Pandas_InClass_3 (DataFrames Methods & Indexing & Groupby) 18.03.24.ipynb

{
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='green'> <b>Importing Libraries </b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-04T19:06:15.654368Z",
     "start_time": "2023-04-04T19:06:10.963607Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='green'> <b>DataFrames </b><font color='black'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>İsim</th>\n",
       "      <th>boy cm</th>\n",
       "      <th>kilo-kg</th>\n",
       "      <th>BMI</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Ali</td>\n",
       "      <td>170</td>\n",
       "      <td>70</td>\n",
       "      <td>24.221453</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>Ayşe</td>\n",
       "      <td>160</td>\n",
       "      <td>60</td>\n",
       "      <td>23.437500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>170</td>\n",
       "      <td>60</td>\n",
       "      <td>20.761246</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>Veli</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>24.691358</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    İsim  boy cm  kilo-kg        BMI  new\n",
       "A    Ali     170       70  24.221453    0\n",
       "B   Ayşe     160       60  23.437500    1\n",
       "C  Fatma     170       60  20.761246    2\n",
       "D   Veli     180       80  24.691358    3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\"İsim\": [\"Ali\", \"Ayşe\", \"Fatma\", \"Veli\"], \"boy cm\": [170,160,170,180], \"kilo-kg\": [70, 60, 60, 80]}\n",
    "df = pd.DataFrame(data, index= [\"A\", \"B\", \"C\", \"D\"])\n",
    "df[\"BMI\"] = df[\"kilo-kg\"] / (df[\"boy cm\"] /100) ** 2\n",
    "df[\"new\"] = np.arange(4)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>Removing Columns & Rows</b><font color='black'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The drop() method is used to remove specified rows or columns from a Pandas DataFrame.\n",
    "\n",
    "If a column is to be removed, the axis='columns' parameter is used, and the specified column is dropped.\n",
    "\n",
    "Similarly, if a row is to be removed, the axis='index' parameter is used, and the specified row is dropped.\n",
    "\n",
    "This method is frequently used to remove unwanted data and perform data manipulation on a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['new'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnew\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# axis varsayılan değer 0 - sıfırdır. ve ssatır silmek içindir. Sütun silmek isteniyorsa axis = 1 yapılmalı\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\frame.py:5568\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   5420\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m(\n\u001b[0;32m   5421\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   5422\u001b[0m     labels: IndexLabel \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5429\u001b[0m     errors: IgnoreRaise \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   5430\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   5431\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   5432\u001b[0m \u001b[38;5;124;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[0;32m   5433\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5566\u001b[0m \u001b[38;5;124;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[0;32m   5567\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 5568\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   5569\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5570\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5571\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5572\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5573\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5574\u001b[0m \u001b[43m        \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5575\u001b[0m \u001b[43m        \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   5576\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\generic.py:4785\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4783\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m   4784\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 4785\u001b[0m         obj \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_drop_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4787\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[0;32m   4788\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\generic.py:4827\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[1;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[0;32m   4825\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4826\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4827\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4828\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[0;32m   4830\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[0;32m   4831\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\indexes\\base.py:7070\u001b[0m, in \u001b[0;36mIndex.drop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   7068\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[0;32m   7069\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m-> 7070\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlabels[mask]\u001b[38;5;241m.\u001b[39mtolist()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   7071\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[0;32m   7072\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['new'] not found in axis\""
     ]
    }
   ],
   "source": [
    "df.drop(\"new\") #The default value for the axis parameter is 0, which is used to drop rows. To drop a column, axis should be set to 1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>İsim</th>\n",
       "      <th>boy cm</th>\n",
       "      <th>kilo-kg</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Ali</td>\n",
       "      <td>170</td>\n",
       "      <td>70</td>\n",
       "      <td>24.221453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>Ayşe</td>\n",
       "      <td>160</td>\n",
       "      <td>60</td>\n",
       "      <td>23.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>170</td>\n",
       "      <td>60</td>\n",
       "      <td>20.761246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>Veli</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>24.691358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    İsim  boy cm  kilo-kg        BMI\n",
       "A    Ali     170       70  24.221453\n",
       "B   Ayşe     160       60  23.437500\n",
       "C  Fatma     170       60  20.761246\n",
       "D   Veli     180       80  24.691358"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(\"new\", axis = 1)  #The drop() method does not make permanent changes unless it is assigned back to the DataFrame or the inplace=True parameter is used."
