├── README.md ├── data.csv ├── iris.ipynb ├── mlbrain.joblib └── soccer.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # ML-Notes -------------------------------------------------------------------------------- /iris.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "# load the iris dataset \n", 10 | "from sklearn.datasets import load_iris\n", 11 | "iris = load_iris()" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 4, 17 | "metadata": {}, 18 | "outputs": [ 19 | { 20 | "name": "stdout", 21 | "output_type": "stream", 22 | "text": [ 23 | "Feature names: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n", 24 | "Target names: ['setosa' 'versicolor' 'virginica']\n" 25 | ] 26 | } 27 | ], 28 | "source": [ 29 | "# store the feature matrix (X): input, and response vector (y): output (pre labeled answers)\n", 30 | "X = iris.data\n", 31 | "y = iris.target\n", 32 | "\n", 33 | "feature_names = iris.feature_names\n", 34 | "target_names = iris.target_names\n", 35 | "\n", 36 | "print(\"Feature names:\", feature_names) \n", 37 | "print(\"Target names:\", target_names) " 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": 7, 43 | "metadata": {}, 44 | "outputs": [ 45 | { 46 | "name": "stdout", 47 | "output_type": "stream", 48 | "text": [ 49 | "(120, 4)\n", 50 | "(30, 4)\n" 51 | ] 52 | } 53 | ], 54 | "source": [ 55 | "#Split data into training and test sets\n", 56 | "from sklearn.model_selection import train_test_split\n", 57 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n", 58 | "\n", 59 | "print(X_train.shape)\n", 60 | "print(X_test.shape)" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": 8, 66 | "metadata": {}, 67 | "outputs": [], 68 | "source": [ 69 | "#KNN Classifier. Try changing the n_neighbors\n", 70 | "from sklearn.neighbors import KNeighborsClassifier\n", 71 | "knn = KNeighborsClassifier(n_neighbors=3)\n", 72 | "knn.fit(X_train, y_train)\n", 73 | "\n", 74 | "#Decision Tree\n", 75 | "# from sklearn.tree import DecisionTreeClassifier \n", 76 | "# knn = DecisionTreeClassifier() \n", 77 | "# knn.fit(X_train, y_train) \n", 78 | "\n", 79 | "\n", 80 | "#make prediction\n", 81 | "y_pred = knn.predict(X_test)" 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "execution_count": 9, 87 | "metadata": {}, 88 | "outputs": [ 89 | { 90 | "name": "stdout", 91 | "output_type": "stream", 92 | "text": [ 93 | "0.9333333333333333\n" 94 | ] 95 | } 96 | ], 97 | "source": [ 98 | "#Accuracy of our model based on our test output and prediction output\n", 99 | "from sklearn import metrics\n", 100 | "print(metrics.accuracy_score(y_test, y_pred))" 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": null, 106 | "metadata": {}, 107 | "outputs": [], 108 | "source": [] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": 11, 113 | "metadata": { 114 | "scrolled": true 115 | }, 116 | "outputs": [ 117 | { 118 | "data": { 119 | "text/plain": [ 120 | "['mlbrain.joblib']" 121 | ] 122 | }, 123 | "execution_count": 11, 124 | "metadata": {}, 125 | "output_type": "execute_result" 126 | } 127 | ], 128 | "source": [ 129 | "#Model persistance is important. Next time we want to make a prediction we save a model to a file and use that file for predictions.\n", 130 | "from sklearn.externals import joblib\n", 131 | "joblib.dump(knn, 'mlbrain.joblib')" 132 | ] 133 | }, 134 | { 135 | "cell_type": "code", 136 | "execution_count": 14, 137 | "metadata": {}, 138 | "outputs": [ 139 | { 140 | "name": "stdout", 141 | "output_type": "stream", 142 | "text": [ 143 | "predictions: ['versicolor', 'virginica']\n" 144 | ] 145 | } 146 | ], 147 | "source": [ 148 | "#Load our model\n", 149 | "model = joblib.load('mlbrain.joblib')\n", 150 | "\n", 151 | "\n", 152 | "model.predict(X_test)\n", 153 | "sample = [[3,5,4,2], [2,3,5,4]]\n", 154 | "predictions = model.predict(sample)\n", 155 | "pred_species = [iris.target_names[p] for p in predictions]\n", 156 | "print(\"predictions: \", pred_species)" 157 | ] 158 | }, 159 | { 160 | "cell_type": "code", 161 | "execution_count": 15, 162 | "metadata": {}, 163 | "outputs": [ 164 | { 165 | "data": { 166 | "image/png": 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", 167 | "text/plain": [ 168 | "
" 169 | ] 170 | }, 171 | "metadata": { 172 | "needs_background": "light" 173 | }, 174 | "output_type": "display_data" 175 | } 176 | ], 177 | "source": [ 178 | "from sklearn.datasets import load_iris \n", 179 | "iris = load_iris() \n", 180 | "import matplotlib.pyplot as plt\n", 181 | "\n", 182 | "# The indices of the features that we are plotting\n", 183 | "x_index = 0\n", 184 | "y_index = 1\n", 185 | "\n", 186 | "# colorbar with the Iris target names\n", 187 | "formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)])\n", 188 | "\n", 189 | "#chart configurations\n", 190 | "plt.figure(figsize=(5, 4))\n", 191 | "plt.scatter(iris.data[:, x_index], iris.data[:, y_index], c=iris.target)\n", 192 | "plt.colorbar(ticks=[0, 1, 2], format=formatter)\n", 193 | "plt.xlabel(iris.feature_names[x_index])\n", 194 | "plt.ylabel(iris.feature_names[y_index])\n", 195 | "\n", 196 | "plt.tight_layout()\n", 197 | "plt.show()" 198 | ] 199 | }, 200 | { 201 | "cell_type": "code", 202 | "execution_count": 16, 203 | "metadata": {}, 204 | "outputs": [ 205 | { 206 | "data": { 207 | "text/plain": [ 208 | "" 209 | ] 210 | }, 211 | "execution_count": 16, 212 | "metadata": {}, 213 | "output_type": "execute_result" 214 | }, 215 | { 216 | "data": { 217 | "image/png": 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", 218 | "text/plain": [ 219 | "
