03_03_pandas_selection_built_in_conditional_selectors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./pic/selecting/built_in/1_title.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
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       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
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       "      <td>Q</td>\n",
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       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>7.0000</td>\n",
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       "      <td>S</td>\n",
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       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
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       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
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       "      <td>Q</td>\n",
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       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0          892         0       3   \n",
       "1          893         1       3   \n",
       "2          894         0       2   \n",
       "3          895         0       3   \n",
       "4          896         1       3   \n",
       "\n",
       "                                           Name     Sex   Age  SibSp  Parch  \\\n",
       "0                              Kelly, Mr. James    male  34.5      0      0   \n",
       "1              Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
       "2                     Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
       "3                              Wirz, Mr. Albert    male  27.0      0      0   \n",
       "4  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
       "\n",
       "    Ticket     Fare Cabin Embarked  \n",
       "0   330911   7.8292   NaN        Q  \n",
       "1   363272   7.0000   NaN        S  \n",
       "2   240276   9.6875   NaN        Q  \n",
       "3   315154   8.6625   NaN        S  \n",
       "4  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./csv/titanic.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "`Pandas` verfügt über einige **eingebaute bedingte Selektoren**, von denen ist die wichtigsten in diesem Notebook hervorhebe. Diese Art von Selektoren sollen dir dabei helfen, schnell einen Überblick über dein `DataFrame` zu erhalten und gewisse Einträge mit gewissen Werten zügig in Erfahrung zu bringen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `.copy()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Methode Pandas `.copy()` wird verwendet, um eine **Kopie eines Pandas-Objekts** zu erstellen. Variablen werden auch verwendet, um eine Kopie eines Objekts zu erzeugen, aber Variablen sind nur Zeiger auf ein Objekt und jede Änderung der neuen Daten wird auch die vorherigen Daten ändern. Mit `.copy()` wird ein neues Objekt im Arbeitsspeicher erstellt."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./pic/aditional_functions/copy/2_copy.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ohne `.copy()` sondern mit variabler Zuweisung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.loc[0, 'Name'] = 'New Name'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <th>Name</th>\n",
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       "      <th>0</th>\n",
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       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0          892         0       3   \n",
       "1          893         1       3   \n",
       "2          894         0       2   \n",
       "3          895         0       3   \n",
       "4          896         1       3   \n",
       "\n",
       "                                           Name     Sex   Age  SibSp  Parch  \\\n",
       "0                                      New Name    male  34.5      0      0   \n",
       "1              Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
       "2                     Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
       "3                              Wirz, Mr. Albert    male  27.0      0      0   \n",
       "4  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
       "\n",
       "    Ticket     Fare Cabin Embarked  \n",
       "0   330911   7.8292   NaN        Q  \n",
       "1   363272   7.0000   NaN        S  \n",
       "2   240276   9.6875   NaN        Q  \n",
       "3   315154   8.6625   NaN        S  \n",
       "4  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>894</td>\n",
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       "      <td>male</td>\n",
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       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0          892         0       3   \n",
       "1          893         1       3   \n",
       "2          894         0       2   \n",
       "3          895         0       3   \n",
       "4          896         1       3   \n",
       "\n",
       "                                           Name     Sex   Age  SibSp  Parch  \\\n",
       "0                                      New Name    male  34.5      0      0   \n",
       "1              Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
       "2                     Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
       "3                              Wirz, Mr. Albert    male  27.0      0      0   \n",
       "4  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
       "\n",
       "    Ticket     Fare Cabin Embarked  \n",
       "0   330911   7.8292   NaN        Q  \n",
       "1   363272   7.0000   NaN        S  \n",
       "2   240276   9.6875   NaN        Q  \n",
       "3   315154   8.6625   NaN        S  \n",
       "4  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hierbei sieht man sehr schön, dass Änderungen im `df2` direkte Auswirkung auf das original `df` haben, da `df2` lediglich eine Referenz von `df` ist und kein eigenständiges Objekt."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mit `.copy()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Survived</th>\n",
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       "      <td>893</td>\n",
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       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0          892         0       3   \n",
       "1          893         1       3   \n",