   ]
  },
  {
   "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",
       "        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>İsim</th>\n",
       "      <th>boy cm</th>\n",
       "      <th>kilo-kg</th>\n",
       "      <th>BMI</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Ali</td>\n",
       "      <td>170</td>\n",
       "      <td>70</td>\n",
       "      <td>24.221453</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>Ayşe</td>\n",
       "      <td>160</td>\n",
       "      <td>60</td>\n",
       "      <td>23.437500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>170</td>\n",
       "      <td>60</td>\n",
       "      <td>20.761246</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>Veli</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>24.691358</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    İsim  boy cm  kilo-kg        BMI  new\n",
       "A    Ali     170       70  24.221453    0\n",
       "B   Ayşe     160       60  23.437500    1\n",
       "C  Fatma     170       60  20.761246    2\n",
       "D   Veli     180       80  24.691358    3"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"kilo-kg\", axis=1, inplace=True)  #The inplace parameter, when set to True, makes permanent changes by modifying the DataFrame directly. For example, it permanently drops the 'kilo' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
       "        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>İsim</th>\n",
       "      <th>boy cm</th>\n",
       "      <th>BMI</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Ali</td>\n",
       "      <td>170</td>\n",
       "      <td>24.221453</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>Ayşe</td>\n",
       "      <td>160</td>\n",
       "      <td>23.437500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>170</td>\n",
       "      <td>20.761246</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>Veli</td>\n",
       "      <td>180</td>\n",
       "      <td>24.691358</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    İsim  boy cm        BMI  new\n",
       "A    Ali     170  24.221453    0\n",
       "B   Ayşe     160  23.437500    1\n",
       "C  Fatma     170  20.761246    2\n",
       "D   Veli     180  24.691358    3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>İsim</th>\n",
       "      <th>boy cm</th>\n",
       "      <th>BMI</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Ali</td>\n",
       "      <td>170</td>\n",
       "      <td>24.221453</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>Ayşe</td>\n",
       "      <td>160</td>\n",
       "      <td>23.437500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>Veli</td>\n",
       "      <td>180</td>\n",
       "      <td>24.691358</td>\n",
       "      <td>3</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   İsim  boy cm        BMI  new\n",
       "A   Ali     170  24.221453    0\n",
       "B  Ayşe     160  23.437500    1\n",
       "D  Veli     180  24.691358    3"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(\"C\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>İsim</th>\n",
       "      <th>boy cm</th>\n",
       "      <th>BMI</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Ali</td>\n",
       "      <td>170</td>\n",
       "      <td>24.221453</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>Ayşe</td>\n",
       "      <td>160</td>\n",
       "      <td>23.437500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>Fatma</td>\n",
       "      <td>170</td>\n",
       "      <td>20.761246</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>Veli</td>\n",
       "      <td>180</td>\n",
       "      <td>24.691358</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    İsim  boy cm        BMI  new\n",
       "A    Ali     170  24.221453    0\n",
       "B   Ayşe     160  23.437500    1\n",
       "C  Fatma     170  20.761246    2\n",
       "D   Veli     180  24.691358    3"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"C\", \"B\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>İsim</th>\n",
       "      <th>boy cm</th>\n",
       "      <th>BMI</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Ali</td>\n",
       "      <td>170</td>\n",
       "      <td>24.221453</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>Veli</td>\n",
       "      <td>180</td>\n",
       "      <td>24.691358</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   İsim  boy cm        BMI  new\n",
       "A   Ali     170  24.221453    0\n",
       "D  Veli     180  24.691358    3"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>İsim</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Ali</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>Veli</td>\n",
       "      <td>3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   İsim  new\n",
       "A   Ali    0\n",
       "D  Veli    3"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([\"boy cm\", \"BMI\"], axis=1, inplace=True) \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>İsim</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Ali</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>Veli</td>\n",
       "      <td>3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   İsim  new\n",
       "A   Ali    0\n",
       "D  Veli    3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>Selecting Rows and Columns using .loc[ ] and iloc[ ]</b><font color='black'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loc: label-based\n",
    "\n",
    "iloc: integer position-based\n",
    "\n",
    "loc is short for \"location\" and allows access to data by row and column labels.\n",
    "\n",
    "loc is used to access data at a specific location by using row and column labels.\n",
    "\n",
    "iloc is short for \"integer location\" and allows access to data by row and column numbers.\n",
    "\n",
