lab_dash.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import dash as ds\n",
    "from dash import html\n",
    "from dash import dcc\n",
    "import dash_bootstrap_components as dbc\n",
    "import plotly.express as px\n",
    "\n",
    "# 1. height vs position\n",
    "# 2. avg value of players from different countries\n",
    "# 3. leagues comparison(value)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column1</th>\n",
       "      <th>player</th>\n",
       "      <th>nationality</th>\n",
       "      <th>position</th>\n",
       "      <th>squad</th>\n",
       "      <th>age</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>value</th>\n",
       "      <th>height</th>\n",
       "      <th>position2</th>\n",
       "      <th>...</th>\n",
       "      <th>Pts/G</th>\n",
       "      <th>xG</th>\n",
       "      <th>xGA</th>\n",
       "      <th>xGDiff</th>\n",
       "      <th>xGDiff/90</th>\n",
       "      <th>Attendance</th>\n",
       "      <th>CL</th>\n",
       "      <th>WinCL</th>\n",
       "      <th>CLBestScorer</th>\n",
       "      <th>Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>Martin Aguirregabiria</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>DF</td>\n",
       "      <td>AlavĂ©s</td>\n",
       "      <td>23</td>\n",
       "      <td>1996</td>\n",
       "      <td>4000000</td>\n",
       "      <td>178</td>\n",
       "      <td>Defender - Right-Back</td>\n",
       "      <td>...</td>\n",
       "      <td>1.03</td>\n",
       "      <td>37.0</td>\n",
       "      <td>53.2</td>\n",
       "      <td>-16.2</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>12,177</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>201920#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>390</td>\n",
       "      <td>Oliver Burke</td>\n",
       "      <td>sco SCO</td>\n",
       "      <td>MF,FW</td>\n",
       "      <td>AlavĂ©s</td>\n",
       "      <td>22</td>\n",
       "      <td>1997</td>\n",
       "      <td>4000000</td>\n",
       "      <td>188</td>\n",
       "      <td>Forward - Right Winger</td>\n",
       "      <td>...</td>\n",
       "      <td>1.03</td>\n",
       "      <td>37.0</td>\n",
       "      <td>53.2</td>\n",
       "      <td>-16.2</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>12,177</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>201920#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>430</td>\n",
       "      <td>VĂ­ctor Camarasa</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>MF</td>\n",
       "      <td>AlavĂ©s</td>\n",
       "      <td>25</td>\n",
       "      <td>1994</td>\n",
       "      <td>4000000</td>\n",
       "      <td>183</td>\n",
       "      <td>Midfielder - Central Midfield</td>\n",
       "      <td>...</td>\n",
       "      <td>1.03</td>\n",
       "      <td>37.0</td>\n",
       "      <td>53.2</td>\n",
       "      <td>-16.2</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>12,177</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>201920#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>737</td>\n",
       "      <td>RubĂ©n Duarte</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>DF</td>\n",
       "      <td>AlavĂ©s</td>\n",
       "      <td>23</td>\n",
       "      <td>1995</td>\n",
       "      <td>4000000</td>\n",
       "      <td>172</td>\n",
       "      <td>Defender - Left-Back</td>\n",
       "      <td>...</td>\n",
       "      <td>1.03</td>\n",
       "      <td>37.0</td>\n",
       "      <td>53.2</td>\n",
       "      <td>-16.2</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>12,177</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>201920#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>770</td>\n",
       "      <td>Rodrigo Ely</td>\n",
       "      <td>br BRA</td>\n",
       "      <td>DF</td>\n",
       "      <td>AlavĂ©s</td>\n",
       "      <td>25</td>\n",
       "      <td>1993</td>\n",
       "      <td>1000000</td>\n",
       "      <td>188</td>\n",
       "      <td>Defender - Centre-Back</td>\n",
       "      <td>...</td>\n",
       "      <td>1.03</td>\n",
       "      <td>37.0</td>\n",
       "      <td>53.2</td>\n",
       "      <td>-16.2</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>12,177</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>201920#</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 400 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column1                 player nationality position    squad  age  \\\n",
       "0       21  Martin Aguirregabiria      es ESP       DF  AlavĂ©s   23   \n",
       "1      390           Oliver Burke     sco SCO    MF,FW  AlavĂ©s   22   \n",
       "2      430       VĂ­ctor Camarasa      es ESP       MF  AlavĂ©s   25   \n",