       "2          894         0       2   \n",
       "3          895         0       3   \n",
       "4          896         1       3   \n",
       "\n",
       "                                           Name     Sex   Age  SibSp  Parch  \\\n",
       "0                                      New Name    male  34.5      0      0   \n",
       "1              Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
       "2                     Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
       "3                              Wirz, Mr. Albert    male  27.0      0      0   \n",
       "4  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
       "\n",
       "    Ticket     Fare Cabin Embarked  \n",
       "0   330911   7.8292   NaN        Q  \n",
       "1   363272   7.0000   NaN        S  \n",
       "2   240276   9.6875   NaN        Q  \n",
       "3   315154   8.6625   NaN        S  \n",
       "4  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.loc[1, 'Name'] = 'New Name Again'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0          892         0       3   \n",
       "1          893         1       3   \n",
       "2          894         0       2   \n",
       "3          895         0       3   \n",
       "4          896         1       3   \n",
       "\n",
       "                                           Name     Sex   Age  SibSp  Parch  \\\n",
       "0                                      New Name    male  34.5      0      0   \n",
       "1                                New Name Again  female  47.0      1      0   \n",
       "2                     Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
       "3                              Wirz, Mr. Albert    male  27.0      0      0   \n",
       "4  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
       "\n",
       "    Ticket     Fare Cabin Embarked  \n",
       "0   330911   7.8292   NaN        Q  \n",
       "1   363272   7.0000   NaN        S  \n",
       "2   240276   9.6875   NaN        Q  \n",
       "3   315154   8.6625   NaN        S  \n",
       "4  3101298  12.2875   NaN        S  "
      ]
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0          892         0       3   \n",
       "1          893         1       3   \n",
       "2          894         0       2   \n",
       "3          895         0       3   \n",
       "4          896         1       3   \n",
       "\n",
       "                                           Name     Sex   Age  SibSp  Parch  \\\n",
       "0                                      New Name    male  34.5      0      0   \n",
       "1              Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
       "2                     Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
       "3                              Wirz, Mr. Albert    male  27.0      0      0   \n",
       "4  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
       "\n",
       "    Ticket     Fare Cabin Embarked  \n",
       "0   330911   7.8292   NaN        Q  \n",
       "1   363272   7.0000   NaN        S  \n",
       "2   240276   9.6875   NaN        Q  \n",
       "3   315154   8.6625   NaN        S  \n",
       "4  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Erneut wird die Änderung vom `df2` sichtbar, die Änderung die jedoch vom `df3` vorgenommen wurde, wurde auch nur in diesem vorgenommen und ist nicht auf das original `df` übertragen worden."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `.isin()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Der erste Function ist `.isin()`. Mit `.isin()` kannst du Daten auswählen, deren Wert **\"in\"** einer Liste von Werten ist."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./pic/selecting/built_in/2_isin.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zum Beispiel können wir damit nur Passagiere des Geschlechtes female auswählen:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7.6292</td>\n",
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       "      <td>Abrahim, Mrs. Joseph (Sophie Halaut Easu)</td>\n",
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       "      <td>18.0</td>\n",
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       "      <td>Snyder, Mrs. John Pillsbury (Nelle Stevenson)</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21228</td>\n",
       "      <td>82.2667</td>\n",
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       "      <td>3</td>\n",
       "      <td>Peacock, Miss. Treasteall</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19928</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>C78</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>1304</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Henriksson, Miss. Jenny Lovisa</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347086</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>152 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1            893         1       3   \n",
       "4            896         1       3   \n",
       "6            898         1       3   \n",
       "8            900         1       3   \n",
       "12           904         1       1   \n",
       "..           ...       ...     ...   \n",
       "409         1301         1       3   \n",
       "410         1302         1       3   \n",
       "411         1303         1       1   \n",
       "412         1304         1       3   \n",
       "414         1306         1       1   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "1                   Wilkes, Mrs. James (Ellen Needs)  female  47.0      1   \n",
       "4       Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1   \n",
       "6                               Connolly, Miss. Kate  female  30.0      0   \n",
       "8          Abrahim, Mrs. Joseph (Sophie Halaut Easu)  female  18.0      0   \n",
       "12     Snyder, Mrs. John Pillsbury (Nelle Stevenson)  female  23.0      1   \n",
       "..                                               ...     ...   ...    ...   \n",
       "409                        Peacock, Miss. Treasteall  female   3.0      1   \n",
       "410                           Naughton, Miss. Hannah  female   NaN      0   \n",
       "411  Minahan, Mrs. William Edward (Lillian E Thorpe)  female  37.0      1   \n",
