"Distribuição de Idade dos sobreviventes\")\n",
+    "plt.xlabel(\"Idade\")\n",
+    "plt.ylabel (\"Contagem\")\n",
+    "plt.show()\n",
+    "\n",
+    "#interpretação \n",
+    "\n",
+    "if valor_p < 0.05:\n",
+    "    print(\"Rejeitamos a hipotese nula\")\n",
+    "else:\n",
+    "    print(\"Não rejeitamos a hipotese nula\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#amostra\n",
+    "\n",
+    "baby_df = df.sample(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>IdPassageiro</th>\n",
+       "      <th>Sobreviveu</th>\n",
+       "      <th>Classe</th>\n",
+       "      <th>Nome</th>\n",
+       "      <th>Genero</th>\n",
+       "      <th>Idade</th>\n",
+       "      <th>Bilhete</th>\n",
+       "      <th>Tarifa</th>\n",
+       "      <th>Cabine</th>\n",
+       "      <th>Embarque</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>481</th>\n",
+       "      <td>482</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Frost, Mr. Anthony Wood \"Archie\"</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>239854</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>230</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Lefebre, Miss. Mathilde</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4133</td>\n",
+       "      <td>25.4667</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>56</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Woolner, Mr. Hugh</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19947</td>\n",
+       "      <td>35.5000</td>\n",
+       "      <td>C52</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     IdPassageiro  Sobreviveu  Classe                              Nome  \\\n",
+       "481           482           0       2  Frost, Mr. Anthony Wood \"Archie\"   \n",
+       "229           230           0       3           Lefebre, Miss. Mathilde   \n",
+       "55             56           1       1                 Woolner, Mr. Hugh   \n",
+       "\n",
+       "     Genero  Idade Bilhete   Tarifa Cabine Embarque  \n",
+       "481    male    NaN  239854   0.0000    NaN        S  \n",
+       "229  female    NaN    4133  25.4667    NaN        S  \n",
+       "55     male    NaN   19947  35.5000    C52        S  "
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "baby_df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sqlite3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Genero  Contagem\n",
+      "0  female        41\n",
+      "1    male        59\n"
+     ]
+    }
+   ],
+   "source": [
+    "#conexão\n",
+    "conn = sqlite3.connect(':memory:')\n",
+    "\n",
+    "#escrever o df em uma tabela SQL\n",
+    "baby_df.to_sql('baby_df',conn, index = False, if_exists='replace')\n",
+    "\n",
+    "#executar a consulta\n",
+    "\n",
+    "query_sql = \"\"\"\n",
+    "SELECT Genero, COUNT(IdPassageiro) AS Contagem\n",
+    "FROM baby_df\n",
+    "GROUP BY Genero;\n",
+    "\"\"\"\n",
+    "\n",
+    "contagem_por_gen =pd.read_sql_query(query_sql,conn)\n",
+    "print (contagem_por_gen)\n",
+    "#fechar conexão\n",
+    "conn.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Genero\n",
+      "male      59\n",
+      "female    41\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#pandas \n",
+    "contagem_por_genero = baby_df ['Genero'].value_counts()\n",
+    "contagem_por_genero.columns = ['Genero', 'contagem']\n",
