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02-김윤지 #15

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da21346
Create yoongja
yoongja Jul 8, 2021
3869ca9
02-김윤지.ipynb
yoongja Jul 8, 2021
7944c5f
Merge branch 'main' of https://github.com/yoongja/2021-1-Data-Science…
yoongja Jul 8, 2021
0ebbfda
yoongja
yoongja Jul 8, 2021
d9e4f53
02-김윤지.ipynb
yoongja Jul 8, 2021
233c2da
02-김윤지.ipynb
yoongja Jul 8, 2021
78f0c73
02-김윤지
yoongja Jul 8, 2021
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02-김윤지
yoongja Jul 8, 2021
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02-김윤지
yoongja Jul 8, 2021
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158 changes: 78 additions & 80 deletions assignments/SolidCitadel/02-전현준.ipynb → ...02/.ipynb_checkpoints/02-checkpoint.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 3,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -217,7 +217,7 @@
"[98 rows x 10 columns]"
]
},
"execution_count": 1,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
Expand Down Expand Up @@ -245,7 +245,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 3,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -283,17 +283,30 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Barcelona</td>\n",
" <td>LaLiga</td>\n",
" <td>85</td>\n",
" <td>15.3</td>\n",
" <td>68</td>\n",
" <th>0</th>\n",
" <td>Manchester City</td>\n",
" <td>Premier League</td>\n",
" <td>83</td>\n",
" <td>15.8</td>\n",
" <td>46</td>\n",
" <td>2</td>\n",
" <td>62.4</td>\n",
" <td>89.7</td>\n",
" <td>10.6</td>\n",
" <td>6.87</td>\n",
" <td>60.8</td>\n",
" <td>89.4</td>\n",
" <td>12.8</td>\n",
" <td>7.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Bayern Munich</td>\n",
" <td>Bundesliga</td>\n",
" <td>99</td>\n",
" <td>17.1</td>\n",
" <td>44</td>\n",
" <td>3</td>\n",
" <td>58.1</td>\n",
" <td>85.5</td>\n",
" <td>12.9</td>\n",
" <td>6.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
Expand All @@ -309,71 +322,59 @@
" <td>6.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Manchester City</td>\n",
" <td>Premier League</td>\n",
" <td>83</td>\n",
" <td>15.8</td>\n",
" <td>46</td>\n",
" <th>3</th>\n",
" <td>Barcelona</td>\n",
" <td>LaLiga</td>\n",
" <td>85</td>\n",
" <td>15.3</td>\n",
" <td>68</td>\n",
" <td>2</td>\n",
" <td>60.8</td>\n",
" <td>89.4</td>\n",
" <td>12.8</td>\n",
" <td>7.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Juventus</td>\n",
" <td>Serie A</td>\n",
" <td>77</td>\n",
" <td>15.7</td>\n",
" <td>76</td>\n",
" <td>6</td>\n",
" <td>55.4</td>\n",
" <td>88.3</td>\n",
" <td>11.4</td>\n",
" <td>6.85</td>\n",
" <td>62.4</td>\n",
" <td>89.7</td>\n",
" <td>10.6</td>\n",
" <td>6.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>Sassuolo</td>\n",
" <td>Serie A</td>\n",
" <td>64</td>\n",
" <td>13.9</td>\n",
" <td>74</td>\n",
" <td>4</td>\n",
" <td>58.2</td>\n",
" <td>87.8</td>\n",
" <td>10.9</td>\n",
" <td>6.67</td>\n",
" <th>4</th>\n",
" <td>Real Madrid</td>\n",
" <td>LaLiga</td>\n",
" <td>67</td>\n",
" <td>14.4</td>\n",
" <td>57</td>\n",
" <td>2</td>\n",
" <td>57.7</td>\n",
" <td>87.7</td>\n",
" <td>11.8</td>\n",
" <td>6.86</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Team Tournament Goals Shots pg yellow_cards \\\n",
"3 Barcelona LaLiga 85 15.3 68 \n",
"2 Paris Saint-Germain Ligue 1 86 15.0 73 \n",
"0 Manchester City Premier League 83 15.8 46 \n",
"6 Juventus Serie A 77 15.7 76 \n",
"35 Sassuolo Serie A 64 13.9 74 \n",
" Team Tournament Goals Shots pg yellow_cards \\\n",
"0 Manchester City Premier League 83 15.8 46 \n",
"1 Bayern Munich Bundesliga 99 17.1 44 \n",
"2 Paris Saint-Germain Ligue 1 86 15.0 73 \n",
"3 Barcelona LaLiga 85 15.3 68 \n",
"4 Real Madrid LaLiga 67 14.4 57 \n",
"\n",
" red_cards Possession% Pass% AerialsWon Rating \n",
"3 2 62.4 89.7 10.6 6.87 \n",
"2 7 60.1 89.5 9.5 6.88 \n",
"0 2 60.8 89.4 12.8 7.01 \n",
"6 6 55.4 88.3 11.4 6.85 \n",
"35 4 58.2 87.8 10.9 6.67 "
" red_cards Possession% Pass% AerialsWon Rating \n",
"0 2 60.8 89.4 12.8 7.01 \n",
"1 3 58.1 85.5 12.9 6.95 \n",
"2 7 60.1 89.5 9.5 6.88 \n",
"3 2 62.4 89.7 10.6 6.87 \n",
"4 2 57.7 87.7 11.8 6.86 "
]
},
"execution_count": 4,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.sort_values(by='Pass%', ascending=False).head(5)"
"df.sort_values(by=\"Pass%\", ascending=False)\n",
"df.head()"
]
},
{
Expand All @@ -386,7 +387,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 4,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -478,13 +479,13 @@
"3 2 62.4 89.7 10.6 6.87 "
]
},
"execution_count": 5,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['Possession%']>=60]"
"df[df[\"Possession%\"]>60]"
]
},
{
Expand All @@ -498,7 +499,7 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 5,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -571,15 +572,15 @@
"Bundesliga 1081 33"
]
},
"execution_count": 22,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1 = df.loc[:, ['Tournament', 'yellow_cards', 'red_cards']]\n",
"df2 = df1.groupby('Tournament').sum()\n",
"df2.sort_values(['yellow_cards', 'red_cards'], ascending=[False, False])"
"df2=df.groupby(df['Tournament']).sum().sort_values(['yellow_cards','red_cards'],ascending= False)\n",
"df2=df2.loc[:,['yellow_cards','red_cards']]\n",
"df2"
]
},
{
Expand All @@ -597,7 +598,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 6,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -677,14 +678,14 @@
"Bundesliga 1081 33 2327"
]
},
"execution_count": 25,
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2['score'] = df2['red_cards']*5 + df2['yellow_cards']*2\n",
"df2.sort_values('score', ascending=False)"
"df2['score']=df2['red_cards']*5 +df2['yellow_cards']*2\n",
"df2"
]
},
{
Expand All @@ -699,7 +700,7 @@
},
{
"cell_type": "code",
"execution_count": 31,
"execution_count": 41,
"metadata": {},
"outputs": [
{
Expand All @@ -709,15 +710,12 @@
" [19.52841995, 21.99771618]])"
]
},
"execution_count": 31,
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr = df.loc[:, ['Possession%', 'Pass%']].to_numpy()\n",
"np.cov(arr.T)"
]
"source": []
},
{
"cell_type": "code",
Expand All @@ -743,7 +741,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
"version": "3.8.8"
}
},
"nbformat": 4,
Expand Down
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