2022.11.02.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'c1' : ['a', 'a', 'b', 'a', 'b'],\n",
    "                    'c2': [1, 1, 1, 2, 2],\n",
    "                    'c3' : [1, 1, 2, 2, 2,]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  c1  c2  c3\n",
      "0  a   1   1\n",
      "1  a   1   1\n",
      "2  b   1   2\n",
      "3  a   2   2\n",
      "4  b   2   2\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    False\n",
      "1     True\n",
      "2    False\n",
      "3    False\n",
      "4    False\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "df_dup = df.duplicated()\n",
    "print(df_dup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "col_dup = df['c2'].duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    False\n",
      "1     True\n",
      "2     True\n",
      "3    False\n",
      "4     True\n",
      "Name: c2, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "print(col_dup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  c1  c2  c3\n",
      "0  a   1   1\n",
      "2  b   1   2\n",
      "3  a   2   2\n",
      "4  b   2   2\n"
     ]
    }
   ],
   "source": [
    "df2 = df.drop_duplicates()\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  c1  c2  c3\n",
      "0  a   1   1\n",
      "1  a   1   1\n",
      "2  b   1   2\n",
      "3  a   2   2\n",
      "4  b   2   2\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  c1  c2  c3\n",
      "0  a   1   1\n",
      "2  b   1   2\n",
      "3  a   2   2\n"
     ]
    }
   ],
   "source": [
    "df3 = df.drop_duplicates(subset=['c2', 'c3'])\n",
    "print(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "df = sns.load_dataset('mpg')\n",
    "df.columns = ['mpg', 'cylinders', 'displacement', 'HP', 'weight', 'acceleration', 'model_year', 'origin', 'name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    mpg  cylinders  displacement     HP  weight  acceleration  model_year  \\\n",
      "0  18.0          8         307.0  130.0    3504          12.0          70   \n",
      "1  15.0          8         350.0  165.0    3693          11.5          70   \n",
      "2  18.0          8         318.0  150.0    3436          11.0          70   \n",
      "\n",
      "  origin                       name  \n",
      "0    usa  chevrolet chevelle malibu  \n",
      "1    usa          buick skylark 320  \n",
      "2    usa         plymouth satellite  \n"
     ]
    }
   ],
   "source": [
    "print(df.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "mpg_to_kpl = 1.60934 / 3.78541"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    mpg  cylinders  displacement     HP  weight  acceleration  model_year  \\\n",
      "0  18.0          8         307.0  130.0    3504          12.0          70   \n",
      "1  15.0          8         350.0  165.0    3693          11.5          70   \n",
      "2  18.0          8         318.0  150.0    3436          11.0          70   \n",
      "\n",
      "  origin                       name       kpl  \n",
      "0    usa  chevrolet chevelle malibu  7.652571  \n",
      "1    usa          buick skylark 320  6.377143  \n",
      "2    usa         plymouth satellite  7.652571  \n"
     ]
    }
   ],
   "source": [
    "df['kpl'] = df.mpg*mpg_to_kpl\n",
    "print(df.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130. 165. 150. 140. 198. 220. 215. 225. 190. 170. 160.  95.  97.  85.\n",
      "  88.  46.  87.  90. 113. 200. 210. 193.  nan 100. 105. 175. 153. 180.\n",
      " 110.  72.  86.  70.  76.  65.  69.  60.  80.  54. 208. 155. 112.  92.\n",
      " 145. 137. 158. 167.  94. 107. 230.  49.  75.  91. 122.  67.  83.  78.\n",
      "  52.  61.  93. 148. 129.  96.  71.  98. 115.  53.  81.  79. 120. 152.\n",
      " 102. 108.  68.  58. 149.  89.  63.  48.  66. 139. 103. 125. 133. 138.\n",
      " 135. 142.  77.  62. 132.  84.  64.  74. 116.  82.]\n"
     ]
    }
   ],
   "source": [
    "print(df['HP'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
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