       "412                   Henriksson, Miss. Jenny Lovisa  female  28.0      0   \n",
       "414                     Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
       "\n",
       "     Parch              Ticket      Fare Cabin Embarked  \n",
       "1        0              363272    7.0000   NaN        S  \n",
       "4        1             3101298   12.2875   NaN        S  \n",
       "6        0              330972    7.6292   NaN        Q  \n",
       "8        0                2657    7.2292   NaN        C  \n",
       "12       0               21228   82.2667   B45        S  \n",
       "..     ...                 ...       ...   ...      ...  \n",
       "409      1  SOTON/O.Q. 3101315   13.7750   NaN        S  \n",
       "410      0              365237    7.7500   NaN        Q  \n",
       "411      0               19928   90.0000   C78        Q  \n",
       "412      0              347086    7.7750   NaN        S  \n",
       "414      0            PC 17758  108.9000  C105        C  \n",
       "\n",
       "[152 rows x 12 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Sex.isin(['female'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...oder alle Passagiere die sich in der Ersten Klasse befinden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
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       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>903</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Jones, Mr. Charles Cresson</td>\n",
       "      <td>male</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>694</td>\n",
       "      <td>26.0000</td>\n",
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       "      <th>12</th>\n",
       "      <td>904</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Snyder, Mrs. John Pillsbury (Nelle Stevenson)</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21228</td>\n",
       "      <td>82.2667</td>\n",
       "      <td>B45</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>906</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chaffee, Mrs. Herbert Fuller (Carrie Constance...</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>W.E.P. 5734</td>\n",
       "      <td>61.1750</td>\n",
       "      <td>E31</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>912</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Rothschild, Mr. Martin</td>\n",
       "      <td>male</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17603</td>\n",
       "      <td>59.4000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>914</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Flegenheim, Mrs. Alfred (Antoinette)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17598</td>\n",
       "      <td>31.6833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>1295</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Carrau, Mr. Jose Pedro</td>\n",
       "      <td>male</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113059</td>\n",
       "      <td>47.1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>1296</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Frauenthal, Mr. Isaac Gerald</td>\n",
       "      <td>male</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17765</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>D40</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>1299</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Widener, Mr. George Dunton</td>\n",
       "      <td>male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>113503</td>\n",
       "      <td>211.5000</td>\n",
       "      <td>C80</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>1303</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Minahan, Mrs. William Edward (Lillian E Thorpe)</td>\n",
       "      <td>female</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19928</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>C78</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "11           903         0       1   \n",
       "12           904         1       1   \n",
       "14           906         1       1   \n",
       "20           912         0       1   \n",
       "22           914         1       1   \n",
       "..           ...       ...     ...   \n",
       "403         1295         0       1   \n",
       "404         1296         0       1   \n",
       "407         1299         0       1   \n",
       "411         1303         1       1   \n",
       "414         1306         1       1   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "11                          Jones, Mr. Charles Cresson    male  46.0      0   \n",
       "12       Snyder, Mrs. John Pillsbury (Nelle Stevenson)  female  23.0      1   \n",
       "14   Chaffee, Mrs. Herbert Fuller (Carrie Constance...  female  47.0      1   \n",
       "20                              Rothschild, Mr. Martin    male  55.0      1   \n",
       "22                Flegenheim, Mrs. Alfred (Antoinette)  female   NaN      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "403                             Carrau, Mr. Jose Pedro    male  17.0      0   \n",
       "404                       Frauenthal, Mr. Isaac Gerald    male  43.0      1   \n",
       "407                         Widener, Mr. George Dunton    male  50.0      1   \n",
       "411    Minahan, Mrs. William Edward (Lillian E Thorpe)  female  37.0      1   \n",
       "414                       Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
       "\n",
       "     Parch       Ticket      Fare Cabin Embarked  \n",
       "11       0          694   26.0000   NaN        S  \n",
       "12       0        21228   82.2667   B45        S  \n",
       "14       0  W.E.P. 5734   61.1750   E31        S  \n",
       "20       0     PC 17603   59.4000   NaN        C  \n",
       "22       0     PC 17598   31.6833   NaN        S  \n",
       "..     ...          ...       ...   ...      ...  \n",
       "403      0       113059   47.1000   NaN        S  \n",
       "404      0        17765   27.7208   D40        C  \n",
       "407      1       113503  211.5000   C80        C  \n",
       "411      0        19928   90.0000   C78        Q  \n",
       "414      0     PC 17758  108.9000  C105        C  \n",
       "\n",
