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rohanjoseph93/NBA_goat.ipynb

Created June 5, 2018 02:02
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NBA_goat.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"#import libraries\n",
"import pandas as pd\n",
"import os\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [],
"source": [
"#Import playoff stats file\n",
"os.chdir('C:\\\\Users\\\\rohan\\\\Documents\\\\Analytics\\\\Data')\n",
"nba = pd.read_csv('nba_goat.csv')"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" 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>Player</th>\n",
" <th>SEASON</th>\n",
" <th>TEAM</th>\n",
" <th>GP</th>\n",
" <th>MIN</th>\n",
" <th>PTS</th>\n",
" <th>FGM</th>\n",
" <th>FGA</th>\n",
" <th>FG_PERC</th>\n",
" <th>3PM</th>\n",
" <th>...</th>\n",
" <th>FTA</th>\n",
" <th>FT_PERC</th>\n",
" <th>OREB</th>\n",
" <th>DREB</th>\n",
" <th>REB</th>\n",
" <th>AST</th>\n",
" <th>TOV</th>\n",
" <th>STL</th>\n",
" <th>BLK</th>\n",
" <th>PF</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Jordan</td>\n",
" <td>1998</td>\n",
" <td>CHI</td>\n",
" <td>21</td>\n",
" <td>872</td>\n",
" <td>680</td>\n",
" <td>243</td>\n",
" <td>526</td>\n",
" <td>46.2</td>\n",
" <td>13</td>\n",
" <td>...</td>\n",
" <td>223</td>\n",
" <td>81.2</td>\n",
" <td>33</td>\n",
" <td>74</td>\n",
" <td>107</td>\n",
" <td>74</td>\n",
" <td>45</td>\n",
" <td>32</td>\n",
" <td>12</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Jordan</td>\n",
" <td>1997</td>\n",
" <td>CHI</td>\n",
" <td>19</td>\n",
" <td>804</td>\n",
" <td>590</td>\n",
" <td>227</td>\n",
" <td>498</td>\n",
" <td>45.6</td>\n",
" <td>13</td>\n",
" <td>...</td>\n",
" <td>148</td>\n",
" <td>83.1</td>\n",
" <td>42</td>\n",
" <td>108</td>\n",
" <td>150</td>\n",
" <td>91</td>\n",
" <td>49</td>\n",
" <td>30</td>\n",
" <td>17</td>\n",
" <td>46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Jordan</td>\n",
" <td>1996</td>\n",
" <td>CHI</td>\n",
" <td>18</td>\n",
" <td>733</td>\n",
" <td>552</td>\n",
" <td>187</td>\n",
" <td>407</td>\n",
" <td>45.9</td>\n",
" <td>25</td>\n",
" <td>...</td>\n",
" <td>187</td>\n",
" <td>81.8</td>\n",
" <td>31</td>\n",
" <td>58</td>\n",
" <td>89</td>\n",
" <td>74</td>\n",
" <td>42</td>\n",
" <td>33</td>\n",
" <td>6</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Jordan</td>\n",
" <td>1995</td>\n",
" <td>CHI</td>\n",
" <td>10</td>\n",
" <td>420</td>\n",
" <td>315</td>\n",
" <td>120</td>\n",
" <td>248</td>\n",
" <td>48.4</td>\n",
" <td>11</td>\n",
" <td>...</td>\n",
" <td>79</td>\n",
" <td>81.0</td>\n",
" <td>20</td>\n",
" <td>45</td>\n",
" <td>65</td>\n",
" <td>45</td>\n",
" <td>41</td>\n",
" <td>23</td>\n",
" <td>14</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Jordan</td>\n",
" <td>1993</td>\n",
" <td>CHI</td>\n",
" <td>19</td>\n",
" <td>783</td>\n",
" <td>666</td>\n",
" <td>251</td>\n",
" <td>528</td>\n",
" <td>47.5</td>\n",
" <td>28</td>\n",
" <td>...</td>\n",
" <td>169</td>\n",
" <td>80.5</td>\n",
" <td>32</td>\n",
" <td>96</td>\n",
" <td>128</td>\n",
" <td>114</td>\n",
" <td>45</td>\n",
" <td>39</td>\n",
" <td>17</td>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Jordan</td>\n",
" <td>1992</td>\n",
" <td>CHI</td>\n",
" <td>22</td>\n",
" <td>920</td>\n",
" <td>759</td>\n",
" <td>290</td>\n",
" <td>581</td>\n",
" <td>49.9</td>\n",
" <td>17</td>\n",
" <td>...</td>\n",
" <td>189</td>\n",
" <td>85.7</td>\n",
" <td>37</td>\n",
" <td>100</td>\n",
" <td>137</td>\n",
" <td>127</td>\n",
" <td>81</td>\n",
" <td>44</td>\n",
" <td>16</td>\n",
" <td>62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Jordan</td>\n",