" 220 | ] 221 | }, 222 | "metadata": { 223 | "needs_background": "light" 224 | }, 225 | "output_type": "display_data" 226 | } 227 | ], 228 | "source": [ 229 | "features = iris.data.T\n", 230 | "\n", 231 | "plt.scatter(features[2], features[3], alpha=0.2,\n", 232 | " s=100*features[3], c=iris.target, cmap='viridis') #https://jakevdp.github.io/PythonDataScienceHandbook/04.02-simple-scatter-plots.html\n", 233 | "plt.xlabel(iris.feature_names[2])\n", 234 | "plt.ylabel(iris.feature_names[3]);\n", 235 | "plt.colorbar(ticks=[0, 1, 2], format=formatter)" 236 | ] 237 | }, 238 | { 239 | "cell_type": "code", 240 | "execution_count": null, 241 | "metadata": {}, 242 | "outputs": [], 243 | "source": [] 244 | } 245 | ], 246 | "metadata": { 247 | "kernelspec": { 248 | "display_name": "Python 3", 249 | "language": "python", 250 | "name": "python3" 251 | }, 252 | "language_info": { 253 | "codemirror_mode": { 254 | "name": "ipython", 255 | "version": 3 256 | }, 257 | "file_extension": ".py", 258 | "mimetype": "text/x-python", 259 | "name": "python", 260 | "nbconvert_exporter": "python", 261 | "pygments_lexer": "ipython3", 262 | "version": "3.7.1" 263 | } 264 | }, 265 | "nbformat": 4, 266 | "nbformat_minor": 2 267 | } 268 | -------------------------------------------------------------------------------- /mlbrain.joblib: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aneagoie/ML-Notes/b70eb004bbbe6d1bdfedba85193fea8802edbb41/mlbrain.joblib -------------------------------------------------------------------------------- /soccer.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 7, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "data": { 10 | "text/html": [ 11 | "
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Unnamed: 0IDNameAgePhotoNationalityFlagOverallPotentialClub...ComposureMarkingStandingTackleSlidingTackleGKDivingGKHandlingGKKickingGKPositioningGKReflexesRelease Clause
00158023L. Messi31https://cdn.sofifa.org/players/4/19/158023.pngArgentinahttps://cdn.sofifa.org/flags/52.png9494FC Barcelona...96.033.028.026.06.011.015.014.08.0€226.5M
1120801Cristiano Ronaldo33https://cdn.sofifa.org/players/4/19/20801.pngPortugalhttps://cdn.sofifa.org/flags/38.png9494Juventus...95.028.031.023.07.011.015.014.011.0€127.1M
22190871Neymar Jr26https://cdn.sofifa.org/players/4/19/190871.pngBrazilhttps://cdn.sofifa.org/flags/54.png9293Paris Saint-Germain...94.027.024.033.09.09.015.015.011.0€228.1M
33193080De Gea27https://cdn.sofifa.org/players/4/19/193080.pngSpainhttps://cdn.sofifa.org/flags/45.png9193Manchester United...68.015.021.013.090.085.087.088.094.0€138.6M
44192985K. De Bruyne27https://cdn.sofifa.org/players/4/19/192985.pngBelgiumhttps://cdn.sofifa.org/flags/7.png9192Manchester City...88.068.058.051.015.013.05.010.013.0€196.4M
55183277E. Hazard27https://cdn.sofifa.org/players/4/19/183277.pngBelgiumhttps://cdn.sofifa.org/flags/7.png9191Chelsea...91.034.027.022.011.012.06.08.08.0€172.1M
66177003L. Modrić32https://cdn.sofifa.org/players/4/19/177003.pngCroatiahttps://cdn.sofifa.org/flags/10.png9191Real Madrid...84.060.076.073.013.09.07.014.09.0€137.4M
77176580L. Suárez31https://cdn.sofifa.org/players/4/19/176580.pngUruguayhttps://cdn.sofifa.org/flags/60.png9191FC Barcelona...85.062.045.038.027.025.031.033.037.0€164M
88155862Sergio Ramos32https://cdn.sofifa.org/players/4/19/155862.pngSpainhttps://cdn.sofifa.org/flags/45.png9191Real Madrid...82.087.092.091.011.08.09.07.011.0€104.6M
99200389J. Oblak25https://cdn.sofifa.org/players/4/19/200389.pngSloveniahttps://cdn.sofifa.org/flags/44.png9093Atlético Madrid...70.027.012.018.086.092.078.088.089.0€144.5M
1010188545R. Lewandowski29https://cdn.sofifa.org/players/4/19/188545.pngPolandhttps://cdn.sofifa.org/flags/37.png9090FC Bayern München...86.034.042.019.015.06.012.08.010.0€127.1M
1111182521T. Kroos28https://cdn.sofifa.org/players/4/19/182521.pngGermanyhttps://cdn.sofifa.org/flags/21.png9090Real Madrid...85.072.079.069.010.011.013.07.010.0€156.8M
1212182493D. Godín32https://cdn.sofifa.org/players/4/19/182493.pngUruguayhttps://cdn.sofifa.org/flags/60.png9090Atlético Madrid...82.090.089.089.06.08.015.05.015.0€90.2M
1313168542David Silva32https://cdn.sofifa.org/players/4/19/168542.pngSpainhttps://cdn.sofifa.org/flags/45.png9090Manchester City...93.059.053.029.06.015.07.06.012.0€111M
1414215914N. Kanté27https://cdn.sofifa.org/players/4/19/215914.pngFrancehttps://cdn.sofifa.org/flags/18.png8990Chelsea...85.090.091.085.015.012.010.07.010.0€121.3M
1515211110P. Dybala24https://cdn.sofifa.org/players/4/19/211110.pngArgentinahttps://cdn.sofifa.org/flags/52.png8994Juventus...84.023.020.020.05.04.04.05.08.0€153.5M
1616202126H. Kane24https://cdn.sofifa.org/players/4/19/202126.pngEnglandhttps://cdn.sofifa.org/flags/14.png8991Tottenham Hotspur...89.056.036.038.08.010.011.014.011.0€160.7M
1717194765A. Griezmann27https://cdn.sofifa.org/players/4/19/194765.pngFrancehttps://cdn.sofifa.org/flags/18.png8990Atlético Madrid...87.059.047.048.014.08.014.013.014.0€165.8M
1818192448M. ter Stegen26https://cdn.sofifa.org/players/4/19/192448.pngGermanyhttps://cdn.sofifa.org/flags/21.png8992FC Barcelona...69.025.013.010.087.085.088.085.090.0€123.3M
1919192119T. Courtois26https://cdn.sofifa.org/players/4/19/192119.pngBelgiumhttps://cdn.sofifa.org/flags/7.png8990Real Madrid...66.020.018.016.085.091.072.086.088.0€113.7M
2020189511Sergio Busquets29https://cdn.sofifa.org/players/4/19/189511.pngSpainhttps://cdn.sofifa.org/flags/45.png8989FC Barcelona...90.090.086.080.05.08.013.09.013.0€105.6M
2121179813E. Cavani31https://cdn.sofifa.org/players/4/19/179813.pngUruguayhttps://cdn.sofifa.org/flags/60.png8989Paris Saint-Germain...82.052.045.039.012.05.013.013.010.0€111M
2222167495M. Neuer32https://cdn.sofifa.org/players/4/19/167495.pngGermanyhttps://cdn.sofifa.org/flags/21.png8989FC Bayern München...70.017.010.011.090.086.091.087.087.0€62.7M
2323153079S. Agüero30https://cdn.sofifa.org/players/4/19/153079.pngArgentinahttps://cdn.sofifa.org/flags/52.png8989Manchester City...90.030.020.012.013.015.06.011.014.0€119.3M
2424138956G. Chiellini33https://cdn.sofifa.org/players/4/19/138956.pngItalyhttps://cdn.sofifa.org/flags/27.png8989Juventus...84.093.093.090.03.03.02.04.03.0€44.6M
2525231747K. Mbappé19https://cdn.sofifa.org/players/4/19/231747.pngFrancehttps://cdn.sofifa.org/flags/18.png8895Paris Saint-Germain...86.034.034.032.013.05.07.011.06.0€166.1M
2626209331M. Salah26https://cdn.sofifa.org/players/4/19/209331.pngEgypthttps://cdn.sofifa.org/flags/111.png8889Liverpool...91.038.043.041.014.014.09.011.014.0€137.3M
2727200145Casemiro26https://cdn.sofifa.org/players/4/19/200145.pngBrazilhttps://cdn.sofifa.org/flags/54.png8890Real Madrid...84.088.090.087.013.014.016.012.012.0€126.4M
2828198710J. Rodríguez26https://cdn.sofifa.org/players/4/19/198710.pngColombiahttps://cdn.sofifa.org/flags/56.png8889FC Bayern München...87.052.041.044.015.015.015.05.014.0NaN
2929198219L. Insigne27https://cdn.sofifa.org/players/4/19/198219.pngItalyhttps://cdn.sofifa.org/flags/27.png8888Napoli...83.051.024.022.08.04.014.09.010.0€105.4M
..................................................................