+    "print(contagem_por_genero)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-sala/titanic_tratado.csv b/exercicios/para-sala/titanic_tratado.csv
new file mode 100644
index 0000000..3a992a0
--- /dev/null
+++ b/exercicios/para-sala/titanic_tratado.csv
@@ -0,0 +1,892 @@
+IdPassageiro,Sobreviveu,Classe,Nome,Genero,Idade,Bilhete,Tarifa,Cabine,Embarque
+1,0,3,"Braund, Mr. Owen Harris",male,22.0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38.0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26.0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35.0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35.0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54.0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2.0,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27.0,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14.0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4.0,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58.0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20.0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39.0,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14.0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55.0,248706,16.0,,S
+17,0,3,"Rice, Master. Eugene",male,2.0,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,244373,13.0,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31.0,345763,18.0,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",male,35.0,239865,26.0,,S
+22,1,2,"Beesley, Mr. Lawrence",male,34.0,248698,13.0,D56,S
+23,1,3,"McGowan, Miss. Anna ""Annie""",female,15.0,330923,8.0292,,Q
+24,1,1,"Sloper, Mr. William Thompson",male,28.0,113788,35.5,A6,S
+25,0,3,"Palsson, Miss. Torborg Danira",female,8.0,349909,21.075,,S
+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38.0,347077,31.3875,,S
+27,0,3,"Emir, Mr. Farred Chehab",male,,2631,7.225,,C
+28,0,1,"Fortune, Mr. Charles Alexander",male,19.0,19950,263.0,C23 C25 C27,S
+29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,330959,7.8792,,Q
+30,0,3,"Todoroff, Mr. Lalio",male,,349216,7.8958,,S
+31,0,1,"Uruchurtu, Don. Manuel E",male,40.0,PC 17601,27.7208,,C
+32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,PC 17569,146.5208,B78,C
+33,1,3,"Glynn, Miss. Mary Agatha",female,,335677,7.75,,Q
+34,0,2,"Wheadon, Mr. Edward H",male,66.0,C.A. 24579,10.5,,S
+35,0,1,"Meyer, Mr. Edgar Joseph",male,28.0,PC 17604,82.1708,,C
+36,0,1,"Holverson, Mr. Alexander Oskar",male,42.0,113789,52.0,,S
+37,1,3,"Mamee, Mr. Hanna",male,,2677,7.2292,,C
+38,0,3,"Cann, Mr. Ernest Charles",male,21.0,A./5. 2152,8.05,,S
+39,0,3,"Vander Planke, Miss. Augusta Maria",female,18.0,345764,18.0,,S
+40,1,3,"Nicola-Yarred, Miss. Jamila",female,14.0,2651,11.2417,,C
+41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40.0,7546,9.475,,S
+42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27.0,11668,21.0,,S
+43,0,3,"Kraeff, Mr. Theodor",male,,349253,7.8958,,C
+44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3.0,SC/Paris 2123,41.5792,,C
+45,1,3,"Devaney, Miss. Margaret Delia",female,19.0,330958,7.8792,,Q
+46,0,3,"Rogers, Mr. William John",male,,S.C./A.4. 23567,8.05,,S
+47,0,3,"Lennon, Mr. Denis",male,,370371,15.5,,Q
+48,1,3,"O'Driscoll, Miss. Bridget",female,,14311,7.75,,Q
+49,0,3,"Samaan, Mr. Youssef",male,,2662,21.6792,,C
+50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18.0,349237,17.8,,S
+51,0,3,"Panula, Master. Juha Niilo",male,7.0,3101295,39.6875,,S
+52,0,3,"Nosworthy, Mr. Richard Cater",male,21.0,A/4. 39886,7.8,,S