    "iloc is used to access data at a specific location by using row and column numbers.\n",
    "\n",
    "Label or Index Independence: Traditional indexing methods are affected by changes in indices, whereas the loc and iloc methods select based on positions. Therefore, changes in the dataset's indices do not affect selections made using the loc and iloc methods.\n",
    "\n",
    "Fast Performance: The iloc function selects data using only row and column indices, which makes it faster than the loc function. This feature is beneficial in large datasets or performance-critical applications.\n",
    "\n",
    "Flexibility: Traditional indexing methods allow indexing using only sequential integers starting from zero. The loc and iloc methods, however, are based on labels or index values, offering more flexible selection options. The loc and iloc functions allow for combined usage of row and column labels. For example, you can use both row and column labels or indices to select data in a specific column and row. This flexibility is useful in data processing operations.\n",
    "\n",
    "More Readable Code: The loc and iloc functions help make the code more readable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[76, 31,  4, 33],\n",
       "       [96, 62, 86, 36],\n",
       "       [69, 16, 66, 15],\n",
       "       [54, 58, 73, 88],\n",
       "       [47,  9, 54, 13]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>101</th>\n",
       "      <td>76</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>96</td>\n",
       "      <td>62</td>\n",
       "      <td>86</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>69</td>\n",
       "      <td>16</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>54</td>\n",
       "      <td>58</td>\n",
       "      <td>73</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>47</td>\n",
       "      <td>9</td>\n",
       "      <td>54</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     col1  col2  col3  col4\n",
       "101    76    31     4    33\n",
       "102    96    62    86    36\n",
       "103    69    16    66    15\n",
       "104    54    58    73    88\n",
       "105    47     9    54    13"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(45)\n",
    "\n",
    "data = np.random.randint(1, 100, 20).reshape(5, 4)\n",
    "\n",
    "df = pd.DataFrame(data, columns=[\"col1\", \"col2\", \"col3\", \"col4\"], index= range(101, 106))\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "col1    69\n",
       "col2    16\n",
       "col3    66\n",
       "col4    15\n",
       "Name: 103, dtype: int32"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[103]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "      <th>col4</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>102</th>\n",
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       "      <td>86</td>\n",
       "      <td>36</td>\n",
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       "    <tr>\n",
       "      <th>103</th>\n",
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      "text/plain": [
       "     col1  col2  col3  col4\n",
       "102    96    62    86    36\n",
       "103    69    16    66    15\n",
       "104    54    58    73    88"
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     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.loc[102:104]  #last value is included"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>101</th>\n",
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       "      <td>15</td>\n",
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       "      <td>54</td>\n",
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       "      <td>9</td>\n",
       "      <td>54</td>\n",
       "      <td>13</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     col1  col2  col3  col4\n",
       "101    76    31     4    33\n",
       "102    96    62    86    36\n",
       "103    69    16    66    15\n",
       "104    54    58    73    88\n",
       "105    47     9    54    13"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>102</th>\n",
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       "      <td>36</td>\n",
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       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>69</td>\n",
       "      <td>16</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>54</td>\n",
       "      <td>58</td>\n",
       "      <td>73</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     col1  col2  col3  col4\n",
       "102    96    62    86    36\n",
       "103    69    16    66    15\n",
       "104    54    58    73    88"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:4]  #Integer values are included, hence the last value is not included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "col1    76\n",
       "col2    31\n",
       "col3     4\n",
       "col4    33\n",
       "Name: 101, dtype: int32"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[101]  #It is accessed using the label name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "single positional indexer is out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m101\u001b[39;49m\u001b[43m]\u001b[49m  \u001b[38;5;66;03m#index numarası ile çağırılır\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\indexing.py:1191\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1189\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[0;32m   1190\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_deprecated_callable_usage(key, maybe_callable)\n\u001b[1;32m-> 1191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\indexing.py:1752\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1749\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot index by location index with a non-integer key\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1751\u001b[0m \u001b[38;5;66;03m# validate the location\u001b[39;00m\n\u001b[1;32m-> 1752\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_integer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1754\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_ixs(key, axis\u001b[38;5;241m=\u001b[39maxis)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\pandas\\core\\indexing.py:1685\u001b[0m, in \u001b[0;36m_iLocIndexer._validate_integer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1683\u001b[0m len_axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis))\n\u001b[0;32m   1684\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m len_axis \u001b[38;5;129;01mor\u001b[39;00m key \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m-\u001b[39mlen_axis:\n\u001b[1;32m-> 1685\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msingle positional indexer is out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