1817718177238550R. Roache18https://cdn.sofifa.org/players/4/19/238550.pngRepublic of Irelandhttps://cdn.sofifa.org/flags/25.png4869Blackpool...49.018.016.011.06.09.011.07.012.0€193K
1817818178243158L. Wahlstedt18https://cdn.sofifa.org/players/4/19/243158.pngSwedenhttps://cdn.sofifa.org/flags/46.png4865Dalkurd FF...28.016.011.010.047.046.050.045.051.0€94K
1817918179246243J. Williams17https://cdn.sofifa.org/players/4/19/246243.pngEnglandhttps://cdn.sofifa.org/flags/14.png4864Northampton Town...37.042.051.049.014.011.07.011.08.0€119K
1818018180221669M. Hurst22https://cdn.sofifa.org/players/4/19/221669.pngScotlandhttps://cdn.sofifa.org/flags/42.png4858St. Johnstone FC...28.012.015.016.045.049.050.050.045.0€78K
1818118181245734C. Maher17https://cdn.sofifa.org/players/4/19/245734.pngRepublic of Irelandhttps://cdn.sofifa.org/flags/25.png4866Bray Wanderers...38.043.049.045.08.010.012.09.010.0€109K
1818218182246001Y. Góez18https://cdn.sofifa.org/players/4/19/246001.pngColombiahttps://cdn.sofifa.org/flags/56.png4865Atlético Nacional...38.044.042.046.09.015.015.08.06.0€101K
181831818353748K. Pilkington44https://cdn.sofifa.org/players/4/19/53748.pngEnglandhttps://cdn.sofifa.org/flags/14.png4848Cambridge United...56.015.015.013.045.048.044.049.046.0NaN
1818418184241657D. Horton18https://cdn.sofifa.org/players/4/19/241657.pngEnglandhttps://cdn.sofifa.org/flags/14.png4855Lincoln City...42.047.049.053.012.05.012.014.015.0€78K
1818518185243961E. Tweed19https://cdn.sofifa.org/players/4/19/243961.pngRepublic of Irelandhttps://cdn.sofifa.org/flags/25.png4859Derry City...43.039.039.048.06.011.09.05.08.0€88K
1818618186240917Zhang Yufeng20https://cdn.sofifa.org/players/4/19/240917.pngChina PRhttps://cdn.sofifa.org/flags/155.png4764Beijing Renhe FC...39.053.041.051.015.07.014.06.08.0€167K
1818718187240158C. Ehlich19https://cdn.sofifa.org/players/4/19/240158.pngGermanyhttps://cdn.sofifa.org/flags/21.png4759SpVgg Unterhaching...47.040.042.042.013.012.011.015.012.0€66K
1818818188240927L. Collins17https://cdn.sofifa.org/players/4/19/240927.pngWaleshttps://cdn.sofifa.org/flags/50.png4762Newport County...46.033.038.041.05.012.08.013.010.0€143K
1818918189240160A. Kaltner18https://cdn.sofifa.org/players/4/19/240160.pngGermanyhttps://cdn.sofifa.org/flags/21.png4761SpVgg Unterhaching...37.028.015.022.015.05.014.012.08.0€125K
1819018190245569L. Watkins18https://cdn.sofifa.org/players/4/19/245569.pngEnglandhttps://cdn.sofifa.org/flags/14.png4767Cambridge United...46.035.044.047.013.07.014.010.08.0€165K
1819118191245570J. Norville-Williams18https://cdn.sofifa.org/players/4/19/245570.pngEnglandhttps://cdn.sofifa.org/flags/14.png4765Cambridge United...36.045.042.046.015.013.06.014.012.0€119K
1819218192245571S. Squire18https://cdn.sofifa.org/players/4/19/245571.pngEnglandhttps://cdn.sofifa.org/flags/14.png4764Cambridge United...38.041.041.044.011.011.08.012.013.0€119K
1819318193244823N. Fuentes18https://cdn.sofifa.org/players/4/19/244823.pngChilehttps://cdn.sofifa.org/flags/55.png4764Unión Española...32.041.048.048.06.010.06.012.011.0€99K
1819418194245862J. Milli18https://cdn.sofifa.org/players/4/19/245862.pngItalyhttps://cdn.sofifa.org/flags/27.png4765Lecce...23.06.010.011.052.052.052.040.044.0€109K
1819518195243582S. Griffin18https://cdn.sofifa.org/players/4/19/243582.pngRepublic of Irelandhttps://cdn.sofifa.org/flags/25.png4767Waterford FC...41.044.037.048.013.014.012.07.013.0€153K
1819618196238477K. Fujikawa19https://cdn.sofifa.org/players/4/19/238477.pngJapanhttps://cdn.sofifa.org/flags/163.png4761Júbilo Iwata...35.041.044.054.010.012.06.011.08.0€113K
1819718197246167D. Holland18https://cdn.sofifa.org/players/4/19/246167.pngRepublic of Irelandhttps://cdn.sofifa.org/flags/25.png4761Cork City...52.041.047.038.013.06.09.010.015.0€88K
1819818198242844J. Livesey18https://cdn.sofifa.org/players/4/19/242844.pngEnglandhttps://cdn.sofifa.org/flags/14.png4770Burton Albion...34.015.011.013.046.052.058.042.048.0€165K
1819918199244677M. Baldisimo18https://cdn.sofifa.org/players/4/19/244677.pngCanadahttps://cdn.sofifa.org/flags/70.png4769Vancouver Whitecaps FC...40.048.049.049.07.07.09.014.015.0€175K
1820018200231381J. Young18https://cdn.sofifa.org/players/4/19/231381.pngScotlandhttps://cdn.sofifa.org/flags/42.png4762Swindon Town...50.015.017.014.011.015.012.012.011.0€143K
1820118201243413D. Walsh18https://cdn.sofifa.org/players/4/19/243413.pngRepublic of Irelandhttps://cdn.sofifa.org/flags/25.png4768Waterford FC...43.044.047.053.09.010.09.011.013.0€153K
1820218202238813J. Lundstram19https://cdn.sofifa.org/players/4/19/238813.pngEnglandhttps://cdn.sofifa.org/flags/14.png4765Crewe Alexandra...45.040.048.047.010.013.07.08.09.0€143K
1820318203243165N. Christoffersson19https://cdn.sofifa.org/players/4/19/243165.pngSwedenhttps://cdn.sofifa.org/flags/46.png4763Trelleborgs FF...42.022.015.019.010.09.09.05.012.0€113K
1820418204241638B. Worman16https://cdn.sofifa.org/players/4/19/241638.pngEnglandhttps://cdn.sofifa.org/flags/14.png4767Cambridge United...41.032.013.011.06.05.010.06.013.0€165K
1820518205246268D. Walker-Rice17https://cdn.sofifa.org/players/4/19/246268.pngEnglandhttps://cdn.sofifa.org/flags/14.png4766Tranmere Rovers...46.020.025.027.014.06.014.08.09.0€143K
1820618206246269G. Nugent16https://cdn.sofifa.org/players/4/19/246269.pngEnglandhttps://cdn.sofifa.org/flags/14.png4666Tranmere Rovers...43.040.043.050.010.015.09.012.09.0€165K
\n", 1519 | "

18207 rows × 89 columns

\n", 1520 | "
