Per player perf.ipynb

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   "execution_count": 10,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "Index(['numero', 'giocatore', 'ruolo', 'ruolo_cod', 'voto', 'gol_fatti_subiti',\n",
       "       'assist', 'rigori_realizzati', 'rigori_sbagliati', 'autogol',\n",
       "       'ammonizioni', 'espulsioni', 'fantavoto', 'squadra', 'dots', 'yyss',\n",
       "       'time_index'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "from plotly.graph_objs.scatter.marker import Line\n",
    "import plotly.express as px\n",
    "import itertools\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    " \n",
    "pd.set_option('display.max_rows', 500)\n",
    "\n",
    "data_path = './data/stats.csv'\n",
    "df = pd.read_csv(data_path)\n",
    "df['time_index'] = df['dots'].astype(str) +'--'+ df['yyss'].astype(str)\n",
    "df['giocatore'] = df.giocatore.str.lower()\n",
    "df.columns"
   ]
  },
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# def myplot(giocatore, df):\n",
    "giocatore='insigne'\n",
    "\n",
    "\n",
    "player = df[df.giocatore.str.contains(giocatore)].reset_index()\n",
    "lists = [df.yyss.unique().tolist(), list(range(1,39))]\n",
    "time_df = pd.DataFrame(index=[str(val[1])+'--'+str(val[0]) for val in itertools.product(*lists)])\n",
    "time_df['fantavoto'] = np.nan\n",
    "player_complete = player.merge(time_df, left_on='time_index', right_index=True, how='outer', suffixes=('', '_y'))\n",
    "player_complete['dots'] = player_complete['time_index'].str.split('--').str[0].astype(int)\n",
    "player_complete['yyss'] = player_complete['time_index'].str.split('--').str[1]\n",
    "player_complete = player_complete.sort_values(by=['yyss', 'dots']).reset_index().drop('index',axis=1)\n",
    "player_complete['x'] = player_complete.index\n",
    "\n",
    "\n",
    "title = \"%s - %s\" %(giocatore.capitalize(), player.ruolo[0])\n",
    "\n",
    "player = player_complete.copy()\n",
    "player['text'] = player['dots'].astype(str) + \"<br>\" +player['yyss'].str[2:]\n",
    "fig = px.scatter(player, x=player.x, y=player.fantavoto, \n",
    "                 trendline=\"ols\", title=title, hover_data=[\"dots\", \"yyss\"])\n",
    "fig.update_traces(hovertemplate='Giornata: %{dots} <br>Stagione: %{yyss} <br><b>voto: %{y}</b>')\n",
    "\n",
    "not_nan = player.fillna(0).reset_index()\n",
    "X = not_nan.index.values.reshape(-1, 1)\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X, not_nan.fantavoto)\n",
    "x_range = np.linspace(X.min(), X.max(), 100)\n",
    "y_range = model.predict(x_range.reshape(-1, 1))\n",
    "fig.add_traces(go.Scatter(x=x_range, y=y_range, name='Regression Fit'))\n",
    "\n",
    "fig.show()\n",
    "\n",
    "#     return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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