{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Calcolo-della-dispersione-in-pandas\" data-toc-modified-id=\"Calcolo-della-dispersione-in-pandas-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Calcolo della dispersione in pandas</a></span><ul class=\"toc-item\"><li><span><a href=\"#Indici-di-dispersione\" data-toc-modified-id=\"Indici-di-dispersione-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Indici di dispersione</a></span></li><li><span><a href=\"#Box-plot\" data-toc-modified-id=\"Box-plot-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Box plot</a></span></li><li><span><a href=\"#Diagrammi-Q-Q\" data-toc-modified-id=\"Diagrammi-Q-Q-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Diagrammi Q-Q</a></span></li><li><span><a href=\"#Simmetria,-distribuzioni-approssimativamente-normali-e-regola-empirica\" data-toc-modified-id=\"Simmetria,-distribuzioni-approssimativamente-normali-e-regola-empirica-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Simmetria, distribuzioni approssimativamente normali e regola empirica</a></span></li><li><span><a href=\"#Una-nota-sulla-produzione-dei-grafici-*\" data-toc-modified-id=\"Una-nota-sulla-produzione-dei-grafici-*-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Una nota sulla produzione dei grafici <sup>*</sup></a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "header": true
   },
   "source": [
    "<div class=\"header\">\n",
    "D. Malchiodi, Superhero data science. Vol 1: probabilità e statistica: Indici di dispersione.\n",
    "</div>\n",
    "<hr style=\"width: 90%;\" align=\"left\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"h-0\"></div>\n",
    "\n",
    "# Calcolo della dispersione in pandas\n",
    "\n",
    "<div id=\"h-1\"></div>\n",
    "\n",
    "## Indici di dispersione\n",
    "\n",
    "Gli oggetti di tipo serie messi a disposizione da pandas permettono di calcolare facilmente i principali indici di dispersione. Importiamo, come al solito, il nostro dataset e impostiamo lo stile per i grafici."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.constants import golden\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.rc('figure', figsize=(5.0, 5.0/golden))\n",
    "\n",
    "\n",
    "heroes = pd.read_csv('data/heroes.csv', sep=';', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gli indici di dispersione di un oggetto di tipo serie si calcolano invocando su quest'ultimo degli appositi metodi. In particolare:\n",
    "\n",
    "- `var` restituisce la varianza campionaria;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(388.76870505203914)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year = heroes['First appearance']\n",
    "year.var()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `std` restituisce la deviazione standard campionaria;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(19.717218491766)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `describe` calcola i principali indici descrittivi di centralità e dispersione (quelli relativi a media, deviazione standard, minimo, primo quartile, mediana, terzo quartile e massimo), unitamente al numero di osservazioni nella serie, che utilizza per popolare un nuovo oggetto di tipo serie;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     367.000000\n",
       "mean     1979.855586\n",
       "std        19.717218\n",
       "min      1933.000000\n",
       "25%      1965.000000\n",
       "50%      1979.000000\n",
       "75%      1994.000000\n",
       "max      2099.000000\n",
       "Name: First appearance, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `quantile` restituisce il quantile corrispondente al livello specificato come argomento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1963.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year.quantile(.15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Va sottolineato come l'eventuale calcolo di percentili, quartili o decili campionari deve essere fatto utilizzando opportunamente il metodo `quantile`, tenendo conto delle ovvie relazioni tra l'indice che si vuole calcolare e il corrispondente quantile. Per esempio, il $35$-esimo percentile si otterrà invocando `quantile(.35)`, così come il terzo quartile verrà restituito invocando `quantile(.75)` (sebbene in quest'ultimo caso si possa utilizzare anche il metodo `describe` introdotto nel punto precedente.\n",