     ]
    }
   ],
   "source": [
    "df.iloc[101]  #It is not accessed using the index number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
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       "      <th>102</th>\n",
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       "      <td>86</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
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       "      <th>104</th>\n",
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       "      <td>73</td>\n",
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       "      <th>105</th>\n",
       "      <td>47</td>\n",
       "      <td>9</td>\n",
       "      <td>54</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "     col1  col2  col3  col4\n",
       "101    76    31     4    33\n",
       "102    96    62    86    36\n",
       "103    69    16    66    15\n",
       "104    54    58    73    88\n",
       "105    47     9    54    13"
      ]
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     "execution_count": 52,
     "metadata": {},
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    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   col1  col2  col3  col4\n",
       "a    76    31     4    33\n",
       "b    96    62    86    36\n",
       "c    69    16    66    15\n",
       "d    54    58    73    88\n",
       "e    47     9    54    13"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = \"a b c d e\".split()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "   col1  col2  col3  col4\n",
       "b    96    62    86    36\n",
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   "source": [
    "df.iloc[1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   col1  col2  col3  col4\n",
       "b    96    62    86    36\n",
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       "d    54    58    73    88"
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     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.loc[\"b\": \"d\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     4\n",
       "b    86\n",
       "c    66\n",
       "Name: col3, dtype: int32"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"a\": \"c\", \"col3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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      "text/plain": [
       "   col1  col2  col3  col4\n",
       "a    76    31     4    33\n",
       "b    96    62    86    36\n",
       "c    69    16    66    15"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.loc[\"a\": \"c\"]  #The left side of the comma refers to rows, while the right side refers to columns. If only a column name is written, the result is returned as a Pandas Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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      "text/plain": [
       "   col3\n",
       "a     4\n",
       "b    86\n",
       "c    66"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"a\": \"c\", [\"col3\"]]  #If the column name is written inside square brackets ([]), the result is returned as a Pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col3\n",
       "a     4\n",
       "b    86\n",
       "c    66"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:3, [2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "      <th>col4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>76</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>96</td>\n",
       "      <td>62</td>\n",
       "      <td>86</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>69</td>\n",
       "      <td>16</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>54</td>\n",
       "      <td>58</td>\n",
       "      <td>73</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>47</td>\n",
       "      <td>9</td>\n",
       "      <td>54</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2  col3  col4\n",
       "a    76    31     4    33\n",
       "b    96    62    86    36\n",
       "c    69    16    66    15\n",
       "d    54    58    73    88\n",
       "e    47     9    54    13"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>58</td>\n",
       "      <td>73</td>\n",
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       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>9</td>\n",
       "      <td>54</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col2  col3\n",
       "d    58    73\n",
       "e     9    54"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"d\": \"e\", \"col2\":\"col3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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",
       "        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>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>58</td>\n",
       "      <td>73</td>\n",
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       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>9</td>\n",
       "      <td>54</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col2  col3\n",
       "d    58    73\n",
