{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2+2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=x+10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Esempio completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    " import numpy as np\n",
    "mu, sigma = 100, 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    " x = np.random.normal(loc=mu, scale=sigma, size=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.hist(x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}