    "\n",
    "<div id=\"h-2\"></div>\n",
    "\n",
    "## Box plot\n",
    "\n",
    "Un _box plot_ (o _box and whiskers plot_, o ancora un _diagramma a scatola_) è una rappresentazione grafica che riassume le principali caratteristiche di un campione di dati. Tale rappresentazione contiene due componenti principali:\n",
    "\n",
    "- una _scatola_, intesa come un rettangolo che evidenzia il primo e il terzo quartile campionario dei dati, che corrispondono alle due basi, e la mediana, indicata tramite un segmento parallelo alle basi stesse;\n",
    "- due _baffi_, che si estendono dagli estremi della scatola fino a raggiungere il minimo e il massimo valore osservato.\n",
    "\n",
    "A titolo di esempio la cella seguente visualizza il box plot relativo all'anno di prima apparizione."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x309.017 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "year.plot.box(showfliers=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si verifica visualmente come questo grafico metta in evidenza:\n",
    "\n",
    "- la centralità delle osservazioni, tramite il segmento che individua la mediana campionaria;\n",
    "- la loro dispersione, sia in termini di range interquartile (l'altezza della scatola) e di intervallo di variazione dei dati (la distanza tra gli estremi dei baffi).\n",
    "\n",
    "Vale la pena sottolineare che la presenza di eventuali valori mancanti non influisce sulla generazione del grafico. È inoltre stato necessario specificare l'argomento opzionale `showfliers`. In caso contrario viene visualizzata una versione leggermente diversa di box plot, in cui le osservazioni vengono preliminarmente analizzate in modo da evidenziare eventuali _outlier_ marcandoli con dei cerchi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x309.017 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "year.plot.box()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sebbene i box plot che abbiamo ottenuto fossero visualizzati in un sistema di riferimento bidimensionale, le informazioni salienti venivano tutte reperite leggendo l'asse delle ordinate. Specificando l'argomento opzionale `vert` è possibile ottenere un diagramma equivalente visualizzando le informazioni sull'asse delle ascisse. In questo caso i valori per i quartili si leggeranno ovviamente in corrispondenza di segmenti paralleli alle altezze della scatola."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x309.017 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "year.plot.box(vert=False, showfliers=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"h-3\"></div>\n",
    "\n",
    "## Diagrammi Q-Q\n",
    "\n",
    "Un _diagramma Q-Q_ (o _diagramma quantile-quantile_) è una rappresentazione grafica che considera due campioni al fine di valutare la validità dell'ipotesi che i campioni stessi seguano una medesima distribuzione. Questi diagrammi si basano sul fatto (che non dimostreremo) che i quantili campionari rappresentano l'approssimazione di quantili teorici che, considerati tutti insieme, individuano univocamente la distribuzione dei dati.\n",
    "\n",
    "Pertanto, se due campioni hanno un'uguale distribuzione, allora estraendo da entrambi il quantile di un livello fissato si dovranno ottenere due numeri molto vicini (in quanto essi rappresentano approssimazioni diverse di uno stesso valore).\n",
    "\n",
    "Se consideriamo per esempio le altezze di due campioni di supereroi Marvel e DC, escludendo eventuali _outlier_ (limitando per la precisione la scelta a valori compresi tra 150 e 200 centimetri), i corrispondenti quantili di livello $0.2$ assumeranno i seguenti valori."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(168.994), np.float64(170.456))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marvel = heroes.loc[(heroes['Publisher']=='Marvel Comics') & \\\n",
    "                    (heroes['Height'].between(150, 200))]\n",
    "\n",
    "dc = heroes.loc[(heroes['Publisher']=='DC Comics') & \\\n",
    "                (heroes['Height'].between(150, 200))]\n",
    "\n",
    "marvel_sample = marvel['Height'].sample(120)\n",
    "dc_sample = dc['Height'].sample(120)\n",
    "\n",