" <td>1991</td>\n",
" <td>CHI</td>\n",
" <td>17</td>\n",
" <td>689</td>\n",
" <td>529</td>\n",
" <td>197</td>\n",
" <td>376</td>\n",
" <td>52.4</td>\n",
" <td>10</td>\n",
" <td>...</td>\n",
" <td>148</td>\n",
" <td>84.5</td>\n",
" <td>18</td>\n",
" <td>90</td>\n",
" <td>108</td>\n",
" <td>142</td>\n",
" <td>43</td>\n",
" <td>40</td>\n",
" <td>23</td>\n",
" <td>53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Jordan</td>\n",
" <td>1990</td>\n",
" <td>CHI</td>\n",
" <td>16</td>\n",
" <td>674</td>\n",
" <td>587</td>\n",
" <td>219</td>\n",
" <td>426</td>\n",
" <td>51.4</td>\n",
" <td>16</td>\n",
" <td>...</td>\n",
" <td>159</td>\n",
" <td>83.6</td>\n",
" <td>24</td>\n",
" <td>91</td>\n",
" <td>115</td>\n",
" <td>109</td>\n",
" <td>56</td>\n",
" <td>45</td>\n",
" <td>14</td>\n",
" <td>54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Jordan</td>\n",
" <td>1989</td>\n",
" <td>CHI</td>\n",
" <td>17</td>\n",
" <td>718</td>\n",
" <td>591</td>\n",
" <td>199</td>\n",
" <td>390</td>\n",
" <td>51.0</td>\n",
" <td>10</td>\n",
" <td>...</td>\n",
" <td>229</td>\n",
" <td>79.9</td>\n",
" <td>26</td>\n",
" <td>93</td>\n",
" <td>119</td>\n",
" <td>130</td>\n",
" <td>68</td>\n",
" <td>42</td>\n",
" <td>13</td>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Jordan</td>\n",
" <td>1988</td>\n",
" <td>CHI</td>\n",
" <td>10</td>\n",
" <td>427</td>\n",
" <td>363</td>\n",
" <td>138</td>\n",
" <td>260</td>\n",
" <td>53.1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>99</td>\n",
" <td>86.9</td>\n",
" <td>23</td>\n",
" <td>48</td>\n",
" <td>71</td>\n",
" <td>47</td>\n",
" <td>39</td>\n",
" <td>24</td>\n",
" <td>11</td>\n",
" <td>38</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" Player SEASON TEAM GP MIN PTS FGM FGA FG_PERC 3PM ... FTA \\\n",
"0 Jordan 1998 CHI 21 872 680 243 526 46.2 13 ... 223 \n",
"1 Jordan 1997 CHI 19 804 590 227 498 45.6 13 ... 148 \n",
"2 Jordan 1996 CHI 18 733 552 187 407 45.9 25 ... 187 \n",
"3 Jordan 1995 CHI 10 420 315 120 248 48.4 11 ... 79 \n",
"4 Jordan 1993 CHI 19 783 666 251 528 47.5 28 ... 169 \n",
"5 Jordan 1992 CHI 22 920 759 290 581 49.9 17 ... 189 \n",
"6 Jordan 1991 CHI 17 689 529 197 376 52.4 10 ... 148 \n",
"7 Jordan 1990 CHI 16 674 587 219 426 51.4 16 ... 159 \n",
"8 Jordan 1989 CHI 17 718 591 199 390 51.0 10 ... 229 \n",
"9 Jordan 1988 CHI 10 427 363 138 260 53.1 1 ... 99 \n",
"\n",
" FT_PERC OREB DREB REB AST TOV STL BLK PF \n",
"0 81.2 33 74 107 74 45 32 12 47 \n",
"1 83.1 42 108 150 91 49 30 17 46 \n",
"2 81.8 31 58 89 74 42 33 6 49 \n",
"3 81.0 20 45 65 45 41 23 14 30 \n",
"4 80.5 32 96 128 114 45 39 17 58 \n",
"5 85.7 37 100 137 127 81 44 16 62 \n",
"6 84.5 18 90 108 142 43 40 23 53 \n",
"7 83.6 24 91 115 109 56 45 14 54 \n",
"8 79.9 26 93 119 130 68 42 13 65 \n",
"9 86.9 23 48 71 47 39 24 11 38 \n",
"\n",
"[10 rows x 23 columns]"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#View top 10 rows\n",
"nba.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Player',\n",
" 'SEASON',\n",
" 'TEAM',\n",
" 'GP',\n",
" 'MIN',\n",
" 'PTS',\n",
" 'FGM',\n",
" 'FGA',\n",
" 'FG_PERC',\n",
" '3PM',\n",
" '3PA',\n",
" '3P_PERC',\n",
" 'FTM',\n",
" 'FTA',\n",
" 'FT_PERC',\n",
" 'OREB',\n",
" 'DREB',\n",
" 'REB',\n",
" 'AST',\n",
" 'TOV',\n",
" 'STL',\n",
" 'BLK',\n",
" 'PF']"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#List of column names\n",
"list(nba)"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [],
"source": [
"#Sum up all the numerical columns - we will be normalizing the columns with the total games played\n",
"nba['Num_years'] =1\n",
