HW1_Dogukan Akgol.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>Processor</th>\n",
       "      <th>RAM(GB)</th>\n",
       "      <th>Price(TL)</th>\n",
       "      <th>ScreenSize(inch)</th>\n",
       "      <th>Weight(kg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Huawei</td>\n",
       "      <td>Matebook D15</td>\n",
       "      <td>Ryzen5</td>\n",
       "      <td>8</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HP</td>\n",
       "      <td>340S G7</td>\n",
       "      <td>Intel i3</td>\n",
       "      <td>8</td>\n",
       "      <td>5339.99</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Ideapad3</td>\n",
       "      <td>Ryzen3</td>\n",
       "      <td>4</td>\n",
       "      <td>4307.16</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Huawei</td>\n",
       "      <td>Matebook D13</td>\n",
       "      <td>intel i5</td>\n",
       "      <td>8</td>\n",
       "      <td>7553.00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Huawei</td>\n",
       "      <td>Matebook D14</td>\n",
       "      <td>Ryzen5</td>\n",
       "      <td>8</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Brand         Model Processor  RAM(GB)  Price(TL)  ScreenSize(inch)  \\\n",
       "0  Huawei  Matebook D15    Ryzen5        8    4999.00              15.6   \n",
       "1      HP       340S G7  Intel i3        8    5339.99              14.0   \n",
       "2  Lenovo      Ideapad3    Ryzen3        4    4307.16              14.0   \n",
       "3  Huawei  Matebook D13  intel i5        8    7553.00              13.0   \n",
       "4  Huawei  Matebook D14    Ryzen5        8    4999.00              14.0   \n",
       "\n",
       "   Weight(kg)  \n",
       "0        1.53  \n",
       "1        1.47  \n",
       "2        1.42  \n",
       "3        1.30  \n",
       "4        1.38  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "data = {\"Brand\": [\"Huawei\",\"HP\",\"Lenovo\",\"Huawei\",\"Huawei\"],\n",
    "\t\t\"Model\": [\"Matebook D15\",\"340S G7\",\"Ideapad3\",\"Matebook D13\",\"Matebook D14\"],\n",
    "\t\t\"Processor\": [\"Ryzen5\",\"Intel i3\",\"Ryzen3\",\"intel i5\",\"Ryzen5\"],\n",
    "\t\t\"RAM(GB)\": [8, 8, 4, 8, 8],\n",
    "\t\t\"Price(TL)\": [4999, 5339.99, 4307.16, 7553, 4999],\n",
    "        \"ScreenSize(inch)\": [15.6, 14, 14, 13, 14],\n",
    "        \"Weight(kg)\": [1.53, 1.47, 1.42, 1.3, 1.38]}\n",
    "\n",
    "df1 = pd.DataFrame(data)\n",
    "df1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Feature(Attribute) Types:\n",
    "Brand---- Categorical | Nominal\n",
    "Model---- Categorical | Nominal\n",
    "Processor---- Categorical | Nominal\n",
    "RAM(GB)---- Numeric | Ratio\n",
    "Price(TL)---- Numeric | Ratio\n",
    "ScreenSize(inch)---- Numeric | Ratio\n",
    "Weight(kg)---- Numeric | Ratio\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Brand</th>\n",
       "      <th>Model</th>\n",
       "      <th>Processor</th>\n",
       "      <th>RAM(GB)</th>\n",
       "      <th>Price(TL)</th>\n",
       "      <th>ScreenSize(inch)</th>\n",
       "      <th>Weight(kg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Huawei</td>\n",
       "      <td>Matebook D14</td>\n",
       "      <td>Ryzen5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>5439.630000</td>\n",
       "      <td>14.120000</td>\n",
       "      <td>1.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.788854</td>\n",
       "      <td>1239.663138</td>\n",
       "      <td>0.933809</td>\n",
       "      <td>0.087464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4307.160000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4999.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4999.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5339.990000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7553.000000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>1.530000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Brand         Model Processor   RAM(GB)    Price(TL)  \\\n",
       "count        5             5         5  5.000000     5.000000   \n",
       "unique       3             5         4       NaN          NaN   \n",
       "top     Huawei  Matebook D14    Ryzen5       NaN          NaN   \n",
       "freq         3             1         2       NaN          NaN   \n",
       "mean       NaN           NaN       NaN  7.200000  5439.630000   \n",
       "std        NaN           NaN       NaN  1.788854  1239.663138   \n",
       "min        NaN           NaN       NaN  4.000000  4307.160000   \n",
       "25%        NaN           NaN       NaN  8.000000  4999.000000   \n",
       "50%        NaN           NaN       NaN  8.000000  4999.000000   \n",
       "75%        NaN           NaN       NaN  8.000000  5339.990000   \n",
       "max        NaN           NaN       NaN  8.000000  7553.000000   \n",
       "\n",
       "        ScreenSize(inch)  Weight(kg)  \n",
       "count           5.000000    5.000000  \n",
       "unique               NaN         NaN  \n",
       "top                  NaN         NaN  \n",
       "freq                 NaN         NaN  \n",
       "mean           14.120000    1.420000  \n",
       "std             0.933809    0.087464  \n",
       "min            13.000000    1.300000  \n",
       "25%            14.000000    1.380000  \n",
       "50%            14.000000    1.420000  \n",
       "75%            14.000000    1.470000  \n",
       "max            15.600000    1.530000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boxplot for Price(TL) Feature')"
      ]
     },
     "execution_count": 49,
     "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": [
    "df2 = pd.DataFrame(data, columns=['RAM(GB)', 'Price(TL)', 'ScreenSize(inch)', 'Weight(kg)'])\n",
    "green_diamond = dict(markerfacecolor='g', marker='D')\n",
    "boxplot = df2.boxplot(column=['Price(TL)'], flierprops=green_diamond)\n",
    "boxplot.set_title('Boxplot for Price(TL) Feature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4999.0, 4999.0, 5339.99, 340.9899999999998]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Quantiles------------------\n",
    "Q =[]\n",
    "\n",
    "Q1 = df1[\"Price(TL)\"].quantile(0.25)\n",
    "Q2=df1[\"Price(TL)\"].quantile(0.50)\n",
    "Q3 = df1[\"Price(TL)\"].quantile(0.75)\n",
    "\n",
    "IQR = Q3 - Q1\n",
    "Q.append(Q1)\n",
    "Q.append(Q2)\n",
    "Q.append(Q3)\n",
    "Q.append(IQR)\n",
    "Q"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}