       "3      737          RubĂ©n Duarte      es ESP       DF  AlavĂ©s   23   \n",
       "4      770            Rodrigo Ely      br BRA       DF  AlavĂ©s   25   \n",
       "\n",
       "   birth_year    value  height                      position2  ... Pts/G  \\\n",
       "0        1996  4000000     178          Defender - Right-Back  ...  1.03   \n",
       "1        1997  4000000     188         Forward - Right Winger  ...  1.03   \n",
       "2        1994  4000000     183  Midfielder - Central Midfield  ...  1.03   \n",
       "3        1995  4000000     172           Defender - Left-Back  ...  1.03   \n",
       "4        1993  1000000     188         Defender - Centre-Back  ...  1.03   \n",
       "\n",
       "     xG   xGA  xGDiff  xGDiff/90  Attendance  CL  WinCL  CLBestScorer   Season  \n",
       "0  37.0  53.2   -16.2      -0.43      12,177   0      0           0.0  201920#  \n",
       "1  37.0  53.2   -16.2      -0.43      12,177   0      0           0.0  201920#  \n",
       "2  37.0  53.2   -16.2      -0.43      12,177   0      0           0.0  201920#  \n",
       "3  37.0  53.2   -16.2      -0.43      12,177   0      0           0.0  201920#  \n",
       "4  37.0  53.2   -16.2      -0.43      12,177   0      0           0.0  201920#  \n",
       "\n",
       "[5 rows x 400 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"transfermarkt_fbref_201920.csv\", sep=\";\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     age      league  count\n",
      "2     14     La Liga      1\n",
      "3     15     Ligue 1      1\n",
      "4     16  Bundesliga      3\n",
      "5     16     La Liga      1\n",
      "6     16     Ligue 1      2\n",
      "..   ...         ...    ...\n",
      "111   38     Serie A      1\n",
      "112   39     Ligue 1      1\n",
      "113   40  Bundesliga      1\n",
      "114   41     Ligue 1      1\n",
      "115   41     Serie A      1\n",
      "\n",
      "[114 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "player_counts_age_league = data.groupby(['age', 'league']).size().reset_index(name='count')\n",
    "player_counts_age_league = player_counts_age_league.loc[player_counts_age_league['age'] != 0]\n",
    "\n",
    "print(player_counts_age_league)\n",
    "\n",
    "data[\"nationality\"] = data[\"nationality\"].str.slice(3,6)\n",
    "\n",
    "data[\"nationality\"] = data[\"nationality\"].replace(' EN', 'GBR')\n",
    "data[\"nationality\"] = data[\"nationality\"].replace(' WA', 'GBR')\n",
    "data[\"nationality\"] = data[\"nationality\"].replace(' SC', 'GBR')\n",
    "data[\"nationality\"] = data[\"nationality\"].replace('GER', 'DEU')\n",
    "data[\"nationality\"] = data[\"nationality\"].replace('POR', 'PRT')\n",
    "data[\"nationality\"] = data[\"nationality\"].replace('DEN', 'DNK')\n",
    "data[\"nationality\"] = data[\"nationality\"].replace('NED', 'NLD')\n",
    "data[\"nationality\"] = data[\"nationality\"].replace('SUI', 'CHE')\n",
    "data[\"nationality\"] = data[\"nationality\"].replace('URU', 'URY')\n",
    "data[\"nationality\"] = data[\"nationality\"].replace('PAR', 'PRY')\n",
    "data[\"nationality\"] = data[\"nationality\"].replace('RSA', 'ZAF')\n",
    "data[\"nationality\"] = data[\"nationality\"].replace('CRO', 'HRV')\n",
    "data[\"nationality\"] = data[\"nationality\"].replace('GRE', 'GRC')\n",
    "data[\"nationality\"] = data[\"nationality\"].replace('CHI', 'RCH')\n",
    "data[\"nationality\"] = data[\"nationality\"].replace('GAM', 'GMB')\n",
    "data[\"nationality\"] = data[\"nationality\"].replace('ALG', 'DZA')\n",
    "\n",
    "player_counts = data['nationality'].value_counts().reset_index()\n",
    "player_counts.columns = ['country', 'count']\n",
    "\n",
    "\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "#Sum of 11 most valued players per country\n",
    "data[\"nationality\"].nunique()\n",
    "print(data[\"nationality\"].isna().sum())\n",
    "print(data[\"value\"].isna().sum())\n",
    "\n",
    "avg_value_per_country = data.groupby(\"nationality\")[\"value\"].nlargest(11)\n",
    "\n",
    "sum_top_10_players_per_country = avg_value_per_country.groupby('nationality').sum()\n",
    "sorted_players_sum = sum_top_10_players_per_country.sort_values(ascending=False).head(20)\n",
    "\n",
    "sorted_players_sum_df = sorted_players_sum.to_frame('sum_value')\n",
    "sorted_players_sum_df['continent'] = ['Europe', 'Europe', 'South America', 'Europe', 'Europe',\n",
    "                                       'South America', 'Europe', 'Europe', 'Europe', 'Europe',\n",
    "                                         'South America', 'Europe', 'Africa', 'Africa', 'Europe', \n",
    "                                         'Europe', 'Europe','South America', 'Asia', 'Europe']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clean positions\n",
