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meneguinha/right_left_foot.ipynb

Created August 25, 2024 19:56
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right_left_foot.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/meneguinha/31b4100f9ef510c873e891196eed29b8/right_left_foot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"id": "36f2e498",
"metadata": {
"id": "36f2e498"
},
"source": [
"To use this Jupyter Notebook, dowload this dataset: https://www.kaggle.com/hugomathien/soccer"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "702d136c",
"metadata": {
"id": "702d136c"
},
"outputs": [],
"source": [
"import sqlite3\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "78c31334",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "78c31334",
"outputId": "54984a22-97e8-4352-f2ff-ec549afa959f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"done\n"
]
}
],
"source": [
"cnx = sqlite3.connect('/content/database.sqlite')\n",
"df = pd.read_sql_query(\"SELECT * FROM Player_Attributes\", cnx)\n",
"print(\"done\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e68e459d",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "e68e459d",
"outputId": "8bf06e64-f020-487a-b857-8607326254b2"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['id', 'player_fifa_api_id', 'player_api_id', 'date', 'overall_rating',\n",
" 'potential', 'preferred_foot', 'attacking_work_rate',\n",
" 'defensive_work_rate', 'crossing', 'finishing', 'heading_accuracy',\n",
" 'short_passing', 'volleys', 'dribbling', 'curve', 'free_kick_accuracy',\n",
" 'long_passing', 'ball_control', 'acceleration', 'sprint_speed',\n",
" 'agility', 'reactions', 'balance', 'shot_power', 'jumping', 'stamina',\n",
" 'strength', 'long_shots', 'aggression', 'interceptions', 'positioning',\n",
" 'vision', 'penalties', 'marking', 'standing_tackle', 'sliding_tackle',\n",
" 'gk_diving', 'gk_handling', 'gk_kicking', 'gk_positioning',\n",
" 'gk_reflexes'],\n",
" dtype='object')"
]
},
"metadata": {},
"execution_count": 3
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "05e94041",
"metadata": {
"id": "05e94041"
},
"outputs": [],
"source": [
"df = df.dropna()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b83c00b8",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 791
},
"id": "b83c00b8",
"outputId": "6a440d96-9f77-4d37-d810-60b2c6ddad25"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" id player_fifa_api_id player_api_id date \\\n",
"0 1 218353 505942 2016-02-18 00:00:00 \n",
"1 2 218353 505942 2015-11-19 00:00:00 \n",
"2 3 218353 505942 2015-09-21 00:00:00 \n",
"3 4 218353 505942 2015-03-20 00:00:00 \n",
"4 5 218353 505942 2007-02-22 00:00:00 \n",
"... ... ... ... ... \n",
"183973 183974 102359 39902 2009-08-30 00:00:00 \n",
"183974 183975 102359 39902 2009-02-22 00:00:00 \n",
"183975 183976 102359 39902 2008-08-30 00:00:00 \n",
"183976 183977 102359 39902 2007-08-30 00:00:00 \n",
"183977 183978 102359 39902 2007-02-22 00:00:00 \n",
"\n",
" overall_rating potential preferred_foot attacking_work_rate \\\n",
"0 67.0 71.0 right medium \n",
"1 67.0 71.0 right medium \n",
"2 62.0 66.0 right medium \n",
"3 61.0 65.0 right medium \n",
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"... ... ... ... ... \n",
"183973 83.0 85.0 right medium \n",
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"183975 77.0 80.0 right medium \n",
"183976 78.0 81.0 right medium \n",
"183977 80.0 81.0 right medium \n",
"\n",
" defensive_work_rate crossing ... vision penalties marking \\\n",
"0 medium 49.0 ... 54.0 48.0 65.0 \n",
"1 medium 49.0 ... 54.0 48.0 65.0 \n",
"2 medium 49.0 ... 54.0 48.0 65.0 \n",
"3 medium 48.0 ... 53.0 47.0 62.0 \n",
"4 medium 48.0 ... 53.0 47.0 62.0 \n",
"... ... ... ... ... ... ... \n",
"183973 low 84.0 ... 88.0 83.0 22.0 \n",
"183974 low 74.0 ... 88.0 70.0 32.0 \n",
"183975 low 74.0 ... 88.0 70.0 32.0 \n",
"183976 low 74.0 ... 88.0 53.0 28.0 \n",
"183977 low 74.0 ... 88.0 53.0 38.0 \n",
"\n",
" standing_tackle sliding_tackle gk_diving gk_handling gk_kicking \\\n",
"0 69.0 69.0 6.0 11.0 10.0 \n",
"1 69.0 69.0 6.0 11.0 10.0 \n",
"2 66.0 69.0 6.0 11.0 10.0 \n",
"3 63.0 66.0 5.0 10.0 9.0 \n",
"4 63.0 66.0 5.0 10.0 9.0 \n",
"... ... ... ... ... ... \n",
"183973 31.0 30.0 9.0 20.0 84.0 \n",
"183974 31.0 30.0 9.0 20.0 73.0 \n",
"183975 31.0 30.0 9.0 20.0 73.0 \n",
"183976 32.0 30.0 9.0 20.0 73.0 \n",
"183977 32.0 30.0 9.0 9.0 78.0 \n",
"\n",
" gk_positioning gk_reflexes \n",
"0 8.0 8.0 \n",
"1 8.0 8.0 \n",
"2 8.0 8.0 \n",
"3 7.0 7.0 \n",
"4 7.0 7.0 \n",
"... ... ... \n",
"183973 20.0 20.0 \n",
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"183975 20.0 20.0 \n",
"183976 20.0 20.0 \n",
"183977 7.0 15.0 \n",
"\n",
"[180354 rows x 42 columns]"
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" <th></th>\n",
" <th>id</th>\n",