" 1521 | ], 1522 | "text/plain": [ 1523 | " Unnamed: 0 ID Name Age \\\n", 1524 | "0 0 158023 L. Messi 31 \n", 1525 | "1 1 20801 Cristiano Ronaldo 33 \n", 1526 | "2 2 190871 Neymar Jr 26 \n", 1527 | "3 3 193080 De Gea 27 \n", 1528 | "4 4 192985 K. De Bruyne 27 \n", 1529 | "5 5 183277 E. Hazard 27 \n", 1530 | "6 6 177003 L. Modrić 32 \n", 1531 | "7 7 176580 L. Suárez 31 \n", 1532 | "8 8 155862 Sergio Ramos 32 \n", 1533 | "9 9 200389 J. Oblak 25 \n", 1534 | "10 10 188545 R. Lewandowski 29 \n", 1535 | "11 11 182521 T. Kroos 28 \n", 1536 | "12 12 182493 D. Godín 32 \n", 1537 | "13 13 168542 David Silva 32 \n", 1538 | "14 14 215914 N. Kanté 27 \n", 1539 | "15 15 211110 P. Dybala 24 \n", 1540 | "16 16 202126 H. Kane 24 \n", 1541 | "17 17 194765 A. Griezmann 27 \n", 1542 | "18 18 192448 M. ter Stegen 26 \n", 1543 | "19 19 192119 T. Courtois 26 \n", 1544 | "20 20 189511 Sergio Busquets 29 \n", 1545 | "21 21 179813 E. Cavani 31 \n", 1546 | "22 22 167495 M. Neuer 32 \n", 1547 | "23 23 153079 S. Agüero 30 \n", 1548 | "24 24 138956 G. Chiellini 33 \n", 1549 | "25 25 231747 K. Mbappé 19 \n", 1550 | "26 26 209331 M. Salah 26 \n", 1551 | "27 27 200145 Casemiro 26 \n", 1552 | "28 28 198710 J. Rodríguez 26 \n", 1553 | "29 29 198219 L. Insigne 27 \n", 1554 | "... ... ... ... ... \n", 1555 | "18177 18177 238550 R. Roache 18 \n", 1556 | "18178 18178 243158 L. Wahlstedt 18 \n", 1557 | "18179 18179 246243 J. Williams 17 \n", 1558 | "18180 18180 221669 M. Hurst 22 \n", 1559 | "18181 18181 245734 C. Maher 17 \n", 1560 | "18182 18182 246001 Y. Góez 18 \n", 1561 | "18183 18183 53748 K. Pilkington 44 \n", 1562 | "18184 18184 241657 D. Horton 18 \n", 1563 | "18185 18185 243961 E. Tweed 19 \n", 1564 | "18186 18186 240917 Zhang Yufeng 20 \n", 1565 | "18187 18187 240158 C. Ehlich 19 \n", 1566 | "18188 18188 240927 L. Collins 17 \n", 1567 | "18189 18189 240160 A. Kaltner 18 \n", 1568 | "18190 18190 245569 L. Watkins 18 \n", 1569 | "18191 18191 245570 J. Norville-Williams 18 \n", 1570 | "18192 18192 245571 S. Squire 18 \n", 1571 | "18193 18193 244823 N. Fuentes 18 \n", 1572 | "18194 18194 245862 J. Milli 18 \n", 1573 | "18195 18195 243582 S. Griffin 18 \n", 1574 | "18196 18196 238477 K. Fujikawa 19 \n", 1575 | "18197 18197 246167 D. Holland 18 \n", 1576 | "18198 18198 242844 J. Livesey 18 \n", 1577 | "18199 18199 244677 M. Baldisimo 18 \n", 1578 | "18200 18200 231381 J. Young 18 \n", 1579 | "18201 18201 243413 D. Walsh 18 \n", 1580 | "18202 18202 238813 J. Lundstram 19 \n", 1581 | "18203 18203 243165 N. Christoffersson 19 \n", 1582 | "18204 18204 241638 B. Worman 16 \n", 1583 | "18205 18205 246268 D. Walker-Rice 17 \n", 1584 | "18206 18206 246269 G. Nugent 16 \n", 1585 | "\n", 1586 | " Photo Nationality \\\n", 1587 | "0 https://cdn.sofifa.org/players/4/19/158023.png Argentina \n", 1588 | "1 https://cdn.sofifa.org/players/4/19/20801.png Portugal \n", 1589 | "2 https://cdn.sofifa.org/players/4/19/190871.png Brazil \n", 1590 | "3 https://cdn.sofifa.org/players/4/19/193080.png Spain \n", 1591 | "4 https://cdn.sofifa.org/players/4/19/192985.png Belgium \n", 1592 | "5 https://cdn.sofifa.org/players/4/19/183277.png Belgium \n", 1593 | "6 https://cdn.sofifa.org/players/4/19/177003.png Croatia \n", 1594 | "7 https://cdn.sofifa.org/players/4/19/176580.png Uruguay \n", 1595 | "8 https://cdn.sofifa.org/players/4/19/155862.png Spain \n", 1596 | "9 https://cdn.sofifa.org/players/4/19/200389.png Slovenia \n", 1597 | "10 https://cdn.sofifa.org/players/4/19/188545.png Poland \n", 1598 | "11 https://cdn.sofifa.org/players/4/19/182521.png Germany \n", 1599 | "12 https://cdn.sofifa.org/players/4/19/182493.png Uruguay \n", 1600 | "13 https://cdn.sofifa.org/players/4/19/168542.png Spain \n", 1601 | "14 https://cdn.sofifa.org/players/4/19/215914.png France \n", 1602 | "15 https://cdn.sofifa.org/players/4/19/211110.png Argentina \n", 1603 | "16 https://cdn.sofifa.org/players/4/19/202126.png England \n", 1604 | "17 https://cdn.sofifa.org/players/4/19/194765.png France \n", 1605 | "18 https://cdn.sofifa.org/players/4/19/192448.png Germany \n", 1606 | "19 https://cdn.sofifa.org/players/4/19/192119.png Belgium \n", 1607 | "20 https://cdn.sofifa.org/players/4/19/189511.png Spain \n", 1608 | "21 https://cdn.sofifa.org/players/4/19/179813.png Uruguay \n", 1609 | "22 https://cdn.sofifa.org/players/4/19/167495.png Germany \n", 1610 | "23 https://cdn.sofifa.org/players/4/19/153079.png Argentina \n", 1611 | "24 https://cdn.sofifa.org/players/4/19/138956.png Italy \n", 1612 | "25 https://cdn.sofifa.org/players/4/19/231747.png France \n", 1613 | "26 https://cdn.sofifa.org/players/4/19/209331.png Egypt \n", 1614 | "27 https://cdn.sofifa.org/players/4/19/200145.png Brazil \n", 1615 | "28 https://cdn.sofifa.org/players/4/19/198710.png Colombia \n", 1616 | "29 https://cdn.sofifa.org/players/4/19/198219.png Italy \n", 1617 | "... ... ... \n", 1618 | "18177 https://cdn.sofifa.org/players/4/19/238550.png Republic of Ireland \n", 1619 | "18178 https://cdn.sofifa.org/players/4/19/243158.png Sweden \n", 1620 | "18179 https://cdn.sofifa.org/players/4/19/246243.png England \n", 1621 | "18180 https://cdn.sofifa.org/players/4/19/221669.png Scotland \n", 1622 | "18181 https://cdn.sofifa.org/players/4/19/245734.png Republic of Ireland \n", 1623 | "18182 https://cdn.sofifa.org/players/4/19/246001.png Colombia \n", 1624 | "18183 https://cdn.sofifa.org/players/4/19/53748.png England \n", 1625 | "18184 https://cdn.sofifa.org/players/4/19/241657.png England \n", 1626 | "18185 https://cdn.sofifa.org/players/4/19/243961.png Republic of Ireland \n", 1627 | "18186 https://cdn.sofifa.org/players/4/19/240917.png China PR \n", 1628 | "18187 https://cdn.sofifa.org/players/4/19/240158.png Germany \n", 1629 | "18188 https://cdn.sofifa.org/players/4/19/240927.png Wales \n", 1630 | "18189 https://cdn.sofifa.org/players/4/19/240160.png Germany \n", 1631 | "18190 https://cdn.sofifa.org/players/4/19/245569.png England \n", 1632 | "18191 https://cdn.sofifa.org/players/4/19/245570.png England \n", 1633 | "18192 https://cdn.sofifa.org/players/4/19/245571.png England \n", 1634 | "18193 https://cdn.sofifa.org/players/4/19/244823.png Chile \n", 1635 | "18194 https://cdn.sofifa.org/players/4/19/245862.png Italy \n", 1636 | "18195 https://cdn.sofifa.org/players/4/19/243582.png Republic of Ireland \n", 1637 | "18196 https://cdn.sofifa.org/players/4/19/238477.png Japan \n", 1638 | "18197 https://cdn.sofifa.org/players/4/19/246167.png Republic of Ireland \n", 1639 | "18198 https://cdn.sofifa.org/players/4/19/242844.png England \n", 1640 | "18199 https://cdn.sofifa.org/players/4/19/244677.png Canada \n", 1641 | "18200 https://cdn.sofifa.org/players/4/19/231381.png Scotland \n", 1642 | "18201 https://cdn.sofifa.org/players/4/19/243413.png Republic of Ireland \n", 1643 | "18202 https://cdn.sofifa.org/players/4/19/238813.png England \n", 1644 | "18203 https://cdn.sofifa.org/players/4/19/243165.png Sweden \n", 1645 | "18204 https://cdn.sofifa.org/players/4/19/241638.png England \n", 1646 | "18205 https://cdn.sofifa.org/players/4/19/246268.png England \n", 1647 | "18206 https://cdn.sofifa.org/players/4/19/246269.png