       "e     9    54"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3:,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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",
       "    .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>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>58</td>\n",
       "      <td>73</td>\n",
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       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>9</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col2  col3\n",
       "d    58    73\n",
       "e     9    54"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[[\"d\",\"e\"]][[\"col2\",\"col3\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anagrams',\n",
       " 'anscombe',\n",
       " 'attention',\n",
       " 'brain_networks',\n",
       " 'car_crashes',\n",
       " 'diamonds',\n",
       " 'dots',\n",
       " 'dowjones',\n",
       " 'exercise',\n",
       " 'flights',\n",
       " 'fmri',\n",
       " 'geyser',\n",
       " 'glue',\n",
       " 'healthexp',\n",
       " 'iris',\n",
       " 'mpg',\n",
       " 'penguins',\n",
       " 'planets',\n",
       " 'seaice',\n",
       " 'taxis',\n",
       " 'tips',\n",
       " 'titanic',\n",
       " 'anagrams',\n",
       " 'anagrams',\n",
       " 'anscombe',\n",
       " 'anscombe',\n",
       " 'attention',\n",
       " 'attention',\n",
       " 'brain_networks',\n",
       " 'brain_networks',\n",
       " 'car_crashes',\n",
       " 'car_crashes',\n",
       " 'diamonds',\n",
       " 'diamonds',\n",
       " 'dots',\n",
       " 'dots',\n",
       " 'dowjones',\n",
       " 'dowjones',\n",
       " 'exercise',\n",
       " 'exercise',\n",
       " 'flights',\n",
       " 'flights',\n",
       " 'fmri',\n",
       " 'fmri',\n",
       " 'geyser',\n",
       " 'geyser',\n",
       " 'glue',\n",
       " 'glue',\n",
       " 'healthexp',\n",
       " 'healthexp',\n",
       " 'iris',\n",
       " 'iris',\n",
       " 'mpg',\n",
       " 'mpg',\n",
       " 'penguins',\n",
       " 'penguins',\n",
       " 'planets',\n",
       " 'planets',\n",
       " 'seaice',\n",
       " 'seaice',\n",
       " 'taxis',\n",
       " 'taxis',\n",
       " 'tips',\n",
       " 'tips',\n",
       " 'titanic',\n",
       " 'titanic',\n",
       " 'anagrams',\n",
       " 'anscombe',\n",
       " 'attention',\n",
       " 'brain_networks',\n",
       " 'car_crashes',\n",
       " 'diamonds',\n",
       " 'dots',\n",
       " 'dowjones',\n",
       " 'exercise',\n",
       " 'flights',\n",
       " 'fmri',\n",
       " 'geyser',\n",
       " 'glue',\n",
       " 'healthexp',\n",
       " 'iris',\n",
       " 'mpg',\n",
       " 'penguins',\n",
       " 'planets',\n",
       " 'seaice',\n",
       " 'taxis',\n",
       " 'tips',\n",
       " 'titanic']"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.get_dataset_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sns.get_dataset_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>7.2500</td>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "0      man        True  NaN  Southampton    no  False  \n",
       "1    woman       False    C    Cherbourg   yes  False  \n",
       "2    woman       False  NaN  Southampton   yes   True  \n",
       "3    woman       False    C  Southampton   yes  False  \n",
       "4      man        True  NaN  Southampton    no   True  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
       "890    man        True  NaN   Queenstown    no   True  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset(\"titanic\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
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       "      <td>yes</td>\n",
       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   survived     891 non-null    int64   \n",
      " 1   pclass       891 non-null    int64   \n",
      " 2   sex          891 non-null    object  \n",
      " 3   age          714 non-null    float64 \n",
      " 4   sibsp        891 non-null    int64   \n",
      " 5   parch        891 non-null    int64   \n",
      " 6   fare         891 non-null    float64 \n",
      " 7   embarked     889 non-null    object  \n",
      " 8   class        891 non-null    category\n",
      " 9   who          891 non-null    object  \n",
      " 10  adult_male   891 non-null    bool    \n",
      " 11  deck         203 non-null    category\n",
      " 12  embark_town  889 non-null    object  \n",
      " 13  alive        891 non-null    object  \n",
      " 14  alone        891 non-null    bool    \n",
      "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
      "memory usage: 80.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>pclass</th>\n",
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       "      <th>age</th>\n",
       "      <td>714.0</td>\n",
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       "      <td>14.526497</td>\n",
       "      <td>0.42</td>\n",
       "      <td>20.1250</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>38.0</td>\n",
       "      <td>80.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sibsp</th>\n",
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       "      <td>1.102743</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parch</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare</th>\n",
       "      <td>891.0</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.9104</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>31.0</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count       mean        std   min      25%      50%   75%       max\n",
       "survived  891.0   0.383838   0.486592  0.00   0.0000   0.0000   1.0    1.0000\n",
       "pclass    891.0   2.308642   0.836071  1.00   2.0000   3.0000   3.0    3.0000\n",
       "age       714.0  29.699118  14.526497  0.42  20.1250  28.0000  38.0   80.0000\n",
       "sibsp     891.0   0.523008   1.102743  0.00   0.0000   0.0000   1.0    8.0000\n",