+53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49.0,PC 17572,76.7292,D33,C
+54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29.0,2926,26.0,,S
+55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65.0,113509,61.9792,B30,C
+56,1,1,"Woolner, Mr. Hugh",male,,19947,35.5,C52,S
+57,1,2,"Rugg, Miss. Emily",female,21.0,C.A. 31026,10.5,,S
+58,0,3,"Novel, Mr. Mansouer",male,28.5,2697,7.2292,,C
+59,1,2,"West, Miss. Constance Mirium",female,5.0,C.A. 34651,27.75,,S
+60,0,3,"Goodwin, Master. William Frederick",male,11.0,CA 2144,46.9,,S
+61,0,3,"Sirayanian, Mr. Orsen",male,22.0,2669,7.2292,,C
+62,1,1,"Icard, Miss. Amelie",female,38.0,113572,80.0,B28,
+63,0,1,"Harris, Mr. Henry Birkhardt",male,45.0,36973,83.475,C83,S
+64,0,3,"Skoog, Master. Harald",male,4.0,347088,27.9,,S
+65,0,1,"Stewart, Mr. Albert A",male,,PC 17605,27.7208,,C
+66,1,3,"Moubarek, Master. Gerios",male,,2661,15.2458,,C
+67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29.0,C.A. 29395,10.5,F33,S
+68,0,3,"Crease, Mr. Ernest James",male,19.0,S.P. 3464,8.1583,,S
+69,1,3,"Andersson, Miss. Erna Alexandra",female,17.0,3101281,7.925,,S
+70,0,3,"Kink, Mr. Vincenz",male,26.0,315151,8.6625,,S
+71,0,2,"Jenkin, Mr. Stephen Curnow",male,32.0,C.A. 33111,10.5,,S
+72,0,3,"Goodwin, Miss. Lillian Amy",female,16.0,CA 2144,46.9,,S
+73,0,2,"Hood, Mr. Ambrose Jr",male,21.0,S.O.C. 14879,73.5,,S
+74,0,3,"Chronopoulos, Mr. Apostolos",male,26.0,2680,14.4542,,C
+75,1,3,"Bing, Mr. Lee",male,32.0,1601,56.4958,,S
+76,0,3,"Moen, Mr. Sigurd Hansen",male,25.0,348123,7.65,F G73,S
+77,0,3,"Staneff, Mr. Ivan",male,,349208,7.8958,,S
+78,0,3,"Moutal, Mr. Rahamin Haim",male,,374746,8.05,,S
+79,1,2,"Caldwell, Master. Alden Gates",male,0.83,248738,29.0,,S
+80,1,3,"Dowdell, Miss. Elizabeth",female,30.0,364516,12.475,,S
+81,0,3,"Waelens, Mr. Achille",male,22.0,345767,9.0,,S
+82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29.0,345779,9.5,,S
+83,1,3,"McDermott, Miss. Brigdet Delia",female,,330932,7.7875,,Q
+84,0,1,"Carrau, Mr. Francisco M",male,28.0,113059,47.1,,S
+85,1,2,"Ilett, Miss. Bertha",female,17.0,SO/C 14885,10.5,,S
+86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33.0,3101278,15.85,,S
+87,0,3,"Ford, Mr. William Neal",male,16.0,W./C. 6608,34.375,,S
+88,0,3,"Slocovski, Mr. Selman Francis",male,,SOTON/OQ 392086,8.05,,S
+89,1,1,"Fortune, Miss. Mabel Helen",female,23.0,19950,263.0,C23 C25 C27,S
+90,0,3,"Celotti, Mr. Francesco",male,24.0,343275,8.05,,S
+91,0,3,"Christmann, Mr. Emil",male,29.0,343276,8.05,,S
+92,0,3,"Andreasson, Mr. Paul Edvin",male,20.0,347466,7.8542,,S
+93,0,1,"Chaffee, Mr. Herbert Fuller",male,46.0,W.E.P. 5734,61.175,E31,S
+94,0,3,"Dean, Mr. Bertram Frank",male,26.0,C.A. 2315,20.575,,S
+95,0,3,"Coxon, Mr. Daniel",male,59.0,364500,7.25,,S
+96,0,3,"Shorney, Mr. Charles Joseph",male,,374910,8.05,,S
+97,0,1,"Goldschmidt, Mr. George B",male,71.0,PC 17754,34.6542,A5,C
+98,1,1,"Greenfield, Mr. William Bertram",male,23.0,PC 17759,63.3583,D10 D12,C
+99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34.0,231919,23.0,,S
+100,0,2,"Kantor, Mr. Sinai",male,34.0,244367,26.0,,S
+101,0,3,"Petranec, Miss. Matilda",female,28.0,349245,7.8958,,S
+102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,349215,7.8958,,S
+103,0,1,"White, Mr. Richard Frasar",male,21.0,35281,77.2875,D26,S
+104,0,3,"Johansson, Mr. Gustaf Joel",male,33.0,7540,8.6542,,S
+105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37.0,3101276,7.925,,S