       "[107 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Pclass.isin([1])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jedoch muss bei `.isin()` immer die komplette Condition angegeben werden, es funktioniert hierbei nicht, für weibliche Passagiere lediglich nach `\"f\"` zu suchen..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>PassengerId</th>\n",
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       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
       "Index: []"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Sex.isin(['f'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hierbei bringt die folgende Methode Abhilfe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `.contains()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anders als bei `.isin()` benötigt `.contains()` nicht die vollständige Condition. Wenn wir z.B. nur den Vornamen der Person wissen, nach der wir suchen wollen, ansonsten jedoch nicht mehr, können wir `.contains()` verwenden. `Pandas` sucht dann nach allen Einträgen im `DataFrame`, die an irgend einer Stelle diesen `String` (oder auch einen einzelnen `Char`) beinhaltet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./pic/selecting/built_in/6_contains.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1       True\n",
       "2      False\n",
       "3      False\n",
       "4       True\n",
       "       ...  \n",
       "413    False\n",
       "414     True\n",
       "415    False\n",
       "416    False\n",
       "417    False\n",
       "Name: Sex, Length: 418, dtype: bool"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Sex.str.contains('f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
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       "      <td>S</td>\n",
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       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Connolly, Miss. Kate</td>\n",
       "      <td>female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330972</td>\n",
       "      <td>7.6292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Abrahim, Mrs. Joseph (Sophie Halaut Easu)</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2657</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>904</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Snyder, Mrs. John Pillsbury (Nelle Stevenson)</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21228</td>\n",
       "      <td>82.2667</td>\n",
       "      <td>B45</td>\n",
       "      <td>S</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>1301</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Peacock, Miss. Treasteall</td>\n",
       "      <td>female</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SOTON/O.Q. 3101315</td>\n",
       "      <td>13.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>1302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Naughton, Miss. Hannah</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>365237</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>1303</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Minahan, Mrs. William Edward (Lillian E Thorpe)</td>\n",
       "      <td>female</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19928</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>C78</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>1304</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Henriksson, Miss. Jenny Lovisa</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347086</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>152 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1            893         1       3   \n",
       "4            896         1       3   \n",
       "6            898         1       3   \n",
       "8            900         1       3   \n",
       "12           904         1       1   \n",
       "..           ...       ...     ...   \n",
       "409         1301         1       3   \n",
       "410         1302         1       3   \n",
       "411         1303         1       1   \n",
       "412         1304         1       3   \n",
       "414         1306         1       1   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "1                   Wilkes, Mrs. James (Ellen Needs)  female  47.0      1   \n",
       "4       Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1   \n",
       "6                               Connolly, Miss. Kate  female  30.0      0   \n",
       "8          Abrahim, Mrs. Joseph (Sophie Halaut Easu)  female  18.0      0   \n",
       "12     Snyder, Mrs. John Pillsbury (Nelle Stevenson)  female  23.0      1   \n",
       "..                                               ...     ...   ...    ...   \n",
       "409                        Peacock, Miss. Treasteall  female   3.0      1   \n",
       "410                           Naughton, Miss. Hannah  female   NaN      0   \n",
       "411  Minahan, Mrs. William Edward (Lillian E Thorpe)  female  37.0      1   \n",
       "412                   Henriksson, Miss. Jenny Lovisa  female  28.0      0   \n",
       "414                     Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
       "\n",
       "     Parch              Ticket      Fare Cabin Embarked  \n",
       "1        0              363272    7.0000   NaN        S  \n",
       "4        1             3101298   12.2875   NaN        S  \n",
       "6        0              330972    7.6292   NaN        Q  \n",
       "8        0                2657    7.2292   NaN        C  \n",
       "12       0               21228   82.2667   B45        S  \n",
       "..     ...                 ...       ...   ...      ...  \n",
       "409      1  SOTON/O.Q. 3101315   13.7750   NaN        S  \n",
       "410      0              365237    7.7500   NaN        Q  \n",
       "411      0               19928   90.0000   C78        Q  \n",
       "412      0              347086    7.7750   NaN        S  \n",
       "414      0            PC 17758  108.9000  C105        C  \n",
       "\n",
       "[152 rows x 12 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Sex.str.contains('f')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2       True\n",
       "3      False\n",
       "4       True\n",
       "       ...  \n",
       "413    False\n",
       "414     True\n",
       "415    False\n",
       "416    False\n",
       "417    False\n",
       "Name: Name, Length: 418, dtype: bool"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Name.str.contains('an', case=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <td>Myles, Mr. Thomas Francis</td>\n",