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" <td>low</td>\n",
" <td>84.0</td>\n",
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" <th>183974</th>\n",
" <td>183975</td>\n",
" <td>102359</td>\n",
" <td>39902</td>\n",
" <td>2009-02-22 00:00:00</td>\n",
" <td>78.0</td>\n",
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" <td>right</td>\n",
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" <td>low</td>\n",
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" <td>31.0</td>\n",
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" <td>73.0</td>\n",
" <td>20.0</td>\n",
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" <tr>\n",
" <th>183975</th>\n",
" <td>183976</td>\n",
" <td>102359</td>\n",
" <td>39902</td>\n",
" <td>2008-08-30 00:00:00</td>\n",
" <td>77.0</td>\n",
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" <td>right</td>\n",
" <td>medium</td>\n",
" <td>low</td>\n",
" <td>74.0</td>\n",
" <td>...</td>\n",
" <td>88.0</td>\n",
" <td>70.0</td>\n",
" <td>32.0</td>\n",
" <td>31.0</td>\n",
" <td>30.0</td>\n",
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" <td>20.0</td>\n",
" <td>73.0</td>\n",
" <td>20.0</td>\n",
" <td>20.0</td>\n",
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" <th>183976</th>\n",
" <td>183977</td>\n",
" <td>102359</td>\n",
" <td>39902</td>\n",
" <td>2007-08-30 00:00:00</td>\n",
" <td>78.0</td>\n",
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" <td>right</td>\n",
" <td>medium</td>\n",
" <td>low</td>\n",
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" <td>...</td>\n",
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" <td>28.0</td>\n",
" <td>32.0</td>\n",
" <td>30.0</td>\n",
" <td>9.0</td>\n",
" <td>20.0</td>\n",
" <td>73.0</td>\n",
" <td>20.0</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183977</th>\n",
" <td>183978</td>\n",
" <td>102359</td>\n",
" <td>39902</td>\n",
" <td>2007-02-22 00:00:00</td>\n",
" <td>80.0</td>\n",
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" <td>right</td>\n",
" <td>medium</td>\n",
" <td>low</td>\n",
" <td>74.0</td>\n",
" <td>...</td>\n",
" <td>88.0</td>\n",
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" <td>30.0</td>\n",
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" <td>9.0</td>\n",
" <td>78.0</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
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"type": "dataframe",
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},
"metadata": {},
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]
},
{
"cell_type": "code",
"execution_count": null,
"id": "507ef04d",
"metadata": {
"id": "507ef04d"
},
"outputs": [],
"source": [
"filtered_df = df[['player_fifa_api_id','preferred_foot', 'dribbling', 'agility', 'balance', 'vision', 'positioning', 'ball_control','gk_reflexes' ]].copy()"
]
},
{
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"id": "fd64a0c3",
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{
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" player_fifa_api_id preferred_foot dribbling agility balance \\\n",
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"... ... ... ... ... ... \n",
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"\n",
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"183972 88.0 84.0 85.0 5.0 \n",
"183977 88.0 68.0 91.0 15.0 \n",
"\n",
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" <td>84.0</td>\n",
" <td>5.0</td>\n",
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" <th>183972</th>\n",
" <td>102359</td>\n",
" <td>right</td>\n",
" <td>84.0</td>\n",
" <td>77.0</td>\n",
" <td>73.0</td>\n",
" <td>88.0</td>\n",
" <td>84.0</td>\n",
" <td>85.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183977</th>\n",
" <td>102359</td>\n",
" <td>right</td>\n",
" <td>87.0</td>\n",
" <td>77.0</td>\n",
" <td>73.0</td>\n",
" <td>88.0</td>\n",
" <td>68.0</td>\n",
" <td>91.0</td>\n",
" <td>15.0</td>\n",
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"metadata": {},
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"source": [
"gk_filter = filtered_df['gk_reflexes'] < 20\n",
"filtered_df = filtered_df[gk_filter]\n",
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"cell_type": "code",
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"id": "e49bfd93",
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"height": 366
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"id": "e49bfd93",
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{
"output_type": "execute_result",
"data": {
"text/plain": [
"player_fifa_api_id False\n",
"preferred_foot False\n",
"dribbling False\n",
"agility False\n",
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"left_foot_filter = filtered_df['preferred_foot']=='left'"
]
},
{
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"id": "92d7dbee",