    "(marvel_sample.quantile(.2), dc_sample.quantile(.2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Per ottenere il diagramma Q-Q ripetiamo ora questa operazione facendo variare i livelli in una discretizzazione che copra ragionevolmente l'intervallo $[0, 1]$ e visualizziamo sul piano cartesiano un punto per ogni coppia di quantili campionari che si riferiscono a uno stesso livello."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x309.017 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "levels = np.linspace(0, 1, 100)\n",
    "plt.plot(np.array(marvel_sample.quantile(levels)),\n",
    "         np.array(dc_sample.quantile(levels)),\n",
    "         'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il fatto che in ogni coppia considerata i due quantili fossero molto simili tra loro fa sì che i punti ottenuti si allineino approssimativamente sulla bisettrice del primo e del terzo quadrante. Possiamo evidenziare tale fatto sovrapponendo al diagramma il grafico della retta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x309.017 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([min(dc_sample), max(dc_sample)],\n",
    "         [min(dc_sample), max(dc_sample)])\n",
    "plt.plot(np.array(marvel_sample.quantile(levels)),\n",
    "         np.array(dc_sample.quantile(levels)),\n",
    "         'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In realtà non è necessario costruire \"a mano\" i diagrammi Q-Q: il package `statmodels` mette a disposizione un oggetto `api` su cui invocare il metodo `qqplot_2samples` a cui passare direttamente i due campioni, aggiungendo l'argomento opzionale `line='45'` nel caso in cui si vuole tracciare anche il riferimento della bisettrice. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x309.017 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "sm.qqplot_2samples(marvel_sample, dc_sample, line='45')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"bs-callout bs-callout-primary\">\n",
    "Il diagramma prodotto da `qqplot_2samples` scambia il ruolo degli assi rispetto al diagramma precedente: è per questo motivo che i due campioni sono stati passati come argomenti considerandoli in ordine inverso rispetto a quanto fatto finora.\n",
    "</div>\n",
    "\n",
    "In sintesi, il diagramma ottenuto ci permette di avvalorare l'ipotesi che l'altezza dei supereroi non segua una distribuzione diversa nei fumetti editi da DC e Marvel.\n",
    "\n",
    "Chiaramente, non è detto che due campioni seguano necessariamente una medesima distribuzione. In tal caso, i punti ottenuti non si disporranno vicino alla bisettrice. È questo il caso della distribuzione del peso tra supereroine e supereroi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x309.017 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "female = heroes.loc[(heroes['Gender']=='F') & \\\n",
    "                    (heroes['Weight'].between(50, 100))]\n",
    "\n",
    "male = heroes.loc[(heroes['Gender']=='M') & \\\n",
    "                (heroes['Weight'].between(50, 100))]\n",
    "\n",
    "female_sample = female['Weight'].sample(100)\n",
    "male_sample = male['Weight'].sample(100)\n",
    "sm.qqplot_2samples(female_sample, male_sample, line='45')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La tecnica del Q-Q plot permette quindi in casi come questo di confutare l'ipotesi di partenza.\n",
    "\n",
    "Si nota infine che una standardizzazione dei dati permette di confinare il grafico ottenuto in prossimità dell'origine. In tal modo diventa più facile accorgersi di eventuali valori fuori scala."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x309.017 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.qqplot_2samples((marvel_sample-marvel_sample.mean())/marvel_sample.std(),\n",
    "                   (dc_sample-dc_sample.mean())/dc_sample.std(),\n",
    "                   line='s',\n",
    "                   xlabel='DC', ylabel='Marvel')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x309.017 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.qqplot_2samples((female_sample-female_sample.mean())/female_sample.std(),\n",
    "                   (male_sample-male_sample.mean())/male_sample.std(),\n",
    "                   line='45',\n",
    "                   xlabel='Male', ylabel='Female')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"h-4\"></div>\n",
    "\n",
    "## Simmetria, distribuzioni approssimativamente normali e regola empirica\n",
    "\n",