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       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Svensson, Mr. Johan Cervin</td>\n",
       "      <td>male</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7538</td>\n",
       "      <td>9.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Caldwell, Mr. Albert Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>14</th>\n",
       "      <td>906</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chaffee, Mrs. Herbert Fuller (Carrie Constance...</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>W.E.P. 5734</td>\n",
       "      <td>61.1750</td>\n",
       "      <td>E31</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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>397</th>\n",
       "      <td>1289</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...</td>\n",
       "      <td>female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13567</td>\n",
       "      <td>79.2000</td>\n",
       "      <td>B41</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Riordan, Miss. Johanna Hannah\"\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>334915</td>\n",
       "      <td>7.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>1302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Naughton, Miss. Hannah</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>365237</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>1303</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Minahan, Mrs. William Edward (Lillian E Thorpe)</td>\n",
       "      <td>female</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19928</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>C78</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "2            894         0       2   \n",
       "4            896         1       3   \n",
       "5            897         0       3   \n",
       "7            899         0       2   \n",
       "14           906         1       1   \n",
       "..           ...       ...     ...   \n",
       "397         1289         1       1   \n",
       "408         1300         1       3   \n",
       "410         1302         1       3   \n",
       "411         1303         1       1   \n",
       "414         1306         1       1   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "2                            Myles, Mr. Thomas Francis    male  62.0      0   \n",
       "4         Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1   \n",
       "5                           Svensson, Mr. Johan Cervin    male  14.0      0   \n",
       "7                         Caldwell, Mr. Albert Francis    male  26.0      1   \n",
       "14   Chaffee, Mrs. Herbert Fuller (Carrie Constance...  female  47.0      1   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "397  Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...  female  48.0      1   \n",
       "408                    Riordan, Miss. Johanna Hannah\"\"  female   NaN      0   \n",
       "410                             Naughton, Miss. Hannah  female   NaN      0   \n",
       "411    Minahan, Mrs. William Edward (Lillian E Thorpe)  female  37.0      1   \n",
       "414                       Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
       "\n",
       "     Parch       Ticket      Fare Cabin Embarked  \n",
       "2        0       240276    9.6875   NaN        Q  \n",
       "4        1      3101298   12.2875   NaN        S  \n",
       "5        0         7538    9.2250   NaN        S  \n",
       "7        1       248738   29.0000   NaN        S  \n",
       "14       0  W.E.P. 5734   61.1750   E31        S  \n",
       "..     ...          ...       ...   ...      ...  \n",
       "397      1        13567   79.2000   B41        C  \n",
       "408      0       334915    7.7208   NaN        Q  \n",
       "410      0       365237    7.7500   NaN        Q  \n",
       "411      0        19928   90.0000   C78        Q  \n",
       "414      0     PC 17758  108.9000  C105        C  \n",
       "\n",
       "[133 rows x 12 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Name.str.contains('an', case=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Cannot mask with non-boolean array containing NA / NaN values",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-2cdf36a99357>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# !!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCabin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontains\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'B'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcase\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3013\u001b[0m         \u001b[0;31m# Do we have a (boolean) 1d indexer?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3014\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3015\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_bool_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3016\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/common.py\u001b[0m in \u001b[0;36mis_bool_indexer\u001b[0;34m(key)\u001b[0m\n\u001b[1;32m    112\u001b[0m                     \u001b[0;31m# Don't raise on e.g. [\"A\", \"B\", np.nan], see\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m                     \u001b[0;31m#  test_loc_getitem_list_of_labels_categoricalindex_with_na\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mna_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Cannot mask with non-boolean array containing NA / NaN values"
     ]
    }
   ],
   "source": [
    "# !!\n",
    "# df[df.Cabin.str.contains('B', case=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>904</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Snyder, Mrs. John Pillsbury (Nelle Stevenson)</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21228</td>\n",
       "      <td>82.2667</td>\n",
       "      <td>B45</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>916</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ryerson, Mrs. Arthur Larned (Emily Maria Borie)</td>\n",
       "      <td>female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>918</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ostby, Miss. Helene Ragnhild</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113509</td>\n",