       "parch     891.0   0.381594   0.806057  0.00   0.0000   0.0000   0.0    6.0000\n",
       "fare      891.0  32.204208  49.693429  0.00   7.9104  14.4542  31.0  512.3292"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "            count unique          top freq\n",
       "sex           891      2         male  577\n",
       "embarked      889      3            S  644\n",
       "who           891      3          man  537\n",
       "embark_town   889      3  Southampton  644\n",
       "alive         891      2           no  549"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=\"object\").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
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       "      <th>age</th>\n",
       "      <td>714.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>20.125</td>\n",
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       "      <th>sibsp</th>\n",
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       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>parch</th>\n",
       "      <td>891.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
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       "      <th>fare</th>\n",
       "      <td>891.0</td>\n",
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       "    <tr>\n",
       "      <th>embarked</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <td>891</td>\n",
       "      <td>3</td>\n",
       "      <td>Third</td>\n",
       "      <td>491</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>who</th>\n",
       "      <td>891</td>\n",
       "      <td>3</td>\n",
       "      <td>man</td>\n",
       "      <td>537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adult_male</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deck</th>\n",
       "      <td>203</td>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>embark_town</th>\n",
       "      <td>889</td>\n",
       "      <td>3</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>644</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alive</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>549</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alone</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count unique          top freq       mean        std   min  \\\n",
       "survived     891.0    NaN          NaN  NaN   0.383838   0.486592   0.0   \n",
       "pclass       891.0    NaN          NaN  NaN   2.308642   0.836071   1.0   \n",
       "sex            891      2         male  577        NaN        NaN   NaN   \n",
       "age          714.0    NaN          NaN  NaN  29.699118  14.526497  0.42   \n",
       "sibsp        891.0    NaN          NaN  NaN   0.523008   1.102743   0.0   \n",
       "parch        891.0    NaN          NaN  NaN   0.381594   0.806057   0.0   \n",
       "fare         891.0    NaN          NaN  NaN  32.204208  49.693429   0.0   \n",
       "embarked       889      3            S  644        NaN        NaN   NaN   \n",
       "class          891      3        Third  491        NaN        NaN   NaN   \n",
       "who            891      3          man  537        NaN        NaN   NaN   \n",
       "adult_male     891      2         True  537        NaN        NaN   NaN   \n",
       "deck           203      7            C   59        NaN        NaN   NaN   \n",
       "embark_town    889      3  Southampton  644        NaN        NaN   NaN   \n",
       "alive          891      2           no  549        NaN        NaN   NaN   \n",
       "alone          891      2         True  537        NaN        NaN   NaN   \n",
       "\n",
       "                25%      50%   75%       max  \n",
       "survived        0.0      0.0   1.0       1.0  \n",
       "pclass          2.0      3.0   3.0       3.0  \n",
       "sex             NaN      NaN   NaN       NaN  \n",
       "age          20.125     28.0  38.0      80.0  \n",
       "sibsp           0.0      0.0   1.0       8.0  \n",
       "parch           0.0      0.0   0.0       6.0  \n",
       "fare         7.9104  14.4542  31.0  512.3292  \n",
       "embarked        NaN      NaN   NaN       NaN  \n",
       "class           NaN      NaN   NaN       NaN  \n",
       "who             NaN      NaN   NaN       NaN  \n",
       "adult_male      NaN      NaN   NaN       NaN  \n",
       "deck            NaN      NaN   NaN       NaN  \n",
       "embark_town     NaN      NaN   NaN       NaN  \n",
       "alive           NaN      NaN   NaN       NaN  \n",
       "alone           NaN      NaN   NaN       NaN  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=\"all\").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived\n",
       "0    549\n",
       "1    342\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.survived.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived\n",
       "0    0.616162\n",
       "1    0.383838\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.survived.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
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       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>S</td>\n",