"nba_agg = nba.groupby(['Player'])[['GP',\n",
" 'MIN',\n",
" 'PTS',\n",
" 'FGM',\n",
" 'FGA',\n",
" 'FG_PERC',\n",
" '3PM',\n",
" '3PA',\n",
" '3P_PERC',\n",
" 'FTM',\n",
" 'FTA',\n",
" 'FT_PERC',\n",
" 'OREB',\n",
" 'DREB',\n",
" 'REB',\n",
" 'AST',\n",
" 'TOV',\n",
" 'STL',\n",
" 'BLK',\n",
" 'PF','Num_years']].sum()"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>GP</th>\n",
" <th>MIN</th>\n",
" <th>PTS</th>\n",
" <th>FGM</th>\n",
" <th>FGA</th>\n",
" <th>FG_PERC</th>\n",
" <th>3PM</th>\n",
" <th>3PA</th>\n",
" <th>3P_PERC</th>\n",
" <th>FTM</th>\n",
" <th>...</th>\n",
" <th>FT_PERC</th>\n",
" <th>OREB</th>\n",
" <th>DREB</th>\n",
" <th>REB</th>\n",
" <th>AST</th>\n",
" <th>TOV</th>\n",
" <th>STL</th>\n",
" <th>BLK</th>\n",
" <th>PF</th>\n",
" <th>Num_years</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Player</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Jordan</th>\n",
" <td>179</td>\n",
" <td>7474</td>\n",
" <td>5987</td>\n",
" <td>2188</td>\n",
" <td>4497</td>\n",
" <td>627.2</td>\n",
" <td>148</td>\n",
" <td>446</td>\n",
" <td>489.0</td>\n",
" <td>1463</td>\n",
" <td>...</td>\n",
" <td>1087.9</td>\n",
" <td>305</td>\n",
" <td>847</td>\n",
" <td>1152</td>\n",
" <td>1022</td>\n",
" <td>546</td>\n",
" <td>376</td>\n",
" <td>158</td>\n",
" <td>541</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kobe</th>\n",
" <td>220</td>\n",
" <td>8652</td>\n",
" <td>5640</td>\n",
" <td>2014</td>\n",
" <td>4499</td>\n",
" <td>664.8</td>\n",
" <td>292</td>\n",
" <td>882</td>\n",
" <td>487.8</td>\n",
" <td>1320</td>\n",
" <td>...</td>\n",
" <td>1220.6</td>\n",
" <td>230</td>\n",
" <td>889</td>\n",
" <td>1119</td>\n",
" <td>1040</td>\n",
" <td>647</td>\n",
" <td>310</td>\n",
" <td>144</td>\n",
" <td>659</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lebron</th>\n",
" <td>237</td>\n",
" <td>9961</td>\n",
" <td>6855</td>\n",
" <td>2437</td>\n",
" <td>4965</td>\n",
" <td>638.8</td>\n",
" <td>369</td>\n",
" <td>1109</td>\n",
" <td>433.0</td>\n",
" <td>1612</td>\n",
" <td>...</td>\n",
" <td>961.8</td>\n",
" <td>362</td>\n",
" <td>1743</td>\n",
" <td>2105</td>\n",
" <td>1668</td>\n",
" <td>856</td>\n",
" <td>417</td>\n",
" <td>229</td>\n",
" <td>562</td>\n",
" <td>13</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" GP MIN PTS FGM FGA FG_PERC 3PM 3PA 3P_PERC FTM \\\n",
"Player \n",
"Jordan 179 7474 5987 2188 4497 627.2 148 446 489.0 1463 \n",
"Kobe 220 8652 5640 2014 4499 664.8 292 882 487.8 1320 \n",
"Lebron 237 9961 6855 2437 4965 638.8 369 1109 433.0 1612 \n",
"\n",
" ... FT_PERC OREB DREB REB AST TOV STL BLK PF \\\n",
"Player ... \n",
"Jordan ... 1087.9 305 847 1152 1022 546 376 158 541 \n",
"Kobe ... 1220.6 230 889 1119 1040 647 310 144 659 \n",
"Lebron ... 961.8 362 1743 2105 1668 856 417 229 562 \n",
"\n",
" Num_years \n",
"Player \n",
"Jordan 13 \n",
"Kobe 15 \n",
"Lebron 13 \n",
"\n",
"[3 rows x 21 columns]"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nba_agg.head()"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [],
"source": [
"#Normalize the metrics by the total number of matches played\n",
"nba_agg['2points'] = nba_agg['FGM']/nba_agg['GP'] \n",
"nba_agg['3points'] = nba_agg['3PM']/nba_agg['GP'] \n",
"nba_agg['Points'] = nba_agg['PTS']/nba_agg['GP'] \n",
"nba_agg['Freethrow_perc'] = nba_agg['FTM']/nba_agg['FTA'] \n",
"nba_agg['Rebounds'] = nba_agg['REB']/nba_agg['GP'] \n",
"nba_agg['Assists'] = nba_agg['AST']/nba_agg['GP'] \n",
"nba_agg['Turnovers'] = nba_agg['TOV']/nba_agg['GP'] \n",
"nba_agg['Steals'] = nba_agg['STL']/nba_agg['GP'] \n",
"nba_agg['Blocks'] = nba_agg['BLK']/nba_agg['GP'] \n",