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" player_fifa_api_id preferred_foot dribbling agility balance \\\n",
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"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "filtered_df_left",
"summary": "{\n \"name\": \"filtered_df_left\",\n \"rows\": 37143,\n \"fields\": [\n {\n \"column\": \"player_fifa_api_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 48640,\n \"min\": 2,\n \"max\": 233911,\n \"num_unique_values\": 2793,\n \"samples\": [\n 149306,\n 186591,\n 177905\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"preferred_foot\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"left\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"dribbling\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12.549756750680073,\n \"min\": 10.0,\n \"max\": 97.0,\n \"num_unique_values\": 87,\n \"samples\": [\n 90.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"agility\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11.804197031498157,\n \"min\": 21.0,\n \"max\": 96.0,\n \"num_unique_values\": 75,\n \"samples\": [\n 74.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"balance\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12.128995827951854,\n \"min\": 20.0,\n \"max\": 96.0,\n \"num_unique_values\": 74,\n \"samples\": [\n 73.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vision\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12.64007495701645,\n \"min\": 18.0,\n \"max\": 94.0,\n \"num_unique_values\": 77,\n \"samples\": [\n 58.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"positioning\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14.754316317451018,\n \"min\": 11.0,\n \"max\": 93.0,\n \"num_unique_values\": 83,\n \"samples\": [\n 54.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ball_control\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9.38128723618305,\n \"min\": 25.0,\n \"max\": 97.0,\n \"num_unique_values\": 68,\n \"samples\": [\n 42.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gk_reflexes\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.1679606838239196,\n \"min\": 1.0,\n \"max\": 19.0,\n \"num_unique_values\": 19,\n \"samples\": [\n 12.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 15
}
],
"source": [
"filtered_df_left"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d7dd1e16",
"metadata": {
"id": "d7dd1e16"
},
"outputs": [],
"source": [
"right_foot_filter = filtered_df['preferred_foot']=='right'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "98cbbe8d",
"metadata": {
"id": "98cbbe8d"
},
"outputs": [],
"source": [
"filtered_df_right = filtered_df[right_foot_filter]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7f1c3f08",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 444
},
"id": "7f1c3f08",
"outputId": "6c9d250a-3aec-436f-89fa-bab2fd1a725c"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
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" player_fifa_api_id preferred_foot dribbling agility balance \\\n",
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"183977 88.0 68.0 91.0 15.0 \n",
"\n",
"[108273 rows x 9 columns]"
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},
"metadata": {},
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],
"source": [
"filtered_df_right"
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},
{
"cell_type": "code",
"execution_count": null,
"id": "0d846e13",
"metadata": {
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"id": "0d846e13",
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{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7d1839af4580>"
]
},
"metadata": {},
"execution_count": 19
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],
"source": [
"filtered_df_left.groupby('player_fifa_api_id')"
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},
{
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"execution_count": null,
"id": "42685211",
"metadata": {
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"id": "42685211",
"outputId": "4cb46dfe-44bd-4081-ad30-63507ca79bad"
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"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7d1839af44c0>"
]
},
"metadata": {},
"execution_count": 20
}
],
"source": [
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]
},
{
"cell_type": "code",
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"id": "393f9c04",
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"id": "393f9c04",
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" player_fifa_api_id preferred_foot dribbling agility balance \\\n",
"5 189615 left 73.0 78.0 90.0 \n",
"6 189615 left 73.0 78.0 90.0 \n",
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"... ... ... ... ... ... \n",