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       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['alive'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[110], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df\u001b[38;5;241m.\u001b[39mdrop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124malive\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\duman\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:5258\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   5110\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m(\n\u001b[0;32m   5111\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   5112\u001b[0m     labels: IndexLabel \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5119\u001b[0m     errors: IgnoreRaise \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   5120\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   5121\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   5122\u001b[0m \u001b[38;5;124;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[0;32m   5123\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5256\u001b[0m \u001b[38;5;124;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[0;32m   5257\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 5258\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdrop(\n\u001b[0;32m   5259\u001b[0m         labels\u001b[38;5;241m=\u001b[39mlabels,\n\u001b[0;32m   5260\u001b[0m         axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[0;32m   5261\u001b[0m         index\u001b[38;5;241m=\u001b[39mindex,\n\u001b[0;32m   5262\u001b[0m         columns\u001b[38;5;241m=\u001b[39mcolumns,\n\u001b[0;32m   5263\u001b[0m         level\u001b[38;5;241m=\u001b[39mlevel,\n\u001b[0;32m   5264\u001b[0m         inplace\u001b[38;5;241m=\u001b[39minplace,\n\u001b[0;32m   5265\u001b[0m         errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[0;32m   5266\u001b[0m     )\n",
      "File \u001b[1;32mc:\\Users\\duman\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:4549\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4547\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m   4548\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 4549\u001b[0m         obj \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_drop_axis(labels, axis, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4551\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[0;32m   4552\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n",
      "File \u001b[1;32mc:\\Users\\duman\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:4591\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[1;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[0;32m   4589\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4590\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4591\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4592\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[0;32m   4594\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[0;32m   4595\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\duman\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6699\u001b[0m, in \u001b[0;36mIndex.drop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   6697\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[0;32m   6698\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m-> 6699\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(labels[mask])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   6700\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[0;32m   6701\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['alive'] not found in axis\""
     ]
    }
   ],
   "source": [
    "df.drop(\"alive\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
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       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
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       "      <td>22.0</td>\n",
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       "      <td>7.2500</td>\n",
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       "      <td>Southampton</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
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       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
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       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
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       "      <th>2</th>\n",
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       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>True</td>\n",
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       "      <th>886</th>\n",
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       "      <td>13.0000</td>\n",
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       "      <td>True</td>\n",
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       "      <td>Southampton</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
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       "      <td>Southampton</td>\n",
       "      <td>False</td>\n",
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       "      <th>889</th>\n",
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       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
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       "      <td>True</td>\n",
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       "      <th>890</th>\n",
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       "      <td>0</td>\n",
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       "      <td>Q</td>\n",
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       "      <td>man</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town  alone  \n",
       "0      man        True  NaN  Southampton  False  \n",
       "1    woman       False    C    Cherbourg  False  \n",
       "2    woman       False  NaN  Southampton   True  \n",
       "3    woman       False    C  Southampton  False  \n",
       "4      man        True  NaN  Southampton   True  \n",
       "..     ...         ...  ...          ...    ...  \n",
       "886    man        True  NaN  Southampton   True  \n",
       "887  woman       False    B  Southampton   True  \n",
       "888  woman       False  NaN  Southampton  False  \n",
       "889    man        True    C    Cherbourg   True  \n",
       "890    man        True  NaN   Queenstown   True  \n",
       "\n",
       "[891 rows x 14 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(\"alive\", axis= 1)  #Did not delete because it is not inplace = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"alive\", axis= 1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town  alone  \n",