    "Alcuni dei grafici finora visti possono essere utili per mettere in evidenza una proprietà interessante di un campione di dati legata alla simmetria delle corrispondenti frequenze. Quando le frequenze, visualizzate a seconda dei casi tramite un grafico a barre o un istogramma, tendono a distribuirsi in modo simmetrico rispetto al valore della media campionaria si dice che il campione segue una distribuzione _approssimativamente simmetrica_. Il grafico qui sotto affianca l'istogramma e il box plot di un siffatto campione. La simmetria è visibile in entrambe le rappresentazioni: l'istogramma è approssimativamente simmetrico rispetto alla sua parte centrale e nel box plot i baffi hanno all'incirca la stessa lunghezza, così come la mediana si posiziona verso il centro della scatola."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "sample = pd.Series(norm.rvs(24, 2, size=1000), name='')\n",
    "f, (h, b) = plt.subplots(2, 1, gridspec_kw = {'height_ratios':[4, 3]})\n",
    "f.set_figheight(6)\n",
    "\n",
    "h.yaxis.label.set_visible(False)\n",
    "\n",
    "sample.plot.hist(bins=20, ax=h)\n",
    "sample.plot.box(vert=False, showfliers=False, ax=b, widths=.4, label=None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'asimmetria in una distribuzione si può invece presentare in due diverse modalità:\n",
    "\n",
    "- tende a essere presente una \"coda\" nella parte destra della distribuzione delle frequenze, evidenziata da valori più bassi delle frequenze nella parte destra dell'istogramma e da un baffo destro sensibilmente più lungo nel box plot, come esemplificato nella coppia di diagrammi qui sotto; in questo caso si parla quindi di distribuzione asimmetrica a destra (utilizzando a volte la terminologia inglese e dicendo che è presente uno _skew_ a destra);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import gamma\n",
    "sample = pd.Series(gamma.rvs(2, size=1000), name='')\n",
    "f, (h, b) = plt.subplots(2, 1, gridspec_kw = {'height_ratios':[4, 3]})\n",
    "f.set_figheight(6)\n",
    "\n",
    "h.yaxis.label.set_visible(False)\n",
    "\n",
    "sample.plot.hist(bins=20, ax=h)\n",
    "sample.plot.box(vert=False, showfliers=False, ax=b, widths=.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- viceversa, è possibile che la coda della distribuzione sia a sinistra, come nel grafico sottostante, e quindi si parla di asimmetria (o _skew_) a sinistra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import beta\n",
    "sample = pd.Series(beta.rvs(20, 2, size=1000), name='')\n",
    "f, (h, b) = plt.subplots(2, 1, gridspec_kw = {'height_ratios':[4, 3]})\n",
    "f.set_figheight(6)\n",
    "\n",
    "h.yaxis.label.set_visible(False)\n",
    "\n",
    "sample.plot.hist(bins=20, ax=h)\n",
    "sample.plot.box(vert=False, showfliers=False, ax=b, widths=.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consideriamo per esempio il BMI dei supereroi nei soli casi in cui il relativo valore è inferiore a $100$, e visualizziamo separatamente i corrispondenti istogramma e box plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = heroes['Weight'] / (heroes['Height']/100) **2\n",
    "sample = sample[sample < 100]\n",
    "\n",
    "sample.name = 'BMI'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x309.017 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample.plot.hist(bins=20)\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x309.017 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample.plot.box(vert=False, showfliers=False, widths=.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si vede chiaramente la presenza di un'asimmetria a destra. Se invece consideriamo l'altezza, restringendosi ai casi in cui il corrispondente valore è compreso tra $150$ e $220$, otteniamo una distribuzione approssimativamente simmetrica."