{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Laboratorio su Misure di Tendenza Centrale, Dispersione e Forma\n",
    "\n",
    "Per esaminare gli strumenti in oggetto, ci avvremo del dataset **Titanic**. Si tratta di un dataset classico, che riporta alcune informazioni, quali sesso, età, classe di imbarco e sopravvivenza di alcuni passaggeri del Titanic. Troviamo una versione del dataset al seguente URL:\n",
    "\n",
    "https://raw.githubusercontent.com/agconti/kaggle-titanic/master/data/train.csv\n",
    "\n",
    "Carichiamo il dataset mediante la libreria Pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "titanic = pd.read_csv('https://raw.githubusercontent.com/agconti/kaggle-titanic/master/data/train.csv',\n",
    "                     index_col='PassengerId')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizziamo alcune informazioni sul dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Name      891 non-null    object \n",
      " 3   Sex       891 non-null    object \n",
      " 4   Age       714 non-null    float64\n",
      " 5   SibSp     891 non-null    int64  \n",
      " 6   Parch     891 non-null    int64  \n",
      " 7   Ticket    891 non-null    object \n",
      " 8   Fare      891 non-null    float64\n",
      " 9   Cabin     204 non-null    object \n",
      " 10  Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 83.5+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il dataset contiene $891$ osservazioni e $11$ colonne. Visualizziamo le prime righe del DataFrame per vedere com'è organizzato:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4                1      0            113803  53.1000  C123        S  \n",
       "5                0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerosità\n",
    "\n",
    "La numerosità dei campioni costituiti dalle singole colonne può essere ottenuta mediante il metodo `len`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "891\n"
     ]
    }
   ],
   "source": [
    "print(len(titanic['Age']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il metodo `len` però non considera i dati incompleti (la colonna potrebbe contenere dei `NaN`). Per ottenere un conteggio più corretto possiamo esplicitamente eliminare i `NaN` con `dropna`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "714\n"
     ]
    }
   ],
   "source": [
    "print(len(titanic['Age'].dropna()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Come possiamo notare, il campione è meno numeroso di quello che sembrava. Alternativamente possiamo richiamare `info` sul DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Name      891 non-null    object \n",
      " 3   Sex       891 non-null    object \n",
      " 4   Age       714 non-null    float64\n",
      " 5   SibSp     891 non-null    int64  \n",
      " 6   Parch     891 non-null    int64  \n",
      " 7   Ticket    891 non-null    object \n",
      " 8   Fare      891 non-null    float64\n",
      " 9   Cabin     204 non-null    object \n",
      " 10  Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 83.5+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lo schema sopra mostra il numero di oggetti `non-null` per ogni colonna. Coerentemente con quanto visto prima, abbiamo solo $714$ elementi di `Age` validi."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **🙋‍♂️ Domanda 1**\n",
    ">\n",
    "> Qual è la numerosità di `Cabin`?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indicatori Centrali: Media, Mediana e Moda\n",
    "\n",
    "\n",
    "### Media\n",
    "\n",
    "Possiamo calcolarlo manualmente la media come segue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Age'].sum()/len(titanic['Age'].dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O semplicemente mediante la funzione `mean`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "> **🙋‍♂️ Domanda 2**\n",
    ">\n",
    "> In quali casi la media riassume bene i dati? In quali casi no?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mediana\n",
    "\n",
    "Possiamo calcolare la mediana manualmente come segue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3   -4\n",
      "1   -2\n",
      "0    1\n",
      "2    3\n",
      "4    7\n",
      "dtype: int64\n",
      "Mediana: 1\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a=pd.Series([1,-2,3,-4,7])\n",
    "print(a.sort_values())\n",
    "print(\"Mediana: {}\".format(a.sort_values().iloc[2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "o alternativamente mediante il metodo `median`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calcoliamo ad esempio il valore mediano di `Age`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Age'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "> **🙋‍♂️ Domanda 3**\n",
    ">\n",
    "> Si confronti la media di `Age` con la sua mediana. I due valori corrispondono? Perché? Quali dei due valori è uguale a uno dei valori appartenente al campione?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantili, Percentili e Quartili\n",