       "0      man        True  NaN  Southampton  False  \n",
       "1    woman       False    C    Cherbourg  False  \n",
       "2    woman       False  NaN  Southampton   True  \n",
       "3    woman       False    C  Southampton  False  \n",
       "4      man        True  NaN  Southampton   True  \n",
       "..     ...         ...  ...          ...    ...  \n",
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       "887  woman       False    B  Southampton   True  \n",
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       "889    man        True    C    Cherbourg   True  \n",
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       "\n",
       "[891 rows x 14 columns]"
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     "execution_count": 114,
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    "df"
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  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.6542</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>A</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>63.3583</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>D</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0          0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1          1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2          1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3          1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4          0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..       ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "95         0       3    male   NaN      0      0   8.0500        S   Third   \n",
       "96         0       1    male  71.0      0      0  34.6542        C   First   \n",
       "97         1       1    male  23.0      0      1  63.3583        C   First   \n",
       "98         1       2  female  34.0      0      1  23.0000        S  Second   \n",
       "99         0       2    male  34.0      1      0  26.0000        S  Second   \n",
       "\n",
       "      who  adult_male deck  embark_town  alone  \n",
       "0     man        True  NaN  Southampton  False  \n",
       "1   woman       False    C    Cherbourg  False  \n",
       "2   woman       False  NaN  Southampton   True  \n",
       "3   woman       False    C  Southampton  False  \n",
       "4     man        True  NaN  Southampton   True  \n",
       "..    ...         ...  ...          ...    ...  \n",
       "95    man        True  NaN  Southampton   True  \n",
       "96    man        True    A    Cherbourg   True  \n",
       "97    man        True    D    Cherbourg  False  \n",
       "98  woman       False  NaN  Southampton  False  \n",
       "99    man        True  NaN  Southampton  False  \n",
       "\n",
       "[100 rows x 14 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:100]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
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       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
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       "      <th>99</th>\n",
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       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    survived  pclass\n",
       "0          0       3\n",
       "1          1       1\n",
       "2          1       3\n",
       "3          1       1\n",
       "4          0       3\n",
       "..       ...     ...\n",
       "95         0       3\n",
       "96         0       1\n",
       "97         1       1\n",
       "98         1       2\n",
       "99         0       2\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
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     "metadata": {},
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   "source": [
    "df.iloc[:100, :2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='blue'> <b>Conditional Selection</b><font color='black'>"
   ]
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  {
   "cell_type": "code",
   "execution_count": 90,
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       "      <th>d</th>\n",
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      "text/plain": [
       "   col1  col2  col3  col4\n",
       "a    76    31     4    33\n",
       "b    96    62    86    36\n",
       "c    69    16    66    15\n",
       "d    54    58    73    88\n",
       "e    47     9    54    13"
      ]
     },
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   "source": [
    "df"
   ]
  },
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   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    col1   col2   col3   col4\n",
       "a   True  False  False  False\n",
       "b   True   True   True  False\n",
       "c   True  False   True  False\n",
       "d   True   True   True   True\n",
       "e  False  False   True  False"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df > 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
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       "      <td>69.0</td>\n",
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       "      <td>66.0</td>\n",
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       "      <th>d</th>\n",
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       "      <td>88.0</td>\n",
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       "      <th>e</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "   col1  col2  col3  col4\n",
       "a  76.0   NaN   NaN   NaN\n",
       "b  96.0  62.0  86.0   NaN\n",
       "c  69.0   NaN  66.0   NaN\n",
       "d  54.0  58.0  73.0  88.0\n",
       "e   NaN   NaN  54.0   NaN"
      ]
     },
     "execution_count": 94,
     "metadata": {},
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   ],
   "source": [
    "df[df > 50]  #I select the rows that satisfy the condition."
   ]
  }
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