+106,0,3,"Mionoff, Mr. Stoytcho",male,28.0,349207,7.8958,,S
+107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21.0,343120,7.65,,S
+108,1,3,"Moss, Mr. Albert Johan",male,,312991,7.775,,S
+109,0,3,"Rekic, Mr. Tido",male,38.0,349249,7.8958,,S
+110,1,3,"Moran, Miss. Bertha",female,,371110,24.15,,Q
+111,0,1,"Porter, Mr. Walter Chamberlain",male,47.0,110465,52.0,C110,S
+112,0,3,"Zabour, Miss. Hileni",female,14.5,2665,14.4542,,C
+113,0,3,"Barton, Mr. David John",male,22.0,324669,8.05,,S
+114,0,3,"Jussila, Miss. Katriina",female,20.0,4136,9.825,,S
+115,0,3,"Attalah, Miss. Malake",female,17.0,2627,14.4583,,C
+116,0,3,"Pekoniemi, Mr. Edvard",male,21.0,STON/O 2. 3101294,7.925,,S
+117,0,3,"Connors, Mr. Patrick",male,70.5,370369,7.75,,Q
+118,0,2,"Turpin, Mr. William John Robert",male,29.0,11668,21.0,,S
+119,0,1,"Baxter, Mr. Quigg Edmond",male,24.0,PC 17558,247.5208,B58 B60,C
+120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2.0,347082,31.275,,S
+121,0,2,"Hickman, Mr. Stanley George",male,21.0,S.O.C. 14879,73.5,,S
+122,0,3,"Moore, Mr. Leonard Charles",male,,A4. 54510,8.05,,S
+123,0,2,"Nasser, Mr. Nicholas",male,32.5,237736,30.0708,,C
+124,1,2,"Webber, Miss. Susan",female,32.5,27267,13.0,E101,S
+125,0,1,"White, Mr. Percival Wayland",male,54.0,35281,77.2875,D26,S
+126,1,3,"Nicola-Yarred, Master. Elias",male,12.0,2651,11.2417,,C
+127,0,3,"McMahon, Mr. Martin",male,,370372,7.75,,Q
+128,1,3,"Madsen, Mr. Fridtjof Arne",male,24.0,C 17369,7.1417,,S
+129,1,3,"Peter, Miss. Anna",female,,2668,22.3583,F E69,C
+130,0,3,"Ekstrom, Mr. Johan",male,45.0,347061,6.975,,S
+131,0,3,"Drazenoic, Mr. Jozef",male,33.0,349241,7.8958,,C
+132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20.0,SOTON/O.Q. 3101307,7.05,,S
+133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47.0,A/5. 3337,14.5,,S
+134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29.0,228414,26.0,,S
+135,0,2,"Sobey, Mr. Samuel James Hayden",male,25.0,C.A. 29178,13.0,,S
+136,0,2,"Richard, Mr. Emile",male,23.0,SC/PARIS 2133,15.0458,,C
+137,1,1,"Newsom, Miss. Helen Monypeny",female,19.0,11752,26.2833,D47,S
+138,0,1,"Futrelle, Mr. Jacques Heath",male,37.0,113803,53.1,C123,S
+139,0,3,"Osen, Mr. Olaf Elon",male,16.0,7534,9.2167,,S
+140,0,1,"Giglio, Mr. Victor",male,24.0,PC 17593,79.2,B86,C
+141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,2678,15.2458,,C
+142,1,3,"Nysten, Miss. Anna Sofia",female,22.0,347081,7.75,,S
+143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24.0,STON/O2. 3101279,15.85,,S
+144,0,3,"Burke, Mr. Jeremiah",male,19.0,365222,6.75,,Q
+145,0,2,"Andrew, Mr. Edgardo Samuel",male,18.0,231945,11.5,,S
+146,0,2,"Nicholls, Mr. Joseph Charles",male,19.0,C.A. 33112,36.75,,S
+147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27.0,350043,7.7958,,S
+148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9.0,W./C. 6608,34.375,,S
+149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,230080,26.0,F2,S
+150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42.0,244310,13.0,,S
+151,0,2,"Bateman, Rev. Robert James",male,51.0,S.O.P. 1166,12.525,,S
+152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22.0,113776,66.6,C2,S
+153,0,3,"Meo, Mr. Alfonzo",male,55.5,A.5. 11206,8.05,,S
+154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,A/5. 851,14.5,,S
+155,0,3,"Olsen, Mr. Ole Martin",male,,Fa 265302,7.3125,,S
+156,0,1,"Williams, Mr. Charles Duane",male,51.0,PC 17597,61.3792,,C
+157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16.0,35851,7.7333,,Q