England \n", 1648 | "\n", 1649 | " Flag Overall Potential \\\n", 1650 | "0 https://cdn.sofifa.org/flags/52.png 94 94 \n", 1651 | "1 https://cdn.sofifa.org/flags/38.png 94 94 \n", 1652 | "2 https://cdn.sofifa.org/flags/54.png 92 93 \n", 1653 | "3 https://cdn.sofifa.org/flags/45.png 91 93 \n", 1654 | "4 https://cdn.sofifa.org/flags/7.png 91 92 \n", 1655 | "5 https://cdn.sofifa.org/flags/7.png 91 91 \n", 1656 | "6 https://cdn.sofifa.org/flags/10.png 91 91 \n", 1657 | "7 https://cdn.sofifa.org/flags/60.png 91 91 \n", 1658 | "8 https://cdn.sofifa.org/flags/45.png 91 91 \n", 1659 | "9 https://cdn.sofifa.org/flags/44.png 90 93 \n", 1660 | "10 https://cdn.sofifa.org/flags/37.png 90 90 \n", 1661 | "11 https://cdn.sofifa.org/flags/21.png 90 90 \n", 1662 | "12 https://cdn.sofifa.org/flags/60.png 90 90 \n", 1663 | "13 https://cdn.sofifa.org/flags/45.png 90 90 \n", 1664 | "14 https://cdn.sofifa.org/flags/18.png 89 90 \n", 1665 | "15 https://cdn.sofifa.org/flags/52.png 89 94 \n", 1666 | "16 https://cdn.sofifa.org/flags/14.png 89 91 \n", 1667 | "17 https://cdn.sofifa.org/flags/18.png 89 90 \n", 1668 | "18 https://cdn.sofifa.org/flags/21.png 89 92 \n", 1669 | "19 https://cdn.sofifa.org/flags/7.png 89 90 \n", 1670 | "20 https://cdn.sofifa.org/flags/45.png 89 89 \n", 1671 | "21 https://cdn.sofifa.org/flags/60.png 89 89 \n", 1672 | "22 https://cdn.sofifa.org/flags/21.png 89 89 \n", 1673 | "23 https://cdn.sofifa.org/flags/52.png 89 89 \n", 1674 | "24 https://cdn.sofifa.org/flags/27.png 89 89 \n", 1675 | "25 https://cdn.sofifa.org/flags/18.png 88 95 \n", 1676 | "26 https://cdn.sofifa.org/flags/111.png 88 89 \n", 1677 | "27 https://cdn.sofifa.org/flags/54.png 88 90 \n", 1678 | "28 https://cdn.sofifa.org/flags/56.png 88 89 \n", 1679 | "29 https://cdn.sofifa.org/flags/27.png 88 88 \n", 1680 | "... ... ... ... \n", 1681 | "18177 https://cdn.sofifa.org/flags/25.png 48 69 \n", 1682 | "18178 https://cdn.sofifa.org/flags/46.png 48 65 \n", 1683 | "18179 https://cdn.sofifa.org/flags/14.png 48 64 \n", 1684 | "18180 https://cdn.sofifa.org/flags/42.png 48 58 \n", 1685 | "18181 https://cdn.sofifa.org/flags/25.png 48 66 \n", 1686 | "18182 https://cdn.sofifa.org/flags/56.png 48 65 \n", 1687 | "18183 https://cdn.sofifa.org/flags/14.png 48 48 \n", 1688 | "18184 https://cdn.sofifa.org/flags/14.png 48 55 \n", 1689 | "18185 https://cdn.sofifa.org/flags/25.png 48 59 \n", 1690 | "18186 https://cdn.sofifa.org/flags/155.png 47 64 \n", 1691 | "18187 https://cdn.sofifa.org/flags/21.png 47 59 \n", 1692 | "18188 https://cdn.sofifa.org/flags/50.png 47 62 \n", 1693 | "18189 https://cdn.sofifa.org/flags/21.png 47 61 \n", 1694 | "18190 https://cdn.sofifa.org/flags/14.png 47 67 \n", 1695 | "18191 https://cdn.sofifa.org/flags/14.png 47 65 \n", 1696 | "18192 https://cdn.sofifa.org/flags/14.png 47 64 \n", 1697 | "18193 https://cdn.sofifa.org/flags/55.png 47 64 \n", 1698 | "18194 https://cdn.sofifa.org/flags/27.png 47 65 \n", 1699 | "18195 https://cdn.sofifa.org/flags/25.png 47 67 \n", 1700 | "18196 https://cdn.sofifa.org/flags/163.png 47 61 \n", 1701 | "18197 https://cdn.sofifa.org/flags/25.png 47 61 \n", 1702 | "18198 https://cdn.sofifa.org/flags/14.png 47 70 \n", 1703 | "18199 https://cdn.sofifa.org/flags/70.png 47 69 \n", 1704 | "18200 https://cdn.sofifa.org/flags/42.png 47 62 \n", 1705 | "18201 https://cdn.sofifa.org/flags/25.png 47 68 \n", 1706 | "18202 https://cdn.sofifa.org/flags/14.png 47 65 \n", 1707 | "18203 https://cdn.sofifa.org/flags/46.png 47 63 \n", 1708 | "18204 https://cdn.sofifa.org/flags/14.png 47 67 \n", 1709 | "18205 https://cdn.sofifa.org/flags/14.png 47 66 \n", 1710 | "18206 https://cdn.sofifa.org/flags/14.png 46 66 \n", 1711 | "\n", 1712 | " Club ... Composure Marking StandingTackle \\\n", 1713 | "0 FC Barcelona ... 96.0 33.0 28.0 \n", 1714 | "1 Juventus ... 95.0 28.0 31.0 \n", 1715 | "2 Paris Saint-Germain ... 94.0 27.0 24.0 \n", 1716 | "3 Manchester United ... 68.0 15.0 21.0 \n", 1717 | "4 Manchester City ... 88.0 68.0 58.0 \n", 1718 | "5 Chelsea ... 91.0 34.0 27.0 \n", 1719 | "6 Real Madrid ... 84.0 60.0 76.0 \n", 1720 | "7 FC Barcelona ... 85.0 62.0 45.0 \n", 1721 | "8 Real Madrid ... 82.0 87.0 92.0 \n", 1722 | "9 Atlético Madrid ... 70.0 27.0 12.0 \n", 1723 | "10 FC Bayern München ... 86.0 34.0 42.0 \n", 1724 | "11 Real Madrid ... 85.0 72.0 79.0 \n", 1725 | "12 Atlético Madrid ... 82.0 90.0 89.0 \n", 1726 | "13 Manchester City ... 93.0 59.0 53.0 \n", 1727 | "14 Chelsea ... 85.0 90.0 91.0 \n", 1728 | "15 Juventus ... 84.0 23.0 20.0 \n", 1729 | "16 Tottenham Hotspur ... 89.0 56.0 36.0 \n", 1730 | "17 Atlético Madrid ... 87.0 59.0 47.0 \n", 1731 | "18 FC Barcelona ... 69.0 25.0 13.0 \n", 1732 | "19 Real Madrid ... 66.0 20.0 18.0 \n", 1733 | "20 FC Barcelona ... 90.0 90.0 86.0 \n", 1734 | "21 Paris Saint-Germain ... 82.0 52.0 45.0 \n", 1735 | "22 FC Bayern München ... 70.0 17.0 10.0 \n", 1736 | "23 Manchester City ... 90.0 30.0 20.0 \n", 1737 | "24 Juventus ... 84.0 93.0 93.0 \n", 1738 | "25 Paris Saint-Germain ... 86.0 34.0 34.0 \n", 1739 | "26 Liverpool ... 91.0 38.0 43.0 \n", 1740 | "27 Real Madrid ... 84.0 88.0 90.0 \n", 1741 | "28 FC Bayern München ... 87.0 52.0 41.0 \n", 1742 | "29 Napoli ... 83.0 51.0 24.0 \n", 1743 | "... ... ... ... ... ... \n", 1744 | "18177 Blackpool ... 49.0 18.0 16.0 \n", 1745 | "18178 Dalkurd FF ... 28.0 16.0 11.0 \n", 1746 | "18179 Northampton Town ... 37.0 42.0 51.0 \n", 1747 | "18180 St. Johnstone FC ... 28.0 12.0 15.0 \n", 1748 | "18181 Bray Wanderers ... 38.0 43.0 49.0 \n", 1749 | "18182 Atlético Nacional ... 38.0 44.0 42.0 \n", 1750 | "18183 Cambridge United ... 56.0 15.0 15.0 \n", 1751 | "18184 Lincoln City ... 42.0 47.0 49.0 \n", 1752 | "18185 Derry City ... 43.0 39.0 39.0 \n", 1753 | "18186 Beijing Renhe FC ... 39.0 53.0 41.0 \n", 1754 | "18187 SpVgg Unterhaching ... 47.0 40.0 42.0 \n", 1755 | "18188 Newport County ... 46.0 33.0 38.0 \n", 1756 | "18189 SpVgg Unterhaching ... 37.0 28.0 15.0 \n", 1757 | "18190 Cambridge United ... 46.0 35.0 44.0 \n", 1758 | "18191 Cambridge United ... 36.0 45.0 42.0 \n", 1759 | "18192 Cambridge United ... 38.0 41.0 41.0 \n", 1760 | "18193 Unión Española ... 32.0 41.0 48.0 \n", 1761 | "18194 Lecce ... 23.0 6.0 10.0 \n", 1762 | "18195 Waterford FC ... 41.0 44.0 37.0 \n", 1763 | "18196 Júbilo Iwata ... 