"183948 164680 left 67.0 62.0 58.0 \n",
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"\n",
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"... ... ... ... ... \n",
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"variable_name": "filtered_df_left",
"summary": "{\n \"name\": \"filtered_df_left\",\n \"rows\": 37143,\n \"fields\": [\n {\n \"column\": \"player_fifa_api_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 48640,\n \"min\": 2,\n \"max\": 233911,\n \"num_unique_values\": 2793,\n \"samples\": [\n 149306,\n 186591,\n 177905\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"preferred_foot\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"left\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"dribbling\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12.549756750680073,\n \"min\": 10.0,\n \"max\": 97.0,\n \"num_unique_values\": 87,\n \"samples\": [\n 90.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"agility\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11.804197031498157,\n \"min\": 21.0,\n \"max\": 96.0,\n \"num_unique_values\": 75,\n \"samples\": [\n 74.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"balance\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12.128995827951854,\n \"min\": 20.0,\n \"max\": 96.0,\n \"num_unique_values\": 74,\n \"samples\": [\n 73.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vision\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12.64007495701645,\n \"min\": 18.0,\n \"max\": 94.0,\n \"num_unique_values\": 77,\n \"samples\": [\n 58.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"positioning\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14.754316317451018,\n \"min\": 11.0,\n \"max\": 93.0,\n \"num_unique_values\": 83,\n \"samples\": [\n 54.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ball_control\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9.38128723618305,\n \"min\": 25.0,\n \"max\": 97.0,\n \"num_unique_values\": 68,\n \"samples\": [\n 42.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gk_reflexes\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.1679606838239196,\n \"min\": 1.0,\n \"max\": 19.0,\n \"num_unique_values\": 19,\n \"samples\": [\n 12.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 21
}
],
"source": [
"filtered_df_left"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2e691681",
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"height": 444
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"id": "2e691681",
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{
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"data": {
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" player_fifa_api_id preferred_foot dribbling agility balance \\\n",
"0 218353 right 51.0 59.0 65.0 \n",
"1 218353 right 51.0 59.0 65.0 \n",
"2 218353 right 51.0 59.0 65.0 \n",
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"183977 102359 right 87.0 77.0 73.0 \n",
"\n",
" vision positioning ball_control gk_reflexes \n",
"0 54.0 45.0 49.0 8.0 \n",
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"2 54.0 45.0 49.0 8.0 \n",
"3 53.0 44.0 48.0 7.0 \n",
"4 53.0 44.0 48.0 7.0 \n",
"... ... ... ... ... \n",
"183969 87.0 77.0 82.0 5.0 \n",
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"183977 88.0 68.0 91.0 15.0 \n",
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" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
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"\n",
" .colab-df-quickchart {\n",
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" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
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"\n",
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"type": "dataframe",
"variable_name": "filtered_df_right"
}
},
"metadata": {},
"execution_count": 22
}
],
"source": [
"filtered_df_right"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a207d5d5",
"metadata": {
"id": "a207d5d5"
},
"outputs": [],
"source": [
"filtered_df_left_mean_by_player = filtered_df_left[['player_fifa_api_id', 'dribbling','agility', 'balance', 'vision',\n",
" 'ball_control']].groupby('player_fifa_api_id', as_index=False).mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "51786f75",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "51786f75",
"outputId": "c6537c82-a192-40a8-da91-9f3cae141ac0"
},
"outputs": [
{
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"data": {
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" player_fifa_api_id dribbling agility balance vision \\\n",
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" ball_control \n",