+158,0,3,"Corn, Mr. Harry",male,30.0,SOTON/OQ 392090,8.05,,S
+159,0,3,"Smiljanic, Mr. Mile",male,,315037,8.6625,,S
+160,0,3,"Sage, Master. Thomas Henry",male,,CA. 2343,69.55,,S
+161,0,3,"Cribb, Mr. John Hatfield",male,44.0,371362,16.1,,S
+162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40.0,C.A. 33595,15.75,,S
+163,0,3,"Bengtsson, Mr. John Viktor",male,26.0,347068,7.775,,S
+164,0,3,"Calic, Mr. Jovo",male,17.0,315093,8.6625,,S
+165,0,3,"Panula, Master. Eino Viljami",male,1.0,3101295,39.6875,,S
+166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9.0,363291,20.525,,S
+167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,113505,55.0,E33,S
+168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45.0,347088,27.9,,S
+169,0,1,"Baumann, Mr. John D",male,,PC 17318,25.925,,S
+170,0,3,"Ling, Mr. Lee",male,28.0,1601,56.4958,,S
+171,0,1,"Van der hoef, Mr. Wyckoff",male,61.0,111240,33.5,B19,S
+172,0,3,"Rice, Master. Arthur",male,4.0,382652,29.125,,Q
+173,1,3,"Johnson, Miss. Eleanor Ileen",female,1.0,347742,11.1333,,S
+174,0,3,"Sivola, Mr. Antti Wilhelm",male,21.0,STON/O 2. 3101280,7.925,,S
+175,0,1,"Smith, Mr. James Clinch",male,56.0,17764,30.6958,A7,C
+176,0,3,"Klasen, Mr. Klas Albin",male,18.0,350404,7.8542,,S
+177,0,3,"Lefebre, Master. Henry Forbes",male,,4133,25.4667,,S
+178,0,1,"Isham, Miss. Ann Elizabeth",female,50.0,PC 17595,28.7125,C49,C
+179,0,2,"Hale, Mr. Reginald",male,30.0,250653,13.0,,S
+180,0,3,"Leonard, Mr. Lionel",male,36.0,LINE,0.0,,S
+181,0,3,"Sage, Miss. Constance Gladys",female,,CA. 2343,69.55,,S
+182,0,2,"Pernot, Mr. Rene",male,,SC/PARIS 2131,15.05,,C
+183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9.0,347077,31.3875,,S
+184,1,2,"Becker, Master. Richard F",male,1.0,230136,39.0,F4,S
+185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4.0,315153,22.025,,S
+186,0,1,"Rood, Mr. Hugh Roscoe",male,,113767,50.0,A32,S
+187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,370365,15.5,,Q
+188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45.0,111428,26.55,,S
+189,0,3,"Bourke, Mr. John",male,40.0,364849,15.5,,Q
+190,0,3,"Turcin, Mr. Stjepan",male,36.0,349247,7.8958,,S
+191,1,2,"Pinsky, Mrs. (Rosa)",female,32.0,234604,13.0,,S
+192,0,2,"Carbines, Mr. William",male,19.0,28424,13.0,,S
+193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19.0,350046,7.8542,,S
+194,1,2,"Navratil, Master. Michel M",male,3.0,230080,26.0,F2,S
+195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44.0,PC 17610,27.7208,B4,C
+196,1,1,"Lurette, Miss. Elise",female,58.0,PC 17569,146.5208,B80,C
+197,0,3,"Mernagh, Mr. Robert",male,,368703,7.75,,Q
+198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42.0,4579,8.4042,,S
+199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,370370,7.75,,Q
+200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24.0,248747,13.0,,S
+201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28.0,345770,9.5,,S
+202,0,3,"Sage, Mr. Frederick",male,,CA. 2343,69.55,,S
+203,0,3,"Johanson, Mr. Jakob Alfred",male,34.0,3101264,6.4958,,S
+204,0,3,"Youseff, Mr. Gerious",male,45.5,2628,7.225,,C
+205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18.0,A/5 3540,8.05,,S
+206,0,3,"Strom, Miss. Telma Matilda",female,2.0,347054,10.4625,G6,S
+207,0,3,"Backstrom, Mr. Karl Alfred",male,32.0,3101278,15.85,,S
+208,1,3,"Albimona, Mr. Nassef Cassem",male,26.0,2699,18.7875,,C
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