    "\n",
    "In pandas, i quantili si ottengono mediante il metodo `quantile`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantile di ordine 0 (minimo): 0.42\n",
      "Quantile di ordine 0.5 (mediana): 28.0\n",
      "Quantile di ordine 1 (massimo): 80.0\n",
      "Quantile di ordine 0.15: 17.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Quantile di ordine 0 (minimo):\", titanic['Age'].quantile(0))\n",
    "print(\"Quantile di ordine 0.5 (mediana):\", titanic['Age'].quantile(0.5))\n",
    "print(\"Quantile di ordine 1 (massimo):\", titanic['Age'].quantile(1))\n",
    "print(\"Quantile di ordine 0.15:\", titanic['Age'].quantile(0.15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I percentili si ottengono sempre mediante la funzione `quantile`, dividendo l'ordine per percentile per 100:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentile di ordine 50% (mediana): 28.0\n",
      "Percentile di ordine 30%: 22.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Percentile di ordine 50% (mediana):\", titanic['Age'].quantile(50/100))\n",
    "print(\"Percentile di ordine 30%:\", titanic['Age'].quantile(30/100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analogamente, i quartili si ottengono dividendo l'ordine del quartile per 4:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quartile di ordine 0 (minimo): 0.42\n",
      "Quartile di ordine 1: 20.125\n",
      "Quartile di ordine 2 (mediana): 28.0\n",
      "Quartile di ordine 3: 38.0\n",
      "Quartile di ordine 4 (massimo): 80.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Quartile di ordine 0 (minimo):\", titanic['Age'].quantile(0/4))\n",
    "print(\"Quartile di ordine 1:\", titanic['Age'].quantile(1/4))\n",
    "print(\"Quartile di ordine 2 (mediana):\", titanic['Age'].quantile(2/4))\n",
    "print(\"Quartile di ordine 3:\", titanic['Age'].quantile(3/4))\n",
    "print(\"Quartile di ordine 4 (massimo):\", titanic['Age'].quantile(4/4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "> **🙋‍♂️ Domanda 4**\n",
    ">\n",
    "> Anche i quartili, come il minimo e il massimo, permettono di farsi un'idea dell'ordine di grandezza dei dati analizzati, tuttavia, i quartili hanno un vantaggio, quale?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Moda\n",
    "Possiamo calcolare la moda manualmente come segue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.0    30\n",
      "22.0    27\n",
      "18.0    26\n",
      "19.0    25\n",
      "28.0    25\n",
      "Name: Age, dtype: int64\n",
      "Moda:  24.0\n"
     ]
    }
   ],
   "source": [
    "print(titanic['Age'].value_counts().head())\n",
    "print(\"Moda: \",titanic['Age'].value_counts().index[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternativamente, possiamo usare la funzione `mode`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Age'].mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "> **🙋‍♂️ Domanda 5**\n",
    ">\n",
    "> Si confrontino la moda di `Age` con la sua media. I valori corrispondono? Perché? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indicatori di dispersione"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Minimo, Massimo e Range\n",
    "Massimo, minimo e range vengono calcolati semplicemente come segue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimo: 0.42\n",
      "Massimo: 80.0\n",
      "Range: 79.58\n"
     ]
    }
   ],
   "source": [
    "print(\"Minimo:\", titanic['Age'].min())\n",
    "print(\"Massimo:\", titanic['Age'].max())\n",
    "print(\"Range:\", titanic['Age'].max() - titanic['Age'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "> **🙋‍♂️ Domanda 6**\n",
    ">\n",
    "> Il range è un indicatore di dispersione robusto alla presenza di \"outliers\"?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distanza interquartile\n",
    "Possiamo calcolare la distanza interquartile come segue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lo scarto interquartile di sample 1 è: 18.0\n",
      "Lo scarto interquartile di sample 2 è: 19.0\n"
     ]
    }
   ],
   "source": [
    "q11,q13 = titanic[titanic['Sex']=='male']['Age'].quantile([1/4,3/4])\n",
    "q21,q23 = titanic[titanic['Sex']=='female']['Age'].quantile([1/4,3/4])\n",
    "print(\"Lo scarto interquartile di sample 1 è:\",q13-q11)\n",
    "print(\"Lo scarto interquartile di sample 2 è:\",q23-q21)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **🙋‍♂️ Domanda 7**\n",
    ">\n",
    "> La distanza interquartile è sempre una misura di dispersione robusta? Cosa succede nel caso di distribuzioni fortemente asimmetriche?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Varianza e Deviazione Standard\n",
    "\n",
    "\n",
    "La varianza può essere calcolata richiamando il metodo `var`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "215.44957942429036\n",