       "      <td>61.9792</td>\n",
       "      <td>B36</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>951</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chaudanson, Miss. Victorine</td>\n",
       "      <td>female</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>B61</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>956</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ryerson, Master. John Borie</td>\n",
       "      <td>male</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>984</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Davidson, Mrs. Thornton (Orian Hays)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>F.C. 12750</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>B71</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1034</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ryerson, Mr. Arthur Larned</td>\n",
       "      <td>male</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>1058</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Brandeis, Mr. Emil</td>\n",
       "      <td>male</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17591</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>B10</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>1076</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Douglas, Mrs. Frederick Charles (Mary Helene B...</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17558</td>\n",
       "      <td>247.5208</td>\n",
       "      <td>B58 B60</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>1107</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Head, Mr. Christopher</td>\n",
       "      <td>male</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113038</td>\n",
       "      <td>42.5000</td>\n",
       "      <td>B11</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>1179</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Snyder, Mr. John Pillsbury</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21228</td>\n",
       "      <td>82.2667</td>\n",
       "      <td>B45</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>1197</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Crosby, Mrs. Edward Gifford (Catherine Elizabe...</td>\n",
       "      <td>female</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>112901</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>B26</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Hays, Mr. Charles Melville</td>\n",
       "      <td>male</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12749</td>\n",
       "      <td>93.5000</td>\n",
       "      <td>B69</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>1208</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Spencer, Mr. William Augustus</td>\n",
       "      <td>male</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17569</td>\n",
       "      <td>146.5208</td>\n",
       "      <td>B78</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>1235</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cardeza, Mrs. James Warburton Martinez (Charlo...</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>1264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ismay, Mr. Joseph Bruce</td>\n",
       "      <td>male</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112058</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>B52 B54 B56</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>1282</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Payne, Mr. Vivian Ponsonby</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12749</td>\n",
       "      <td>93.5000</td>\n",
       "      <td>B24</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>1289</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...</td>\n",
       "      <td>female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13567</td>\n",
       "      <td>79.2000</td>\n",
       "      <td>B41</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "12           904         1       1   \n",
       "24           916         1       1   \n",
       "26           918         1       1   \n",
       "59           951         1       1   \n",
       "64           956         0       1   \n",
       "92           984         1       1   \n",
       "142         1034         0       1   \n",
       "166         1058         0       1   \n",
       "184         1076         1       1   \n",
       "215         1107         0       1   \n",
       "287         1179         0       1   \n",
       "305         1197         1       1   \n",
       "308         1200         0       1   \n",
       "316         1208         0       1   \n",
       "343         1235         1       1   \n",
       "372         1264         0       1   \n",
       "390         1282         0       1   \n",
       "397         1289         1       1   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "12       Snyder, Mrs. John Pillsbury (Nelle Stevenson)  female  23.0      1   \n",
       "24     Ryerson, Mrs. Arthur Larned (Emily Maria Borie)  female  48.0      1   \n",
       "26                        Ostby, Miss. Helene Ragnhild  female  22.0      0   \n",
       "59                         Chaudanson, Miss. Victorine  female  36.0      0   \n",
       "64                         Ryerson, Master. John Borie    male  13.0      2   \n",
       "92                Davidson, Mrs. Thornton (Orian Hays)  female  27.0      1   \n",
       "142                         Ryerson, Mr. Arthur Larned    male  61.0      1   \n",
       "166                                 Brandeis, Mr. Emil    male  48.0      0   \n",
       "184  Douglas, Mrs. Frederick Charles (Mary Helene B...  female  27.0      1   \n",
       "215                              Head, Mr. Christopher    male  42.0      0   \n",
       "287                         Snyder, Mr. John Pillsbury    male  24.0      1   \n",
       "305  Crosby, Mrs. Edward Gifford (Catherine Elizabe...  female  64.0      1   \n",
       "308                         Hays, Mr. Charles Melville    male  55.0      1   \n",
       "316                      Spencer, Mr. William Augustus    male  57.0      1   \n",
       "343  Cardeza, Mrs. James Warburton Martinez (Charlo...  female  58.0      0   \n",
       "372                            Ismay, Mr. Joseph Bruce    male  49.0      0   \n",