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"summary": "{\n \"name\": \"filtered_df_left_mean_by_player\",\n \"rows\": 2793,\n \"fields\": [\n {\n \"column\": \"player_fifa_api_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 54387,\n \"min\": 2,\n \"max\": 233911,\n \"num_unique_values\": 2793,\n \"samples\": [\n 223697,\n 182309,\n 224921\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"dribbling\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12.108102392029513,\n \"min\": 11.666666666666666,\n \"max\": 96.42857142857143,\n \"num_unique_values\": 1669,\n \"samples\": [\n 73.85,\n 32.285714285714285,\n 82.6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"agility\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11.000991113774354,\n \"min\": 27.076923076923077,\n \"max\": 94.33333333333333,\n \"num_unique_values\": 1492,\n \"samples\": [\n 67.45454545454545,\n 68.19354838709677,\n 49.291666666666664\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"balance\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 10.672710558259668,\n \"min\": 29.0,\n \"max\": 94.0,\n \"num_unique_values\": 1434,\n \"samples\": [\n 67.91304347826087,\n 69.11538461538461,\n 80.08333333333333\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vision\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11.849632422872071,\n \"min\": 20.0,\n \"max\": 94.0,\n \"num_unique_values\": 1636,\n \"samples\": [\n 63.46153846153846,\n 43.7,\n 48.85\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ball_control\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9.081313698450675,\n \"min\": 29.0,\n \"max\": 95.9047619047619,\n \"num_unique_values\": 1561,\n \"samples\": [\n 34.0,\n 61.65,\n 62.85\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
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},
"metadata": {},
"execution_count": 24
}
],
"source": [
"filtered_df_left_mean_by_player"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cfc7e04b",
"metadata": {
"id": "cfc7e04b"
},
"outputs": [],
"source": [
"filtered_df_right_mean_by_player = filtered_df_right[['player_fifa_api_id', 'dribbling','agility', 'balance', 'vision',\n",
" 'ball_control']].groupby('player_fifa_api_id', as_index=False).mean()"
]
},
{
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"execution_count": null,
"id": "7a0595e7",
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"id": "7a0595e7",
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"source": [
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},
{
"cell_type": "code",
"execution_count": null,
"id": "24b5fcb1",
"metadata": {
"id": "24b5fcb1"
},
"outputs": [],
"source": [
"del filtered_df_left_mean_by_player['vision']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "96f0bd13",
"metadata": {
"id": "96f0bd13"
},
"outputs": [],
"source": [
"del filtered_df_right_mean_by_player['vision']"
]
},
{
"cell_type": "code",
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"id": "83f409d4",
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"id": "83f409d4",
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"summary": "{\n \"name\": \"filtered_df_left_mean_by_player\",\n \"rows\": 2793,\n \"fields\": [\n {\n \"column\": \"player_fifa_api_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 54387,\n \"min\": 2,\n \"max\": 233911,\n \"num_unique_values\": 2793,\n \"samples\": [\n 223697,\n 182309,\n 224921\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"dribbling\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 12.108102392029513,\n \"min\": 11.666666666666666,\n \"max\": 96.42857142857143,\n \"num_unique_values\": 1669,\n \"samples\": [\n 73.85,\n 32.285714285714285,\n 82.6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"agility\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11.000991113774354,\n \"min\": 27.076923076923077,\n \"max\": 94.33333333333333,\n \"num_unique_values\": 1492,\n \"samples\": [\n 67.45454545454545,\n 68.19354838709677,\n 49.291666666666664\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"balance\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 10.672710558259668,\n \"min\": 29.0,\n \"max\": 94.0,\n \"num_unique_values\": 1434,\n \"samples\": [\n 67.91304347826087,\n 69.11538461538461,\n 80.08333333333333\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ball_control\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9.081313698450675,\n \"min\": 29.0,\n \"max\": 95.9047619047619,\n \"num_unique_values\": 1561,\n \"samples\": [\n 34.0,\n 61.65,\n 62.85\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 29
}
],
"source": [
"filtered_df_left_mean_by_player"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "21a76087",
"metadata": {
"id": "21a76087"
},
"outputs": [],
"source": [
"final_left = filtered_df_left_mean_by_player.drop(columns='player_fifa_api_id').mean(axis=1)"
]
},
{
"cell_type": "code",
"source": [
"final_left"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 458