35.0 41.0 44.0 \n", 1764 | "18197 Cork City ... 52.0 41.0 47.0 \n", 1765 | "18198 Burton Albion ... 34.0 15.0 11.0 \n", 1766 | "18199 Vancouver Whitecaps FC ... 40.0 48.0 49.0 \n", 1767 | "18200 Swindon Town ... 50.0 15.0 17.0 \n", 1768 | "18201 Waterford FC ... 43.0 44.0 47.0 \n", 1769 | "18202 Crewe Alexandra ... 45.0 40.0 48.0 \n", 1770 | "18203 Trelleborgs FF ... 42.0 22.0 15.0 \n", 1771 | "18204 Cambridge United ... 41.0 32.0 13.0 \n", 1772 | "18205 Tranmere Rovers ... 46.0 20.0 25.0 \n", 1773 | "18206 Tranmere Rovers ... 43.0 40.0 43.0 \n", 1774 | "\n", 1775 | " SlidingTackle GKDiving GKHandling GKKicking GKPositioning \\\n", 1776 | "0 26.0 6.0 11.0 15.0 14.0 \n", 1777 | "1 23.0 7.0 11.0 15.0 14.0 \n", 1778 | "2 33.0 9.0 9.0 15.0 15.0 \n", 1779 | "3 13.0 90.0 85.0 87.0 88.0 \n", 1780 | "4 51.0 15.0 13.0 5.0 10.0 \n", 1781 | "5 22.0 11.0 12.0 6.0 8.0 \n", 1782 | "6 73.0 13.0 9.0 7.0 14.0 \n", 1783 | "7 38.0 27.0 25.0 31.0 33.0 \n", 1784 | "8 91.0 11.0 8.0 9.0 7.0 \n", 1785 | "9 18.0 86.0 92.0 78.0 88.0 \n", 1786 | "10 19.0 15.0 6.0 12.0 8.0 \n", 1787 | "11 69.0 10.0 11.0 13.0 7.0 \n", 1788 | "12 89.0 6.0 8.0 15.0 5.0 \n", 1789 | "13 29.0 6.0 15.0 7.0 6.0 \n", 1790 | "14 85.0 15.0 12.0 10.0 7.0 \n", 1791 | "15 20.0 5.0 4.0 4.0 5.0 \n", 1792 | "16 38.0 8.0 10.0 11.0 14.0 \n", 1793 | "17 48.0 14.0 8.0 14.0 13.0 \n", 1794 | "18 10.0 87.0 85.0 88.0 85.0 \n", 1795 | "19 16.0 85.0 91.0 72.0 86.0 \n", 1796 | "20 80.0 5.0 8.0 13.0 9.0 \n", 1797 | "21 39.0 12.0 5.0 13.0 13.0 \n", 1798 | "22 11.0 90.0 86.0 91.0 87.0 \n", 1799 | "23 12.0 13.0 15.0 6.0 11.0 \n", 1800 | "24 90.0 3.0 3.0 2.0 4.0 \n", 1801 | "25 32.0 13.0 5.0 7.0 11.0 \n", 1802 | "26 41.0 14.0 14.0 9.0 11.0 \n", 1803 | "27 87.0 13.0 14.0 16.0 12.0 \n", 1804 | "28 44.0 15.0 15.0 15.0 5.0 \n", 1805 | "29 22.0 8.0 4.0 14.0 9.0 \n", 1806 | "... ... ... ... ... ... \n", 1807 | "18177 11.0 6.0 9.0 11.0 7.0 \n", 1808 | "18178 10.0 47.0 46.0 50.0 45.0 \n", 1809 | "18179 49.0 14.0 11.0 7.0 11.0 \n", 1810 | "18180 16.0 45.0 49.0 50.0 50.0 \n", 1811 | "18181 45.0 8.0 10.0 12.0 9.0 \n", 1812 | "18182 46.0 9.0 15.0 15.0 8.0 \n", 1813 | "18183 13.0 45.0 48.0 44.0 49.0 \n", 1814 | "18184 53.0 12.0 5.0 12.0 14.0 \n", 1815 | "18185 48.0 6.0 11.0 9.0 5.0 \n", 1816 | "18186 51.0 15.0 7.0 14.0 6.0 \n", 1817 | "18187 42.0 13.0 12.0 11.0 15.0 \n", 1818 | "18188 41.0 5.0 12.0 8.0 13.0 \n", 1819 | "18189 22.0 15.0 5.0 14.0 12.0 \n", 1820 | "18190 47.0 13.0 7.0 14.0 10.0 \n", 1821 | "18191 46.0 15.0 13.0 6.0 14.0 \n", 1822 | "18192 44.0 11.0 11.0 8.0 12.0 \n", 1823 | "18193 48.0 6.0 10.0 6.0 12.0 \n", 1824 | "18194 11.0 52.0 52.0 52.0 40.0 \n", 1825 | "18195 48.0 13.0 14.0 12.0 7.0 \n", 1826 | "18196 54.0 10.0 12.0 6.0 11.0 \n", 1827 | "18197 38.0 13.0 6.0 9.0 10.0 \n", 1828 | "18198 13.0 46.0 52.0 58.0 42.0 \n", 1829 | "18199 49.0 7.0 7.0 9.0 14.0 \n", 1830 | "18200 14.0 11.0 15.0 12.0 12.0 \n", 1831 | "18201 53.0 9.0 10.0 9.0 11.0 \n", 1832 | "18202 47.0 10.0 13.0 7.0 8.0 \n", 1833 | "18203 19.0 10.0 9.0 9.0 5.0 \n", 1834 | "18204 11.0 6.0 5.0 10.0 6.0 \n", 1835 | "18205 27.0 14.0 6.0 14.0 8.0 \n", 1836 | "18206 50.0 10.0 15.0 9.0 12.0 \n", 1837 | "\n", 1838 | " GKReflexes Release Clause \n", 1839 | "0 8.0 €226.5M \n", 1840 | "1 11.0 €127.1M \n", 1841 | "2 11.0 €228.1M \n", 1842 | "3 94.0 €138.6M \n", 1843 | "4 13.0 €196.4M \n", 1844 | "5 8.0 €172.1M \n", 1845 | "6 9.0 €137.4M \n", 1846 | "7 37.0 €164M \n", 1847 | "8 11.0 €104.6M \n", 1848 | "9 89.0 €144.5M \n", 1849 | "10 10.0 €127.1M \n", 1850 | "11 10.0 €156.8M \n", 1851 | "12 15.0 €90.2M \n", 1852 | "13 12.0 €111M \n", 1853 | "14 10.0 €121.3M \n", 1854 | "15 8.0 €153.5M \n", 1855 | "16 11.0 €160.7M \n", 1856 | "17 14.0 €165.8M \n", 1857 | "18 90.0 €123.3M \n", 1858 | "19 88.0 €113.7M \n", 1859 | "20 13.0 €105.6M \n", 1860 | "21 10.0 €111M \n", 1861 | "22 87.0 €62.7M \n", 1862 | "23 14.0 €119.3M \n", 1863 | "24 3.0 €44.6M \n", 1864 | "25 6.0 €166.1M \n", 1865 | "26 14.0 €137.3M \n", 1866 | "27 12.0 €126.4M \n", 1867 | "28 14.0 NaN \n", 1868 | "29 10.0 €105.4M \n", 1869 | "... ... ... \n", 1870 | "18177 12.0 €193K \n", 1871 | "18178 51.0 €94K \n", 1872 | "18179 8.0 €119K \n", 1873 | "18180 45.0 €78K \n", 1874 | "18181 10.0 €109K \n", 1875 | "18182 6.0 €101K \n", 1876 | "18183 46.0 NaN \n", 1877 | "18184 15.0 €78K \n", 1878 | "18185 8.0 €88K \n", 1879 | "18186 8.0 €167K \n", 1880 | "18187 12.0 €66K \n", 1881 | "18188 10.0 €143K \n", 1882 | "18189 8.0 €125K \n", 1883 | "18190 8.0 €165K \n", 1884 | "18191 12.0 €119K \n", 1885 | "18192 13.0 €119K \n", 1886 | "18193 11.0 €99K \n", 1887 | "18194 44.0 €109K \n", 1888 | "18195 13.0 €153K \n", 1889 | "18196 8.0 €113K \n", 1890 | "18197 15.0 €88K \n", 1891 | "18198 48.0 €165K \n", 1892 | "18199 15.0 €175K \n", 1893 | "18200 11.0 €143K \n", 1894 | "18201 13.0 €153K \n", 1895 | "18202 9.0 €143K \n", 1896 | "18203 12.0 €113K \n", 1897 | "18204 13.0 €165K \n", 1898 | "18205 9.0 €143K \n", 1899 | "18206 9.0 €165K \n", 1900 | "\n", 1901 | "[18207 rows x 89 columns]" 1902 | ] 1903 | }, 1904 | "execution_count": 7, 1905 | "metadata": {}, 1906 | "output_type": "execute_result" 1907 | } 1908 | ], 1909 | "source": [ 1910 | "import pandas as pd\n", 1911 | "data_frame = pd.read_csv('data.csv')\n", 1912 | "data_frame " 1913 | ] 1914 | }, 1915 | { 1916 | "cell_type": "code", 1917 | "execution_count": 4, 1918 | "metadata": {}, 1919 | "outputs": [ 1920 | { 1921 | "data": { 1922 | "text/html": [ 1923 | "
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Unnamed: 0IDAgeOverallPotentialSpecialInternational ReputationWeak FootSkill MovesJersey Number...PenaltiesComposureMarkingStandingTackleSlidingTackleGKDivingGKHandlingGKKickingGKPositioningGKReflexes
count18207.00000018207.00000018207.00000018207.00000018207.00000018207.00000018159.00000018159.00000018159.00000018147.000000...18159.00000018159.00000018159.00000018159.00000018159.00000018159.00000018159.00000018159.00000018159.00000018159.000000
mean9103.000000214298.33860625.12220666.23869971.3072991597.8099081.1132222.9472992.36130819.546096...48.54859858.64827447.28162347.69783645.66143516.61622316.39159616.23206116.38889816.710887
std5256.05251129965.2442044.6699436.9089306.136496272.5860160.3940310.6604560.75616415.947765...15.70405311.43613319.90439721.66400421.28913517.69534916.90690016.50286417.03466917.955119
