{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Πείραμα μέτρησης ταχύτητας του φωτός"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = [28, 26, 33, 24, 34,-44, 27, 16, 40, -2, 29,\n",
    "     22, 24, 21, 25, 30, 23, 29, 31, 19, 24, 20,\n",
    "     36, 32, 36, 28, 25, 21, 28, 29, 37, 25, 28,\n",
    "     26, 30, 32, 36, 26, 30, 22, 36, 23, 27, 27,\n",
    "     28, 27, 31, 27, 26, 33, 26, 32, 32, 24, 39,\n",
    "     28, 24, 25, 32, 25, 29, 27, 28, 29, 16, 23]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.DataFrame(L)"
   ]
  },
  {
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0\n",
       "0   28\n",
       "1   26\n",
       "2   33\n",
       "3   24\n",
       "4   34\n",
       "..  ..\n",
       "61  27\n",
       "62  28\n",
       "63  29\n",
       "64  16\n",
       "65  23\n",
       "\n",
       "[66 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Υπολογισμός του πρώτου τεταρτημόριου Q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = x.quantile(0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24.0\n",
       "Name: 0.25, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Άσκηση 1 : Υπολογίστε Διάμεσο Μ, το Q3 και το IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "M = x.median()\n",
    "Q2 = x.quantile(0.5)\n",
    "Q3 = x.quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    27.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    27.0\n",
       "Name: 0.5, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    30.75\n",
       "Name: 0.75, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "IQR = Q3-Q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6.75\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Άσκηση 2:  Δημιουργήστε το Box-and-Whisker plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "x.plot.box()"
   ]
  },
  {
   "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",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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