      "199.0962330533453\n"
     ]
    }
   ],
   "source": [
    "print(titanic[titanic['Sex']=='male']['Age'].var())\n",
    "print(titanic[titanic['Sex']=='female']['Age'].var())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La **deviazione standard** si calcola mediante il metodo `std`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deviazione standard del campione: 14.526497332334044 anni\n"
     ]
    }
   ],
   "source": [
    "print(\"Deviazione standard del campione:\",titanic['Age'].std(),\"anni\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La deviazione standard ci dice qualcosa anche sulla \"forma\" della distribuzione. Confrontiamo ad esempio le deviazioni standard delle età dei passeggeri appartenenti alle tre classi di imbarco:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    14.802856\n",
       "2    14.001077\n",
       "3    12.495398\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby('Pclass')['Age'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confrontiamo adesso le densità stimate dai tre campioni:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "titanic.groupby('Pclass')['Age'].plot.density(figsize=(8,4))\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **🙋‍♂️ Domanda 8**\n",
    ">\n",
    "> Esiste una relazione tra le deviazioni standard dei campioni e le relative densità? Quale?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalizzazione tra 0 e 1\n",
    "Secondo la normalizzazione tra 0 e 1, i dati vengono riscalati come segue:\n",
    "\n",
    "$$\n",
    "x_{norm} = (x-x_{min})/(x_{max}-x_{min})\n",
    "$$\n",
    "\n",
    "Possiamo effettuare questa trasformazione in Pandas come segue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_norm_01 = (titanic['Age']-titanic['Age'].min())/(titanic['Age'].max()-titanic['Age'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verifichiamo che gli estremi del nuovo campione siano 0 e 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 1.0\n"
     ]
    }
   ],
   "source": [
    "print(age_norm_01.min(), age_norm_01.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalizzazione tra -1 e 1\n",
    "I dati vengono riscalati come segue:\n",
    "\n",
    "$$\n",
    "x_{norm} = (x_{max}+x_{min}-2\\cdot x)/(x_{max}-x_{min})\n",
    "$$\n",
    "\n",
    "Possiamo effettuare questa trasformazione in Pandas come segue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_norm_11 = (titanic['Age'].max()+titanic['Age'].min()-2*titanic['Age'])/(titanic['Age'].max()-titanic['Age'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verifichiamo che gli estremi del nuovo campione siano -1 e 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.0 1.0\n"
     ]
    }
   ],
   "source": [
    "print(age_norm_11.min(), age_norm_11.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardizzazione (z-scoring)\n",
    "\n",
    "Possiamo effettuare questa normalizzazione usando la funzione `zscore` di `scipy`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.41421356, -0.70710678,  0.        ,  0.70710678,  1.41421356])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import zscore\n",
    "zscore([0,1,2,3,4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applichiamo questa normalizzazione alle età dei passeggeri del Titanic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_z=zscore(titanic['Age'].dropna())\n",
    "plt.figure(figsize=(12,4))\n",
    "plt.subplot(121)\n",
    "titanic['Age'].plot.density()\n",
    "plt.grid()\n",
    "plt.subplot(122)\n",
    "age_z.plot.density()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indicatori di Forma\n",
    "\n",
    "#### Asimmetria (skewness)\n",
    "Posso calcolare il valore di skewness mediante il metodo di Pandas `skew`. Vediamo un paio di esempi sul dataset di pesi e altezze:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/cs/p62_d78d49n3ddj0xlfh1h7r0000gn/T/ipykernel_31494/1596635564.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  titanic.skew()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Survived    0.478523\n",
       "Pclass     -0.630548\n",
       "Age         0.389108\n",
       "SibSp       3.695352\n",
       "Parch       2.749117\n",
       "Fare        4.787317\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confrontiamo i risultati con le stime di densità:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>,\n",
       "        <AxesSubplot:ylabel='Density'>],\n",
       "       [<AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>,\n",
       "        <AxesSubplot:ylabel='Density'>]], dtype=object)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.plot.density(subplots=True, layout=(2,3), figsize=(12,8), sharex=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I valori di skewness di pesi e altezze sarnno:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skweness pesi: 0.57\n",