min0.00000016.00000016.00000046.00000048.000000731.0000001.0000001.0000001.0000001.000000...5.0000003.0000003.0000002.0000003.0000001.0000001.0000001.0000001.0000001.000000
25%4551.500000200315.50000021.00000062.00000067.0000001457.0000001.0000003.0000002.0000008.000000...39.00000051.00000030.00000027.00000024.0000008.0000008.0000008.0000008.0000008.000000
50%9103.000000221759.00000025.00000066.00000071.0000001635.0000001.0000003.0000002.00000017.000000...49.00000060.00000053.00000055.00000052.00000011.00000011.00000011.00000011.00000011.000000
75%13654.500000236529.50000028.00000071.00000075.0000001787.0000001.0000003.0000003.00000026.000000...60.00000067.00000064.00000066.00000064.00000014.00000014.00000014.00000014.00000014.000000
max18206.000000246620.00000045.00000094.00000095.0000002346.0000005.0000005.0000005.00000099.000000...92.00000096.00000094.00000093.00000091.00000090.00000092.00000091.00000090.00000094.000000
\n", 2159 | "

8 rows × 44 columns

\n", 2160 | "
" 2161 | ], 2162 | "text/plain": [ 2163 | " Unnamed: 0 ID Age Overall Potential \\\n", 2164 | "count 18207.000000 18207.000000 18207.000000 18207.000000 18207.000000 \n", 2165 | "mean 9103.000000 214298.338606 25.122206 66.238699 71.307299 \n", 2166 | "std 5256.052511 29965.244204 4.669943 6.908930 6.136496 \n", 2167 | "min 0.000000 16.000000 16.000000 46.000000 48.000000 \n", 2168 | "25% 4551.500000 200315.500000 21.000000 62.000000 67.000000 \n", 2169 | "50% 9103.000000 221759.000000 25.000000 66.000000 71.000000 \n", 2170 | "75% 13654.500000 236529.500000 28.000000 71.000000 75.000000 \n", 2171 | "max 18206.000000 246620.000000 45.000000 94.000000 95.000000 \n", 2172 | "\n", 2173 | " Special International Reputation Weak Foot Skill Moves \\\n", 2174 | "count 18207.000000 18159.000000 18159.000000 18159.000000 \n", 2175 | "mean 1597.809908 1.113222 2.947299 2.361308 \n", 2176 | "std 272.586016 0.394031 0.660456 0.756164 \n", 2177 | "min 731.000000 1.000000 1.000000 1.000000 \n", 2178 | "25% 1457.000000 1.000000 3.000000 2.000000 \n", 2179 | "50% 1635.000000 1.000000 3.000000 2.000000 \n", 2180 | "75% 1787.000000 1.000000 3.000000 3.000000 \n", 2181 | "max 2346.000000 5.000000 5.000000 5.000000 \n", 2182 | "\n", 2183 | " Jersey Number ... Penalties Composure Marking \\\n", 2184 | "count 18147.000000 ... 18159.000000 18159.000000 18159.000000 \n", 2185 | "mean 19.546096 ... 48.548598 58.648274 47.281623 \n", 2186 | "std 15.947765 ... 15.704053 11.436133 19.904397 \n", 2187 | "min 1.000000 ... 5.000000 3.000000 3.000000 \n", 2188 | "25% 8.000000 ... 39.000000 51.000000 30.000000 \n", 2189 | "50% 17.000000 ... 49.000000 60.000000 53.000000 \n", 2190 | "75% 26.000000 ... 60.000000 67.000000 64.000000 \n", 2191 | "max 99.000000 ... 92.000000 96.000000 94.000000 \n", 2192 | "\n", 2193 | " StandingTackle SlidingTackle GKDiving GKHandling \\\n", 2194 | "count 18159.000000 18159.000000 18159.000000 18159.000000 \n", 2195 | "mean 47.697836 45.661435 16.616223 16.391596 \n", 2196 | "std 21.664004 21.289135 17.695349 16.906900 \n", 2197 | "min 2.000000 3.000000 1.000000 1.000000 \n", 2198 | "25% 27.000000 24.000000 8.000000 8.000000 \n", 2199 | "50% 55.000000 52.000000 11.000000 11.000000 \n", 2200 | "75% 66.000000 64.000000 14.000000 14.000000 \n", 2201 | "max 93.000000 91.000000 90.000000 92.000000 \n", 2202 | "\n", 2203 | " GKKicking GKPositioning GKReflexes \n", 2204 | "count 18159.000000 18159.000000 18159.000000 \n", 2205 | "mean 16.232061 16.388898 16.710887 \n", 2206 | "std 16.502864 17.034669 17.955119 \n", 2207 | "min 1.000000 1.000000 1.000000 \n", 2208 | "25% 8.000000 8.000000 8.000000 \n", 2209 | "50% 11.000000 11.000000 11.000000 \n", 2210 | "75% 14.000000 14.000000 14.000000 \n", 2211 | "max 91.000000 90.000000 94.000000 \n", 2212 | "\n", 2213 | "[8 rows x 44 columns]" 2214 | ] 2215 | }, 2216 | "execution_count": 4, 2217 | "metadata": {}, 2218 | "output_type": "execute_result" 2219 | } 2220 | ], 2221 | "source": [ 2222 | "data_frame.describe()" 2223 | ] 2224 | }, 2225 | { 2226 | "cell_type": "code", 2227 | "execution_count": 5, 2228 | "metadata": {}, 2229 | "outputs": [ 2230 | { 2231 | "data": { 2232 | "text/plain": [ 2233 | "array([[0, 158023, 'L. Messi', ..., 14.0, 8.0, '€226.5M'],\n", 2234 | " [1, 20801, 'Cristiano Ronaldo', ..., 14.0, 11.0, '€127.1M'],\n", 2235 | " [2, 190871, 'Neymar Jr', ..., 15.0, 11.0, '€228.1M'],\n", 2236 | " ...,\n", 2237 | " [18204, 241638, 'B. Worman', ..., 6.0, 13.0, '€165K'],\n", 2238 | " [18205, 246268, 'D. Walker-Rice', ..., 8.0, 9.0, '€143K'],\n", 2239 | " [18206, 246269, 'G. Nugent', ..., 12.0, 9.0, '€165K']],\n", 2240 | " dtype=object)" 2241 | ] 2242 | }, 2243 | "execution_count": 5, 2244 | "metadata": {}, 2245 | "output_type": "execute_result" 2246 | } 2247 | ], 2248 | "source": [ 2249 | "data_frame.values" 2250 | ] 2251 | }, 2252 | { 2253 | "cell_type": "code", 2254 | "execution_count": 16, 2255 | "metadata": {}, 2256 | "outputs": [ 2257 | { 2258 | "data": { 2259 | "text/html": [ 2260 | "
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NameWageValuedifference
2Neymar Jr290000.0118500000.0118210000.0
0L. Messi565000.0110500000.0109935000.0
4K. De Bruyne355000.0102000000.0101645000.0
5E. Hazard340000.093000000.092660000.0
15P. Dybala205000.089000000.088795000.0
16H. Kane205000.083500000.083295000.0
25K. Mbappé100000.081000000.080900000.0
7L. Suárez455000.080000000.079545000.0
17A. Griezmann145000.078000000.077855000.0
10R. Lewandowski205000.077000000.076795000.0
1Cristiano Ronaldo405000.077000000.076595000.0
11T. Kroos355000.076500000.076145000.0
31C. Eriksen205000.073500000.073295000.0
30Isco315000.073500000.073185000.0
3De Gea260000.072000000.071740000.0
26M. Salah255000.069500000.069245000.0
28J. Rodríguez315000.069500000.069185000.0
32Coutinho340000.069500000.069160000.0
9J. Oblak94000.068000000.067906000.0
6L. Modrić420000.067000000.066580000.0
43M. Icardi130000.064500000.064370000.0
23S. Agüero300000.064500000.064200000.0
45P. Pogba210000.064000000.063790000.0
14N. Kanté225000.063000000.062775000.0
47R. Lukaku230000.062500000.062270000.0
29L. Insigne165000.062000000.061835000.0
55L. Sané195000.061000000.060805000.0
21E. Cavani200000.060000000.059800000.0
13David Silva285000.060000000.059715000.0
36G. Bale355000.060000000.059645000.0
...............