    "data[\"position\"] = data[\"position\"].str.slice(0, 2)\n",
    "data[\"position\"].isna().sum()\n",
    "data[\"position\"].nunique()\n",
    "data_for_height = data[data[\"height\"] > 140]\n",
    "data= data[data[\"goals\"] > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"transfermarkt_fbref_201920.csv\", sep=\";\")\n",
    "data['Attendance'] = data['Attendance'].str.replace(',', '').astype(int)\n",
    "\n",
    "team_values = data.groupby('squad')['value'].sum().reset_index()\n",
    "team_attendance = data.groupby('squad')['Attendance'].first().reset_index()\n",
    "\n",
    "goals_per_team_league = data.groupby(['squad', 'league'])['goals'].sum().reset_index()\n",
    "goals_per_team_league_attendance = pd.merge(goals_per_team_league, team_attendance, on = 'squad', how= 'left')\n",
    "teams_per_value_per_goals_per_league = pd.merge(goals_per_team_league_attendance, team_values, on='squad', how='left')\n",
    "teams_per_value_per_goals_per_league['value'] = teams_per_value_per_goals_per_league['value'] / 1e6\n",
    "teams_per_value_per_goals_per_league = teams_per_value_per_goals_per_league.rename(columns={'value': 'value_in_millions'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"650\"\n",
       "            src=\"http://127.0.0.1:8081/\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
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       "<IPython.lib.display.IFrame at 0x24a8ff5e990>"
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "app = ds.Dash(__name__)\n",
    "\n",
    "# Create choropleth map\n",
    "color_scale = [\"#b3fcb3\", \"#63c402\", \"#32CD32\", \"#FFFF00\", \"#FFD700\", \"#FFA500\", \"#FF8000\", \"#FF4500\",\"#ff5454\", \"#FF0000\", \"red\", \"red\" ]\n",
    "fig1 = px.choropleth(player_counts, locations='country', color='count',\n",
    "                    locationmode='ISO-3',\n",
    "                    color_continuous_scale= color_scale,\n",
    "                    title='Counts of Players per Nationality in Each Country')\n",
    "fig1.update_layout(height=800, width=1400)\n",
    "\n",
    "\n",
    "fig2 = px.bar(sorted_players_sum_df, x=sorted_players_sum.index, y='sum_value', color = 'continent', text_auto='.2s',\n",
    "              title = \"Sum of values of 11 most valued players per country 2020\",\n",
    "              labels= {\"sum_value\": \"Total Value (milions)\", \"nationality\":\"Country Code\"})\n",
    "fig2.update_layout(font_color = 'black', title_font_family = 'Arial Black', font=dict(size=14))\n",
    "fig2.update_xaxes(categoryorder='total descending', title_font_family = 'Arial')\n",
    "fig2.update_yaxes(tickfont_family=\"Arial Black\")\n",
    "\n",
    "\n",
    "fig3 = px.line(player_counts_age_league, x='age', y='count', color='league', title='Number of Players per Each Age in Each League')\n",
    "fig3.update_layout(font_color = 'black', title_font_family = 'Arial Black', font=dict(size=14))\n",
    "\n",
    "\n",
    "fig4 = px.scatter(teams_per_value_per_goals_per_league , x='Attendance', y='goals', size='value_in_millions', color='league',\n",
    "                 title='Team goals scored vs Attendance per Value per League', \n",
    "                 labels={'squad':'Team', 'league':'League', 'team_value':'Team Value (Milions)', 'goal':'Number of Goals'}, hover_data=['squad'])\n",
    "fig4.update_layout(font_color = 'black', title_font_family = 'Arial Black',font=dict(size=14))\n",
    "fig4.update_yaxes(tickfont_family=\"Arial Black\")\n",
    "fig4.update_xaxes(tickfont_family=\"Arial Black\")\n",
    "\n",
    "fig5 = px.scatter(data, x = \"minutes\", y = 'goals', color = \"position\", title='Player Goals scored vs minutes played per position.',hover_data=['player','squad'])\n",
    "fig5.update_layout(font_color = 'black', title_font_family = 'Arial Black',font=dict(size=14))\n",
    "fig5.update_yaxes(tickfont_family=\"Arial Black\")\n",
    "fig5.update_xaxes(tickfont_family=\"Arial Black\")\n",
    "\n",
    "app.layout = html.Div(children=[\n",
    "    html.H1(children='Lab Part Dashboard'),\n",
    "    dcc.Graph(\n",
    "        id='graph1',\n",
    "        figure=fig1\n",
    "    ),\n",
    "    dcc.Graph(\n",
    "        id='graph2',\n",
    "        figure=fig2\n",
    "    ),\n",
    "    dcc.Graph(\n",
    "        id='graph3',\n",
    "        figure=fig3\n",
    "    ),\n",
    "    dcc.Graph(\n",
    "        id='graph4',\n",
    "        figure=fig4\n",
    "    ),\n",
    "    dcc.Graph(\n",
    "        id='graph5',\n",
    "        figure=fig5\n",
    "    )\n",
    "])\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True, host='127.0.0.1', port=8081)"
   ]
  }
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