"nba_agg['Personal_fouls'] = nba_agg['PF']/nba_agg['GP'] \n",
"nba_agg['Games_played'] = nba_agg['GP'] \n"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {},
"outputs": [],
"source": [
"#Keep the required metrics only\n",
"nba_final = nba_agg[['Games_played','2points','3points','Points','Freethrow_perc','Rebounds','Assists','Turnovers','Steals',\n",
" 'Blocks','Personal_fouls']]"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Games_played</th>\n",
" <th>2points</th>\n",
" <th>3points</th>\n",
" <th>Points</th>\n",
" <th>Freethrow_perc</th>\n",
" <th>Rebounds</th>\n",
" <th>Assists</th>\n",
" <th>Turnovers</th>\n",
" <th>Steals</th>\n",
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" <th>Personal_fouls</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Player</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>Jordan</th>\n",
" <td>179</td>\n",
" <td>12.223464</td>\n",
" <td>0.826816</td>\n",
" <td>33.446927</td>\n",
" <td>0.828426</td>\n",
" <td>6.435754</td>\n",
" <td>5.709497</td>\n",
" <td>3.050279</td>\n",
" <td>2.100559</td>\n",
" <td>0.882682</td>\n",
" <td>3.022346</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kobe</th>\n",
" <td>220</td>\n",
" <td>9.154545</td>\n",
" <td>1.327273</td>\n",
" <td>25.636364</td>\n",
" <td>0.816327</td>\n",
" <td>5.086364</td>\n",
" <td>4.727273</td>\n",
" <td>2.940909</td>\n",
" <td>1.409091</td>\n",
" <td>0.654545</td>\n",
" <td>2.995455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lebron</th>\n",
" <td>237</td>\n",
" <td>10.282700</td>\n",
" <td>1.556962</td>\n",
" <td>28.924051</td>\n",
" <td>0.741832</td>\n",
" <td>8.881857</td>\n",
" <td>7.037975</td>\n",
" <td>3.611814</td>\n",
" <td>1.759494</td>\n",
" <td>0.966245</td>\n",
" <td>2.371308</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Games_played 2points 3points Points Freethrow_perc \\\n",
"Player \n",
"Jordan 179 12.223464 0.826816 33.446927 0.828426 \n",
"Kobe 220 9.154545 1.327273 25.636364 0.816327 \n",
"Lebron 237 10.282700 1.556962 28.924051 0.741832 \n",
"\n",
" Rebounds Assists Turnovers Steals Blocks Personal_fouls \n",
"Player \n",
"Jordan 6.435754 5.709497 3.050279 2.100559 0.882682 3.022346 \n",
"Kobe 5.086364 4.727273 2.940909 1.409091 0.654545 2.995455 \n",
"Lebron 8.881857 7.037975 3.611814 1.759494 0.966245 2.371308 "
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nba_final.head()"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1a8ac688400>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1a8ac8cd390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1a8ac93f4a8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1a8ac97dba8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1a8ac9f6898>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1a8aca4a0f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1a8aca99828>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1a8acae3320>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1a8acb2c5c0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1a8acb97320>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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g5cAA8OMky6rq50OaLq2qs3ZBjZKkUeqk534ksL6qNlTVQ8DlwKJdW5YkaTw6CfcDgTsH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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a8acbff4e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Bar plot for comparison of stats\n",
"for i in list(nba_final):\n",
" nba_final.plot.bar(y=i)"
]
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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