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAEmCAYAAABChbjLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8ekN5oAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAgsklEQVR4nO3df1jV9f3/8Qdi+OOclJmCGqiAWir+mKswfzTDtBxlptYqZ1S2FeqU2EV6WW5smT+iEqdpWk3mj+lyM41gNZKcpUVZKS5Lrg6RkMSVbcAOiaDw/ePrOR9PHNADB3hxuN+uqyt9v17n/X49fZ/Dg9f71/ErKSmpEQAAMFa7lh4AAACoH2ENAIDhCGsAAAxHWAMAYDjCGgAAwxHWAAAYjrAGAMBwhDUAAIYjrAEAMBxhDQCA4QhrAAAMR1g3g4qKCuXl5amioqKlh9KsqLtt1a02XDt1U3dTI6ybyblz51p6CC2Cutuetlo7dbctzV03YQ0AgOEIawAADEdYAwBgOMIaAADDEdYAABiOsAYAwHCENQAAhiOsAQAwXPuWHgDg6wI3fe3V9ZU8cKVX1wfAfMysAQAwHGENAIDhCGsAAAxHWAMAYDjCGgAAwxHWAAAYjrAGAMBwhDUAAIYjrAEAMBxhDQCA4QhrAAAMR1gDAGA4whoAAMMR1gAAGI6wBgDAcIQ1AACGI6wBADAcYQ0AgOEIawAADEdYAwBguAaHdVpamqZOnaqwsDAFBwdr2LBhmj17tgoLC136lZWVafHixYqMjFRQUJCGDh2qJUuWyG63e2P8AAD4vPaevqCmpkaPPvqoUlNTFRYWpunTp8tqtaqoqEgHDhxQQUGBQkJCJEnl5eWKiYnR0aNHFR0drRkzZignJ0dr1qzRgQMHlJGRoY4dOzZFXQAA+AyPw/qFF15QamqqHnroIa1cuVL+/v4u7WfPnnX+efXq1Tp69Kji4+OVlJTkXJ6UlKSUlBStW7dOCQkJja0BAACf5tFh8NOnT2vlypXq16+fVqxYUSuoJal9+/+f/zU1NdqyZYusVqsSExNd+iQmJspqtWrz5s2NHT8AAD7Po5l1VlaWSkpKNHPmTJ07d04ZGRmy2Wzq2rWrxo8fr/DwcGdfm82moqIiTZgwQRaLxWU9FotFUVFR2rt3rwoLC52HzQEAQG0ehfXhw4clSf7+/hozZoy++OILZ1u7du00Z84cLV26VDof1pJcAvxC4eHh2rt3r2w2G2ENAEA9PArrU6dOSZKef/55DR8+XFlZWRo4cKBycnIUHx+vtWvXKiwsTLNnz1ZZWZkkqWvXrm7X1aVLF+n81eIXU1FR4ckwjVNZWeny/7aCupumbpM/D+xz6m4LLrVub15A7VFYV1dXS5ICAgK0bds29erVS5I0evRopaamauzYsVq7dq1mz57ttQFK0smTJ3Xu3DmvrrMlFBcXt/QQWgR1d/bqegsKCry6vqbAPm9bqLs2f3//Oo8sN4RHYe2YDY8YMcIZ1A6DBw9Wv379lJeXp5KSEmff0tJSt+tyzKgd/erTu3dvT4ZpnMrKShUXFys4OFgBAQEtPZxmQ92Our/z6vpDQ0O9uj5vYp9Td1vQEnV7FNYDBgyQ6jm07VheUVGhiIgISVJeXp7bvo7ljn718ZV7sQMCAnymFk9Qt3e1hn9L9nnbQt1Nz6OwHjdunCQpNze3VltVVZXy8vJksVjUvXt3BQcHq1evXsrOzlZ5ebnLFeHl5eXKzs5W3759ubgMAICL8Og+67CwMEVHRysvL6/WPdKrVq1SaWmpYmJi1L59e/n5+WnWrFmy2+1KTk526ZucnCy73a7Y2FjvVAEAgA/z+Almzz77rCZNmqT58+crPT1dAwYMUE5Ojvbv36/Q0FA9+eSTzr4LFixQRkaGUlJSlJOTo+HDh+vIkSPKysrSyJEjFRcX5+16AADwOR5/kUdYWJjefvtt3XvvvTp8+LA2bNigvLw8/fKXv1RWVpaCg4OdfS0Wi9LT0xUXF6fc3FytXbtWubm5mjdvnvbs2aNOnTp5ux4AAHyOxzNrSQoJCdG6desuqW/Xrl21fPlyLV++vCGbAgCgzeP7rAEAMBxhDQCA4QhrAAAMR1gDAGA4whoAAMMR1gAAGI6wBgDAcIQ1AACGI6wBADAcYQ0AgOEIawAADEdYAwBgOMIaAADDNehbt9D2BG76uoGv7Czpu1pLSx64stFjAoC2gpk1AACGI6wBADAch8GBVqbhpyTc45QEYD5m1gAAGI6wBgDAcIQ1AACG45w10MZ5+xz4h2O9ujoAzKwBADAfYQ0AgOEIawAADEdYAwBgOMIaAADDEdYAABiOW7cANxp/O5P7bxsDgIZgZg0AgOEIawAADEdYAwBgOMIaAADDEdYAABiOsAYAwHCENQAAhiOsAQAwHGENAIDhCGsAAAxHWAMAYDjCGgAAwxHWAAAYjrAGAMBwhDUAAIYjrAEAMBxhDQCA4QhrAAAMR1gDAGA4whoAAMMR1gAAGI6wBgDAcIQ1AACGI6wBADAcYQ0AgOEIawAADEdYAwBgOMIaAADDeSWsU1JSFBgYqMDAQH344Ye12svKyrR48WJFRkYqKChIQ4cO1ZIlS2S3272xeQAAfFqjw/rYsWNavny5LBaL2/by8nLFxMRo3bp1GjhwoObMmaMBAwZozZo1mjJliioqKho7BAAAfFqjwrqqqkpxcXEaOnSoYmJi3PZZvXq1jh49qvj4eO3atUtJSUnatWuX4uPj9fHHH2vdunWNGQIAAD6vUWH9zDPP6PPPP9fatWvl7+9fq72mpkZbtmyR1WpVYmKiS1tiYqKsVqs2b97cmCEAAODzGhzWhw8f1rPPPquFCxfq6quvdtvHZrOpqKhIUVFRtQ6TWywWRUVFKT8/X4WFhQ0dBgAAPq99Q1505swZ5+HvBQsW1NnPZrNJksLDw922h4eHa+/evbLZbAoJCalzPa39vHZlZaXL/9H69ynq19be6231M07d9dfdsWNHr22zQWG9bNky2Ww27du3z+3hb4eysjJJUteuXd22d+nSxaVfXU6ePKlz5841ZKhGKS4ubukhNEJnr66toKDAq+vzPu/W29a07vd6w1F321Jf3f7+/nVOVBvC47D+4IMPtGbNGi1atEiDBw/22kDq07t372bZTlOprKxUcXGxgoODFRAQ0NLDaaDvvLq20NBQr67P+7xbb1vTut/rnvONz7jnqLv56vYorM+ePau4uDgNGTJEjz766EX7O2bOpaWlbtsdM2pHv7p481BCSwoICPCZWhqLfwff1lbf69TdtjRn3R6Ftd1ud56H7tGjh9s+EydOlCRt3brVeeFZXl6e276O5REREZ6NGgCANsSjsO7QoYNmzZrltu3gwYOy2WyaPHmyunfvrj59+igiIkK9evVSdna2ysvLXa4ILy8vV3Z2tvr27VvvxWUAALR1HoV1p06dtGbNGrdtcXFxstlsSkhI0LXXXutcPmvWLD399NNKTk5WUlKSc3lycrLsdrsSEhIaM34AAHxeg64G98SCBQuUkZGhlJQU5eTkaPjw4Tpy5IiysrI0cuRIxcXFNfUQAABo1Zr8W7csFovS09MVFxen3NxcrV27Vrm5uZo3b5727NmjTp06NfUQAABo1bw2s16/fr3Wr1/vtq1r165avny5li9f7q3NAQDQZvB91gAAGI6wBgDAcIQ1AACGI6wBADAcYQ0AgOEIawAADEdYAwBgOMIaAADDEdYAABiOsAYAwHCENQAAhiOsAQAwXJN/RSbQHAI3fd3SQwCAJsPMGgAAwxHWAAAYjrAGAMBwhDUAAIYjrAEAMBxhDQCA4QhrAAAMR1gDAGA4whoAAMMR1gAAGI6wBgDAcIQ1AACGI6wBADAcYQ0AgOEIawAADEdYAwBgOMIaAADDEdYAABiOsAYAwHCENQAAhiOsAQAwHGENAIDhCGsAAAzXvqUHAMC3XPtuZ0nfeW19JQ9c6bV1Aa0VM2sAAAxHWAMAYDjCGgAAw3HO2ocFbvq6pYcAGMXbnwnOp6O5MLMGAMBwhDUAAIYjrAEAMBxhDQCA4QhrAAAMR1gDAGA4whoAAMMR1gAAGI6wBgDAcIQ1AACGI6wBADAcYQ0AgOEIawAADEdYAwBgOI/C+uTJk1q3bp3uuOMORUZGqkePHho4cKBmzZqlQ4cOuX1NWVmZFi9erMjISAUFBWno0KFasmSJ7Ha7t2oAAMCnefR91hs3blRKSorCwsJ04403qnv37rLZbEpPT1d6erpeeuklTZs2zdm/vLxcMTExOnr0qKKjozVjxgzl5ORozZo1OnDggDIyMtSxY8emqAsAAJ/hUViPHDlSr7/+usaOHeuy/ODBg7r99tuVkJCgmJgYdejQQZK0evVqHT16VPHx8UpKSnL2T0pKUkpKitatW6eEhARv1QIAgE/y6DD4lClTagW1JI0ePVrjxo1TSUmJjh07JkmqqanRli1bZLValZiY6NI/MTFRVqtVmzdvbuz4AQDweV67wOyyyy6TJPn7+0uSbDabioqKFBUVJYvF4tLXYrEoKipK+fn5Kiws9NYQAADwSR4dBq9LQUGB9u3bp549e2rIkCHS+bCWpPDwcLevCQ8P1969e2Wz2RQSElLv+isqKrwxzBZTWVnp8n+0/n2K5mPye8Uxtrb6Gafu+uv25jVZjQ7rqqoqPfzwwzpz5oySkpKcM+uysjJJUteuXd2+rkuXLi796nPy5EmdO3eusUNtccXFxc28xc7NvL1LV1BQ4OU1mlsrGse77xXvvk9+OLbm/4ybgbpr8/f3r3Oy2hCNCuvq6mrNmTNHBw8eVGxsrO6++26vDexCvXv3bpL1NpfKykoVFxcrODhYAQEBzbjl75pxW54JDQ318hrNrRWN4933inffJ46xtdxnvGVRd/PV3eCwrq6u1ty5c7Vz507dddddWrVqlUu7Y+ZcWlrq9vWOGbWjX3185faugIAAn6mlsfh3wKUy+b3yw7G11c84dTe9BoW1Y0a9Y8cOzZgxQ+vXr1e7dq7XqkVEREiS8vLy3K7DsdzRDwAAuOfx1eAXBvW0adO0YcMG53nqC0VERKhXr17Kzs5WeXm5S1t5ebmys7PVt2/fi15cBgBAW+dRWDsOfe/YsUNTp07Vxo0b3Qa1JPn5+WnWrFmy2+1KTk52aUtOTpbdbldsbGzjRg8AQBvg0WHwlStXavv27bJarerfv3+tEJakmJgYDRs2TJK0YMECZWRkKCUlRTk5ORo+fLiOHDmirKwsjRw5UnFxcd6rBAAAH+VRWJ84cUKSZLfb9cwzz7jt06dPH2dYWywWpaena8WKFUpLS9M777yj4OBgzZs3TwsXLlSnTp28UQNaocBNX7f0ENBK8F4BJL+SkpKalh6Er6uoqFBBQYFCQ0Ob9YpJfsgBTavkgSulFvyMtzTqbr66+T5rAAAMR1gDAGA4whoAAMMR1gAAGI6wBgDAcIQ1AACGI6wBADAcYQ0AgOEIawAADEdYAwBgOMIaAADDEdYAABiOsAYAwHCENQAAhiOsAQAwHGENAIDhCGsAAAxHWAMAYDjCGgAAwxHWAAAYjrAGAMBwhDUAAIYjrAEAMFz7lh4AALRWgZu+vuBvnSV916j1lTxwZaPHBN/EzBoAAMMR1gAAGI6wBgDAcIQ1AACGI6wBADAcYQ0AgOEIawAADEdYAwBgOMIaAADDtaknmLk+bajxvP20IW+PDwDgG5hZAwBgOMIaAADDEdYAABiuTZ2zBgCYwfRriEzDzBoAAMMR1gAAGI6wBgDAcIQ1AACGI6wBADAcYQ0AgOG4dQsADGHy7Ux1j62zpO+8th24x8waAADDEdYAABiOsAYAwHCENQAAhiOsAQAwHGENAIDhuHULANDqmXzbmzcwswYAwHCENQAAhuMwOAD4KG8fGkbLabaZ9ccff6w777xTffr0Ue/evXXTTTfp1Vdfba7NAwDQajXLzHr//v2aPn26OnbsqGnTpslqteq1117TAw88oMLCQv36179ujmEAANAqNXlYnz17VgsWLFC7du2Unp6uYcOGSZIee+wxTZgwQU8++aRuv/129enTp6mHAgBAq9TkYb1//359+eWXmjlzpjOoJalr165KSEjQnDlztH37di1cuLCph6IrOnj3qH/EX4o86G2VPvxvvT28PT4AQNPw9/dv1u01eVi/++67kqTo6OhabRMmTJAkHThwoKmHIUmy3durWbYDAPBdHTt2VHh4eLNus8mncjabTZIUERFRqy04OFhWq1V5eXlNPQwAAFqtJg/rsrIySVKXLl3ctl9++eXOPgAAoDZOkgIAYLgmD2vHjLqu2fP//ve/OmfdAACgGcLaca7ace76QsXFxbLb7c1+oh4AgNakycN6zJgxkqSsrKxabXv37nXpAwAAavMrKSmpacoNnD17Vtdcc42KioqUmZnpvNe6tLRUEyZM0IkTJ/Thhx+qb9++TTkMAABarSafWbdv315//OMfVV1drZiYGC1YsECPP/64xo4dqy+++EJLlixpFUH917/+VfHx8Ro/fryCgoIUGBiobdu21fua/Px8zZ8/X5GRkQoKCtKAAQN06623avfu3W77v/LKK4qOjlbv3r3Vt29f/fznP9fhw4ebqKJL40ndgYGBF/2vsLCw1utae906f5pnzpw5GjlypHr27KlBgwZp6tSpysjIqPM1vlD3oUOHdM899yg8PFxBQUEaOXKknnrqKZ0+fbrO15hY98mTJ7Vu3TrdcccdioyMVI8ePTRw4EDNmjVLhw4dcvuasrIyLV682Pn5Hjp0qJYsWSK73e62f3V1tTZs2KDRo0erZ8+eioiI0OzZs5Wfn9/E1dXN07pzcnL0hz/8QdOmTVNERIQCAwMVExNz0e2Yts89qbuqqkp79uzRI488ouuuu05XXnmlQkJCNGHCBL388ss6d+5cndvxZt1NPrN2+Oijj7R8+XJ98MEHqqqq0uDBgzV37lxNmzatOTbfaEOHDlVBQYGuuOIKde7cWQUFBXr++ec1c+ZMt/3ffvttZ9stt9yifv36qaSkRJ9++qkGDRqklJQUl/7PPPOMli5dqtDQUE2ZMkV2u127du1SZWWl9uzZo1GjRjVLnT/kSd3Lly93u44vv/xSr7zyiq6++mq9//77Lm2+UPehQ4d02223qaqqSpMnT1ZERIS+/fZbpaWlqaysTIsWLdKiRYtcXuMLdb/22mt68MEH5e/vrylTpigoKEjZ2dk6dOiQRo0apT179qhDhw4urzG17qSkJKWkpCgsLExjx45V9+7dZbPZlJ6erpqaGr300ksuP6vKy8t1yy236OjRo4qOjtawYcOUk5OjrKwsjRw5UhkZGerYsaPLNubPn6/Nmzdr0KBBmjRpkoqKirR7925ZLBa99dZbbp9FYVrdy5cv18qVKxUQEKD+/fvr2LFjGjNmjNLT0+vchon73JO6c3Nzdd1118lqteqGG27QgAEDVFZWpjfeeENFRUW6+eabtWPHDvn5+TVp3c0W1q3dvn37FB4erj59+mjVqlX6/e9/X+cPsYKCAo0ZM0Y9evTQ7t27FRoa6tJ+9uxZtW//fw+Ps9lsioqKUr9+/bR371517dpVOv9b7MSJE9WvXz+99957ateu+e+086TuuiQmJurFF1/U0qVLNW/ePOdyX6n7zjvvVGZmprZt2+Yyyzhx4oTGjBmjs2fPKj8/3xlcvlD36dOnNXToUJWWliozM1MjRoyQJNXU1Oixxx7Tiy++qN/97nd69NFHna8xue7XXntN3bp109ixY12WHzx4ULfffrssFouOHz/u3IfLli3T008/rfj4eCUlJTn7O0Lgt7/9rRISEpzL9+/frylTpmj06NHavXu3AgICJEmZmZm68847FR0drV27djVbvQ6e1v3ZZ5/pzJkzGjJkiP7zn//oqquuqjesTd3nntR98uRJZWRk6J577pHFYnH2LS8v16233qpPPvlEqampmjp1qrOtKermPutLNH78+Ev+spHnnntOZWVleu6552oFtc6fGrjQtm3bdPbsWf3mN79x7lRJGjZsmKZPn67jx4/rvffe80IVnvOkbncqKiq0c+dOBQQE6O6773Zp85W68/Pz5efnp4kTJ7os79OnjwYPHqzTp0+rvLzcudwX6v7ggw906tQpxcTEOINakvz8/PT4449Lkv70pz+ppub/5gIm1z1lypRaP7glafTo0Ro3bpxKSkp07Ngx6fwvJFu2bJHValViYqJL/8TERFmtVm3evNlluePvjz/+uDOoJWnixIkaO3assrKyVFBQ0ETV1c2TuiVp0KBBGjFihC677LJLWr+p+9yTunv37q2HHnrIJaglyWKxaO7cuZKbR2Y3Rd2EtZfV1NRo9+7d6tatm37605/q8OHDWrt2rdasWaN9+/apurq61mtMen66t6WlpamkpESTJ09W9+7dXdp8pe5BgwappqZGmZmZLssLCgp07NgxRUZGqlu3bs7lvlB3cXGxJLm93sRxfUJBQYHL+djWWrcjmBxf3GCz2VRUVKSoqCi3P8CjoqKUn5/vcn3Gu+++K4vF4vbQp6m1/7DuhmiN+9yTuuvq2xR1N8v3WbclX331lf773//qxz/+seLj45WamurSPmzYMG3fvl1XXnmlc5nNZpPValVwcHCt9dV3n3prsGXLFknSfffdV6vNV+p+4oknlJ2drdjYWE2ePFn9+/d3nrPu16+fNm3a5NLfF+q+4oorpPPv9x8qLS1VSUmJJOmLL75QWFiY1ErrLigo0L59+9SzZ08NGTJEumCMdT0fIjw8XHv37pXNZlNISIjKy8v1zTffaPDgwW4DwLEek2p3V3dDtLZ97mndW7duldyEclPUzczay7799lvp/LmJv/3tb3r++eeVn5+vI0eOKDY2Vjk5OYqNjXV5TVlZWb3PTlc9T4AzWX5+vt555x2FhIToxhtvrNXuK3UPHDjQeVtiWlqaVq1apa1bt8rf318zZ850hpWDL9QdFRWlLl26KD09XUeOHHFpW7ZsmfPPpaWlzj+3trqrqqr08MMP68yZM0pKSnIGrWOMFx7evNAPn9p4se9HuNhTHptbXXU3RGva557WnZqaqszMTN1www2aNGmSS1tT1E1Ye5njMPe5c+e0ePFizZw5U4GBgerbt69Wr16ta665RocOHWqxc3PNaevWraqpqdHMmTNb5KKh5vLRRx9p0qRJCgwM1L59+3Ty5EkdPnxYd999txYtWqTZs2e39BC