5272C. Deac0.00.00.0
14054P. Halder0.00.00.0
4823P. Anton0.00.00.0
5245R. Cardozo0.00.00.0
2984M. Borjan0.00.00.0
6841N. Bancu0.00.00.0
7990B. Mitrev0.00.00.0
5126L. Cáceda0.00.00.0
9568C. Gonzáles0.00.00.0
8057D. Mendiseca0.00.00.0
8061S. Gbohouo0.00.00.0
11247A. Cicâldău0.00.00.0
2999K. Rausch0.00.00.0
17436D. Lalhlimpuia0.00.00.0
8273D. Furman0.00.00.0
16539L. Lalruatthara0.00.00.0
3037Y. Banana0.00.00.0
5082J. Paredes0.00.00.0
4945H. Vaca0.00.00.0
17672R. Kawai1000.00.0-1000.0
10356F. Kippe1000.00.0-1000.0
12453W. Díaz1000.00.0-1000.0
14129Y. Nakazawa1000.00.0-1000.0
18183K. Pilkington1000.00.0-1000.0
17726T. Warner1000.00.0-1000.0
17752S. Phillips1000.00.0-1000.0
12192H. Sulaimani3000.00.0-3000.0
3550S. Nakamura4000.00.0-4000.0
4228B. Nivet5000.00.0-5000.0
864Hilton18000.00.0-18000.0
\n", 2714 | "

18207 rows × 4 columns

\n", 2715 | "
" 2716 | ], 2717 | "text/plain": [ 2718 | " Name Wage Value difference\n", 2719 | "2 Neymar Jr 290000.0 118500000.0 118210000.0\n", 2720 | "0 L. Messi 565000.0 110500000.0 109935000.0\n", 2721 | "4 K. De Bruyne 355000.0 102000000.0 101645000.0\n", 2722 | "5 E. Hazard 340000.0 93000000.0 92660000.0\n", 2723 | "15 P. Dybala 205000.0 89000000.0 88795000.0\n", 2724 | "16 H. Kane 205000.0 83500000.0 83295000.0\n", 2725 | "25 K. Mbappé 100000.0 81000000.0 80900000.0\n", 2726 | "7 L. Suárez 455000.0 80000000.0 79545000.0\n", 2727 | "17 A. Griezmann 145000.0 78000000.0 77855000.0\n", 2728 | "10 R. Lewandowski 205000.0 77000000.0 76795000.0\n", 2729 | "1 Cristiano Ronaldo 405000.0 77000000.0 76595000.0\n", 2730 | "11 T. Kroos 355000.0 76500000.0 76145000.0\n", 2731 | "31 C. Eriksen 205000.0 73500000.0 73295000.0\n", 2732 | "30 Isco 315000.0 73500000.0 73185000.0\n", 2733 | "3 De Gea 260000.0 72000000.0 71740000.0\n", 2734 | "26 M. Salah 255000.0 69500000.0 69245000.0\n", 2735 | "28 J. Rodríguez 315000.0 69500000.0 69185000.0\n", 2736 | "32 Coutinho 340000.0 69500000.0 69160000.0\n", 2737 | "9 J. Oblak 94000.0 68000000.0 67906000.0\n", 2738 | "6 L. Modrić 420000.0 67000000.0 66580000.0\n", 2739 | "43 M. Icardi 130000.0 64500000.0 64370000.0\n", 2740 | "23 S. Agüero 300000.0 64500000.0 64200000.0\n", 2741 | "45 P. Pogba 210000.0 64000000.0 63790000.0\n", 2742 | "14 N. Kanté 225000.0 63000000.0 62775000.0\n", 2743 | "47 R. Lukaku 230000.0 62500000.0 62270000.0\n", 2744 | "29 L. Insigne 165000.0 62000000.0 61835000.0\n", 2745 | "55 L. Sané 195000.0 61000000.0 60805000.0\n", 2746 | "21 E. Cavani 200000.0 60000000.0 59800000.0\n", 2747 | "13 David Silva 285000.0 60000000.0 59715000.0\n", 2748 | "36 G. Bale 355000.0 60000000.0 59645000.0\n", 2749 | "... ... ... ... ...\n", 2750 | "5272 C. Deac 0.0 0.0 0.0\n", 2751 | "14054 P. Halder 0.0 0.0 0.0\n", 2752 | "4823 P. Anton 0.0 0.0 0.0\n", 2753 | "5245 R. Cardozo 0.0 0.0 0.0\n", 2754 | "2984 M. Borjan 0.0 0.0 0.0\n", 2755 | "6841 N. Bancu 0.0 0.0 0.0\n", 2756 | "7990 B. Mitrev 0.0 0.0 0.0\n", 2757 | "5126 L. Cáceda 0.0 0.0 0.0\n", 2758 | "9568 C. Gonzáles 0.0 0.0 0.0\n", 2759 | "8057 D. Mendiseca 0.0 0.0 0.0\n", 2760 | "8061 S. Gbohouo 0.0 0.0 0.0\n", 2761 | "11247 A. Cicâldău 0.0 0.0 0.0\n", 2762 | "2999 K. Rausch 0.0 0.0 0.0\n", 2763 | "17436 D. Lalhlimpuia 0.0 0.0 0.0\n", 2764 | "8273 D. Furman 0.0 0.0 0.0\n", 2765 | "16539 L. Lalruatthara 0.0 0.0 0.0\n", 2766 | "3037 Y. Banana 0.0 0.0 0.0\n", 2767 | "5082 J. Paredes 0.0 0.0 0.0\n", 2768 | "4945 H. Vaca 0.0 0.0 0.0\n", 2769 | "17672 R. Kawai 1000.0 0.0 -1000.0\n", 2770 | "10356 F. Kippe 1000.0 0.0 -1000.0\n", 2771 | "12453 W. Díaz 1000.0 0.0 -1000.0\n", 2772 | "14129 Y. Nakazawa 1000.0 0.0 -1000.0\n", 2773 | "18183 K. Pilkington 1000.0 0.0 -1000.0\n", 2774 | "17726 T. Warner 1000.0 0.0 -1000.0\n", 2775 | "17752 S. Phillips 1000.0 0.0 -1000.0\n", 2776 | "12192 H. Sulaimani 3000.0 0.0 -3000.0\n", 2777 | "3550 S. Nakamura 4000.0 0.0 -4000.0\n", 2778 | "4228 B. Nivet 5000.0 0.0 -5000.0\n", 2779 | "864 Hilton 18000.0 0.0 -18000.0\n", 2780 | "\n", 2781 | "[18207 rows x 4 columns]" 2782 | ] 2783 | }, 2784 | "execution_count": 16, 2785 | "metadata": {}, 2786 | "output_type": "execute_result" 2787 | } 2788 | ], 2789 | "source": [ 2790 | "df1 = pd.DataFrame(data_frame, columns=['Name', 'Wage', 'Value'])\n", 2791 | "def value_to_float(x):\n", 2792 | " if type(x) == float or type(x) == int:\n", 2793 | " return x\n", 2794 | " if 'K' in x:\n", 2795 | " if len(x) > 1:\n", 2796 | " return float(x.replace('K', '')) * 1000\n", 2797 | " return 1000.0\n", 2798 | " if 'M' in x:\n", 2799 | " if len(x) > 1:\n", 2800 | " return float(x.replace('M', '')) * 1000000\n", 2801 | " return 1000000.0\n", 2802 | " if 'B' in x:\n", 2803 | " return float(x.replace('B', '')) * 1000000000\n", 2804 | " return 0.0\n", 2805 | "\n", 2806 | "wage = df1['Wage'].replace('[\\€,]', '', regex=True).apply(value_to_float)\n", 2807 | "value = df1['Value'].replace('[\\€,]', '', regex=True).apply(value_to_float)\n", 2808 | "\n", 2809 | "df1['Wage'] = wage\n", 2810 | "df1['Value'] = value\n", 2811 | "\n", 2812 | "df1['difference'] = df1['Value'] - df1['Wage']\n", 2813 | "df1.sort_values('difference', ascending=False)\n" 2814 | ] 2815 | }, 2816 | { 2817 | "cell_type": "code", 2818 | "execution_count": 18, 2819 | "metadata": {}, 2820 | "outputs": [ 2821 | { 2822 | "data": { 2823 | "text/plain": [ 2824 | "" 2825 | ] 2826 | }, 2827 | "execution_count": 18, 2828 | "metadata": {}, 2829 | "output_type": "execute_result" 2830 | }, 2831 | { 2832 | "data": { 2833 | "image/png": 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", 2834 | "text/plain": [ 2835 | "
" 2836 | ] 2837 | }, 2838 | "metadata": {}, 2839 | "output_type": "display_data" 2840 | } 2841 | ], 2842 | "source": [ 2843 | "import seaborn as sns\n", 2844 | "sns.set()\n", 2845 | "\n", 2846 | "graph = sns.scatterplot(x='Wage', y='Value', data=df1)\n", 2847 | "graph" 2848 | ] 2849 | }, 2850 | { 2851 | "cell_type": "code", 2852 | "execution_count": 28, 2853 | "metadata": {}, 2854 | "outputs": [], 2855 | "source": [ 2856 | "from bokeh.plotting import figure,show\n", 2857 | "from bokeh.models import HoverTool\n", 2858 | "\n", 2859 | "TOOLTIPS = HoverTool(tooltips=[\n", 2860 | " (\"index\", \"$index\"),\n", 2861 | " (\"(Wage,Value)\", \"(@Wage, @Value)\"),\n", 2862 | " (\"Name\", \"@Name\")]\n", 2863 | ")\n", 2864 | "\n", 2865 | "p = figure(title=\"Soccer 2019\", x_axis_label='Wage', y_axis_label='Value', plot_width=700, plot_height=700, tools=[TOOLTIPS])\n", 2866 | "p.circle('Wage', 'Value', size=10, source=df1)\n", 2867 | "show(p)" 2868 | ] 2869 | }, 2870 | { 2871 | "cell_type": "code", 2872 | "execution_count": null, 2873 | "metadata": {}, 2874 | "outputs": [], 2875 | "source": [] 2876 | } 2877 | ], 2878 | "metadata": { 2879 | "kernelspec": { 2880 | "display_name": "Python 3", 2881 | "language": "python", 2882 | "name": "python3" 2883 | }, 2884 | "language_info": { 2885 | "codemirror_mode": { 2886 | "name": "ipython", 2887 | "version": 3 2888 | }, 2889 | "file_extension": ".py", 2890 | "mimetype": "text/x-python", 2891 | "name": "python", 2892 | "nbconvert_exporter": "python", 2893 | "pygments_lexer": "ipython3", 2894 | "version": "3.7.1" 2895 | } 2896 | }, 2897 | "nbformat": 4, 2898 | "nbformat_minor": 2 2899 | } 2900 | --------------------------------------------------------------------------------