      "Skweness altezza: 0.19\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "hw=pd.read_csv('http://antoninofurnari.it/downloads/height_weight.csv')\n",
    "\n",
    "print(f\"Skweness pesi: {hw['weight'].skew():0.2f}\")\n",
    "print(f\"Skweness altezza: {hw['height'].skew():0.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confrontando con le densità:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>,\n",
       "        <AxesSubplot:ylabel='Density'>],\n",
       "       [<AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>,\n",
       "        <AxesSubplot:ylabel='Density'>]], dtype=object)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hw.plot.density(subplots=True, layout=(2,3), figsize=(12,8), sharex=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Curtosi (kurtosis)\n",
    "Analogamente, possiamo calcolare la curtosi con il metodo `Kurtosis`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/cs/p62_d78d49n3ddj0xlfh1h7r0000gn/T/ipykernel_31494/3071249296.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  titanic.kurtosis()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Survived    -1.775005\n",
       "Pclass      -1.280015\n",
       "Age          0.178274\n",
       "SibSp       17.880420\n",
       "Parch        9.778125\n",
       "Fare        33.398141\n",
       "dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.kurtosis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/cs/p62_d78d49n3ddj0xlfh1h7r0000gn/T/ipykernel_31494/4227707798.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  hw.kurtosis()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BMI       0.917179\n",
       "height   -0.687287\n",
       "weight   -0.056386\n",
       "dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hw.kurtosis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boxplot\n",
    "\n",
    "Possiamo visualizzare un boxplot direttamente mediante `Pandas`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 144x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "titanic['Age'].plot.box(figsize=(2,6))\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternativamente, possiamo mostrare un boxplot mediante matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 144x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(2,6))\n",
    "plt.boxplot(titanic['Age'].dropna())#inseriamo dropna per eliminare i NaN\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot Multipli\n",
    "\n",
    "Mediante `Pandas` è possibile mostrare i boxplot di tutte le variabili numeriche di un DataFrame come segue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.plot.box()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se vogliamo restringere l'analisi ad alcune variabili, possiamo selezionare solo le variabili interessate come segue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic[['Age','Fare']].plot.box()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "E' inoltre possibile confrontare i boxplot di diversi campioni. Supponiamo di voler confrontare le età di uomini e donne:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.boxplot(by='Sex',column=['Age'], figsize=(8,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nel caso di variabili multiple:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.boxplot(by='Sex',column=['Age','Fare'], figsize=(8,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Possiamo anche combinare i boxplot con un `groupby`. Confrontiamo ad esempio le tariffe pagate da e donne appartenenti alle tre classi di imbarco:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.groupby('Pclass').boxplot(by='Sex',column=['Fare'], figsize=(12,4), layout=(1,3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.groupby('Pclass').boxplot(by='Sex',column=['Age'], figsize=(12,4), layout=(1,3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **🙋‍♂️ Domanda 9**\n",
    ">\n",
    "> Si confrontino le età di donne e uomini nelle varie classi di imbarco. In quale classe esistano differenze notevoli tra le età di uomini e donne?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Esercizi\n",
    "\n",
    "> 🧑‍💻 Esercizio 1\n",
    "> \n",
    "> Considerando il dataset Titanic, si calcolino per ogni variabile la numerosità, la media, la deviazione standard, il minimo, il massimo, il valore mediano, il primo e il terzo quartile. Dopo aver calcolato i valori richiesti individualmente, si utilizzi il metodo `describe` per ottenere tali valori. Qual è la variabile più dispersa?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 🧑‍💻 Esercizio 2\n",
    "> \n",
    "> Considerando il dataset Titanic, per ognuna delle tre classi, si calcolino medie e varianze delle età dei passeggeri. In quale classe le età sono meno disperse? Quale classe contiene i soggetti più giovani? Si completi l'analisi con dei grafici a barre."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}