9zmq1aunSpaqqqtKkSZP0q1/9Sk888YQmTZqkTZs2aeDAgZLUavd7dXW15syZo4MHDyo2NrbW9Ra+irovre433nhDiYmJCg0N1caNG5tljK3zk2SwC3+b+tnPflar/ZZbbpEkffLJJy6vqe/Z6T9cb2tQXV2t7du3q127dvrFL37hto8v1F1VVaUHH3xQ7dq109atWzVixAh17txZ/fr107JlyxQTE6Pdu3e73LLmC3Xr/KmNnTt36tprr1VGRoZefvlltW/fXnv27HEeTbjwOoXWUnd1dbXmzp2rnTt36q677tKqVatc2h1jvPCowYV+OJO+2Mz5YjPv5nKxuhuiNexzT+v+5z//qdjYWAUFBSktLU09e/as1acp6iasvSwsLMx5+MTdYTLHsoqKCueyiIgI2e1250U7F6rv+8BN9tZbb+nrr7/WjTfe6PaKePlI3bm5ufrqq6/0k5/8RJ07d67VPm7cOOn8aREHX6jbYeLEiXr99ddVWFiooqIiZWRk6Prrr9dnn32mdu3aafjw4c6+raFuxwxr+/btmjFjhtavX1/r6IBjjHl5eW7X4Vju6GexWNSzZ0999dVXbh+g8cP+LeFS6m4I0/e5p3W/+eabmjVrlq644grnNSnuNEXdhLWXdezYUdddd50k6fPPP6/Vfvz4cen8bT0Ovvj89PouLHPwhbqrqqokSadOnXLb7lh+4cNBfKHu+rz//vs6ceKEbrrpJpdfWE2v2/GDe8eOHZo2bZo2bNjg9rxlRESEevXqpezsbJdb8nT+3tvs7Gz17dtXISEhzuVjxoxReXl5rYcC6YLaR48e3SR1Xcyl1t0QJu9zT+t+8803dd999+lHP/qR0tLS6v0Cqqaom7BuAo5zlCtWrNCZM2ecy3Nzc/WXv/xFl19+uW666Sbn8pkzZ6p9+/Z69tlnXQ6t5eTk6O9//7uuuuoqXX/99c1cRcOdOnVKb7zxhrp3767JkyfX2c8X6h40aJC6dOmi7OzsWh/MwsJCpaamys/Pz+WD6Qt1q47DukVFRZo/f77at2+vxYsXu7SZXLfjUOiOHTs0depUbdy4sc4f3H5+fpo1a5bsdruSk5Nd2pKTk2W322tdROr4+1NPPaXKykrn8szMTL377ruKjo5u1PMMGsqTuhvC1H3uad2ZmZm67777FBgYqLS0tIvOipuibp5gdok2b97svCjs2LFjOnLkiEaNGuU8N3f99dc7Z5E1NTW6//77tWfPHg0YMEDR0dEqKytTWlqavv/+e73wwgu66667XNZv4iP55GHdDmvWrNGSJUs0d+5cPfXUU/Wu3xfqTk1NVXx8vNq1a6ebb75ZAwcOVHFxsV5//XXZ7XbNmzdPS5cudVm/L9SdnJysV155RaNGjVKPHj1UWFiof/zjH/r++++1Zs0a3XvvvbXWb2rdjsdoWq1WPfLII25/cMfExDi/iKi8vFw333yz/v3vfys6OlrDhw/XkSNHnI8bTU9PV6dOnVxe/8PHjX7zzTd69dVXZbFYlJmZqf79+zdbvQ6e1p2bm+s8p1tRUaFXX31VQUFBznuHJWn9+vUurzdxn3tSd25ursaNG6czZ85o+vTpbvdTnz59aj3lj8eNtpC4uDht3769zvZ77rnH5U169uxZbdiwQVu3blVeXp46dOiga665RgkJCW6fnKPzD31fv369Pv/8c1122WUaNWqUFi9e7PKEqObmad06f1vP8ePHlZ2drauuuuqi2/CFut9++2298MILOnTokEpKSmSxWBQZGan777+/1i9mDq297n/9619atWqVPv30U5WUlKhbt24aM2aMFixY4HKu+odaY92Saj12tbS0VCtWrFBaWpqKi4sVHBysqVOnauHChc7bcy5UXV2tjRs36s9//rPy8vJksVg0fvx4LVmypNbtfc3F07rfeecd3XbbbfX2d9xjfyHT9rkndV9KzXU9ctWbdRPWAAAYjnPWAAAYjrAGAMBwhDUAAIYjrAEAMBxhDQCA4QhrAAAMR1gDAGA4whoAAMMR1gAAGI6wBgDAcIQ1AACGI6wBADAcYQ0AgOH+H/pqBnEJ+OMkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x309.017 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = heroes[(heroes['Height'].between(150, 220))]['Height']\n",
    "sample.plot.hist(bins=20)\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x309.017 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample.plot.box(vert=False, showfliers=False, widths=.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tra le distribuzioni approssimativamente simmetriche, un ruolo particolare spetta alle cosiddette distribuzioni _approssimativamente normali_, in cui la simmetria è accompagnata da una forma \"a campana\" del grafico delle frequenze. In questo tipo di distribuzioni i dati si concentrano attorno alla media campionaria secondo la seguente _regola empirica_:\n",
    "\n",
    "- approssimativamente il 68% delle osservazioni dista dalla media campionaria non più di una deviazione standard campionaria;\n",
    "- approssimativamente il 95% delle osservazioni dista dalla media campionaria non più di due deviazioni standard campionarie;\n",
    "- approssimativamente il 99.7% delle osservazioni dista dalla media campionaria non più di tre deviazioni standard campionarie.\n",
    "\n",
    "Siccome il grafico delle frequenze dell'altezza ha approssimativamente un andamento a campana, possiamo controllare numericamente se questa regola empirica risulti verificata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.672269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.947479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.993697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          %\n",
       "1  0.672269\n",
       "2  0.947479\n",
       "3  0.993697"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def check_empirical_rule(n):\n",
    "    within = len(sample[np.abs(sample - sample.mean()) < n*sample.std()])\n",
    "    return  within / len(sample)\n",
    "\n",
    "pd.DataFrame([check_empirical_rule(n) for n in range(1, 4)], columns=['%'],\n",
    "             index=range(1, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I risultati ottenuti sono altamente in accordo con le percentuali sopra indicate, e quindi l'ipotesi iniziale che le altezze considerate fossero distribuite in modo approssimativamente normale risulta validata.\n",