},
"id": "9eLbGx8py_pS",
"outputId": "0125c7ff-1877-44d9-f253-5416e0a7294b"
},
"id": "9eLbGx8py_pS",
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
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"2 64.850000\n",
"3 61.083333\n",
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" ... \n",
"2788 56.750000\n",
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},
"metadata": {},
"execution_count": 41
}
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cb538b67",
"metadata": {
"id": "cb538b67",
"outputId": "22d9f298-522f-4d87-85d2-2d882cbdd6e0"
},
"outputs": [
{
"data": {
"text/plain": [
"count 2793.000000\n",
"mean 65.158958\n",
"std 8.951857\n",
"min 34.071429\n",
"25% 59.783333\n",
"50% 65.972222\n",
"75% 71.375000\n",
"max 95.011905\n",
"dtype: float64"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_left.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "71eb167e",
"metadata": {
"id": "71eb167e"
},
"outputs": [],
"source": [
"final_right = filtered_df_right_mean_by_player.drop(columns='player_fifa_api_id').mean(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f71e7e2d",
"metadata": {
"colab": {
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"height": 335
},
"id": "f71e7e2d",
"outputId": "f298ed3b-c948-4d0f-8e2c-b666b48e8441"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"count 7487.000000\n",
"mean 64.106280\n",
"std 9.502308\n",
"min 30.500000\n",
"25% 58.159722\n",
"50% 65.062500\n",
"75% 70.795177\n",
"max 91.612500\n",
"dtype: float64"
],
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" <tbody>\n",
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" </tr>\n",
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" <th>mean</th>\n",
" <td>64.106280</td>\n",
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" <tr>\n",
" <th>min</th>\n",
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"metadata": {},
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}
],
"source": [
"final_right.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4468cbd7",
"metadata": {
"id": "4468cbd7"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.rcParams.update({'font.size': 12})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aeb6f992",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 901
},
"id": "aeb6f992",
"outputId": "a619393f-a100-40f8-e897-520fe96f538b"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x1000 with 1 Axes>"
],
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\n"
},
"metadata": {}
}
],
"source": [
"left_mean = final_left.mean()\n",
"right_mean = final_right.mean()\n",
"plt.axvline(left_mean, color='blue', linestyle='dashed', linewidth=1.5, label=f'Mean Left Players: {left_mean:.2f}')\n",
"plt.axvline(right_mean, color='red', linestyle='dashed', linewidth=1.5, label=f'Mean Right Players: {right_mean:.2f}')\n",
"plt.hist(final_left, 50, alpha = 0.8, label='Left',density=False, facecolor='blue')\n",
"plt.hist(final_right, 50, alpha = 0.3, label = 'Right',density=False, facecolor='red')\n",
"plt.legend(loc='upper right')\n",
"plt.xlabel('Ability')\n",
"plt.ylabel('Number of Players')\n",
"plt.title('Left Players x Right Players')\n",
"plt.xlim([30, 100])\n",
"fig = plt.gcf()\n",
"fig.set_size_inches(15, 10)\n",
"fig.savefig('left_right.png', dpi=500)\n",
"#plt.grid(True)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"source": [
"from scipy import stats\n",
"\n",
"# Perform the t-test\n",
"t_statistic, p_value = stats.ttest_ind(final_left, final_right, alternative='greater', equal_var=False)\n",
"\n",
"# Print the results\n",
"print(f\"T-statistic: {t_statistic}\")\n",
"print(f\"P-value: {p_value}\")\n",
"\n",
"# Interpret the results\n",
"alpha = 0.05 # Common significance level\n",
"if p_value < alpha:\n",
" print(\"Reject the null hypothesis. There is significant evidence that the Left Footed mean is higher.\")\n",
"else:\n",
" print(\"Fail to reject the null hypothesis. There is not significant evidence that the Left Footed mean is higher.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sWQVdvlQ4LL7",
"outputId": "1a0f7345-c280-42c3-db88-0905c53381e2"
},
"id": "sWQVdvlQ4LL7",
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"T-statistic: 5.214621050184478\n",
"P-value: 9.560563675210363e-08\n",
"Reject the null hypothesis. There is significant evidence that the Left Footed mean is higher.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"There is significant evidence that the Left Footed mean is higher.\n",
"\n"
],
"metadata": {
"id": "-b5zFAdI5Cpm"
},
"id": "-b5zFAdI5Cpm"
}
],
"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": "3.8.8"
},
"colab": {
"provenance": [],
"include_colab_link": true
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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