    "\n",
    "<div id=\"h-5\"></div>\n",
    "\n",
    "## Una nota sulla produzione dei grafici <sup>*</sup>\n",
    "\n",
    "I lettori più curiosi si saranno probabilmente chiesti come mai gli esempi riportati all'inizio del paragrafo precedente affiancassero istogrammi e box plot, mentre quando è stato mostrato come analizzare lo stato di simmetria della distribuzione di un campione questi due grafici sono stati costruiti separatamente. Ciò è dovuto al fatto che l'utilizzo di `subplot` permette di creare più grafici in una stessa figura, con il vincolo che le loro dimensioni devono essere uguali. Questo avrebbe avuto come effetto quello di ottenere un istogramma con delle barre molto basse, o in alternativa un box plot con un'altezza troppo elevata. In entrambi i casi si sarebbe quindi generato un grafico non particolarmente agevole da leggere. Una soluzione a questo problema, non introdotta prima per non complicare inutilmente la spiegazione, consiste nell'utilizzare come nella cella seguente il metodo `plt.subplots`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (h, b) = plt.subplots(2, 1, gridspec_kw = {'height_ratios':[4, 3]})\n",
    "f.set_figheight(6)\n",
    "\n",
    "h.yaxis.label.set_visible(False)\n",
    "\n",
    "sample.plot.hist(bins=20, ax=h)\n",
    "sample.plot.box(vert=False, showfliers=False, ax=b, widths=.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vediamo nell'ordine quali sono i punti interessanti nel codice che ha prodotto questo grafico:\n",
    "\n",
    "1. il metodo `plt.subplots` accetta due argomenti il cui significato è lo stesso di quelli di `plt.subplot`: pertanto nella prima riga si genera una figura con due righe, ognuna contenente un asse;\n",
    "2. lo stesso metodo restituisce dei riferimenti alla figura e ai due assi;\n",
    "3. l'argomento opzionale `gridspec_kw` permette di specificare un dizionario con informazioni addizionali: nel nostro caso le altezze relative dei due assi;\n",
    "4. usando il riferimento alla figura è possibile invocare il metodo `set_figheight` al fine di impostare l'altezza globale della figura;\n",
    "5. analogamente, tramite il riferimento agli assi è possibile disattivare la visualizzazione dell'etichetta sull'asse delle ascisse dell'istogramma (tale etichetta conterrebbe il testo `'Frequency'`, dunque non sarebbe particolarmente informativa);\n",
    "6. l'invocazione dei metodi `hist` e `box` viene fatta specificando per l'argomento opzionale `ax` i riferimenti agli assi restituiti da `plt.subplots`;\n",
    "7. infine, nella creazione del box plot viene specificato un valore per l'argomento opzionale `width` che permette di ottenere un grafico in cui la scatola ha un'altezza più alta di quella predefinita, al fine di migliorare la leggibilità del risultato."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "footer": true
   },
   "source": [
    "<hr style=\"width: 90%;\" align=\"left\" />\n",
    "<span style=\"font-size: 0.8rem;\">D. Malchiodi, Superhero data science. Vol 1: probabilità e statistica: Indici di dispersione, 2017.</span>\n",
    "<br>\n",
    "<span style=\"font-size: 0.8rem;\">Powered by <img src=\"img/jupyter-logo.png\" style=\"height: 1rem; display: inline; margin-left: 0.5ex; margin-top: 0;\" alt=\"Jupyter Notebook\"></span>\n",
    "<div style=\"float: left; margin-top: 1ex;\">\n",
    "<img src=\"http://mirrors.creativecommons.org/presskit/icons/cc.large.png\" style=\"width: 1.5em; float: left; margin-right: 0.6ex; margin-top: 0;\">\n",
    "<img src=\"http://mirrors.creativecommons.org/presskit/icons/by.large.png\" style=\"width: 1.5em; float: left; margin-right: 0.6ex; margin-top: 0;\">\n",
    "<img src=\"http://mirrors.creativecommons.org/presskit/icons/nc.large.png\" style=\"width: 1.5em; float: left; margin-right: 0.6ex; margin-top: 0;\">\n",
    "<img src=\"http://mirrors.creativecommons.org/presskit/icons/nd.large.png\" style=\"width: 1.5em; float: left; margin-right: 0.6ex; margin-top: 0;\">\n",
    "<span style=\"font-size: 0.7rem; line-height: 0.7rem; vertical-align: middle;\">Quest'opera è distribuita con Licenza <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-nd/4.0/\">Creative Commons Attribuzione - Non commerciale - Non opere derivate 4.0 Internazionale</a></span>.\n",
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