       "390                         Payne, Mr. Vivian Ponsonby    male  23.0      0   \n",
       "397  Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...  female  48.0      1   \n",
       "\n",
       "     Parch      Ticket      Fare            Cabin Embarked  \n",
       "12       0       21228   82.2667              B45        S  \n",
       "24       3    PC 17608  262.3750  B57 B59 B63 B66        C  \n",
       "26       1      113509   61.9792              B36        C  \n",
       "59       0    PC 17608  262.3750              B61        C  \n",
       "64       2    PC 17608  262.3750  B57 B59 B63 B66        C  \n",
       "92       2  F.C. 12750   52.0000              B71        S  \n",
       "142      3    PC 17608  262.3750  B57 B59 B63 B66        C  \n",
       "166      0    PC 17591   50.4958              B10        C  \n",
       "184      1    PC 17558  247.5208          B58 B60        C  \n",
       "215      0      113038   42.5000              B11        S  \n",
       "287      0       21228   82.2667              B45        S  \n",
       "305      1      112901   26.5500              B26        S  \n",
       "308      1       12749   93.5000              B69        S  \n",
       "316      0    PC 17569  146.5208              B78        C  \n",
       "343      1    PC 17755  512.3292      B51 B53 B55        C  \n",
       "372      0      112058    0.0000      B52 B54 B56        S  \n",
       "390      0       12749   93.5000              B24        S  \n",
       "397      1       13567   79.2000              B41        C  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Cabin.str.contains('B', case=False, na=False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `.notnull()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die zweite ist `.notnull()`. Mit diesen Methoden kannst du Werte hervorheben, nicht leer sind (`NaN`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./pic/selecting/built_in/3_notnull.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`.notnull()` kannst du entweder auf das **komplette** `DataFrame` anwenden und kriegst alle Zeilen, die keinen einzigen `Nan` Value in keiner einzigen Spalte beinhalten..."
   ]
  },
  {
   "cell_type": "code",
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       "     PassengerId  Survived  Pclass  Name   Sex    Age  SibSp  Parch  Ticket  \\\n",
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     "execution_count": 44,
     "metadata": {},
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  {
   "cell_type": "code",
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       "     PassengerId  Survived  Pclass  \\\n",
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       "..           ...       ...     ...   \n",
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       "0                                Kelly, Mr. James    male  34.5      0      0   \n",
       "1                Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
       "2                       Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
       "3                                Wirz, Mr. Albert    male  27.0      0      0   \n",
       "4    Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
       "..                                            ...     ...   ...    ...    ...   \n",
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       "414                  Oliva y Ocana, Dona. Fermina  female  39.0      0      0   \n",
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       "416                           Ware, Mr. Frederick    male   NaN      0      0   \n",
       "417                      Peter, Master. Michael J    male   NaN      1      1   \n",
       "\n",
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       "..                  ...       ...   ...      ...  \n",
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...oder aber auch auf **vereinzelte Spalten**, wobei sich dann in anderen Spalten der Zeile wiederrum `Nan` Values befinden können."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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       "      <th>24</th>\n",
       "      <td>916</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ryerson, Mrs. Arthur Larned (Emily Maria Borie)</td>\n",
       "      <td>female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>918</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ostby, Miss. Helene Ragnhild</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113509</td>\n",
       "      <td>61.9792</td>\n",
       "      <td>B36</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>920</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Brady, Mr. John Bertram</td>\n",
       "      <td>male</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113054</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>A21</td>\n",
       "      <td>S</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>1296</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Frauenthal, Mr. Isaac Gerald</td>\n",
       "      <td>male</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17765</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>D40</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>1297</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Nourney, Mr. Alfred (Baron von Drachstedt\")\"</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2166</td>\n",
       "      <td>13.8625</td>\n",
       "      <td>D38</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>1299</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Widener, Mr. George Dunton</td>\n",
       "      <td>male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>113503</td>\n",
       "      <td>211.5000</td>\n",
       "      <td>C80</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>1303</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Minahan, Mrs. William Edward (Lillian E Thorpe)</td>\n",
       "      <td>female</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19928</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>C78</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "12           904         1       1   \n",
       "14           906         1       1   \n",
       "24           916         1       1   \n",
       "26           918         1       1   \n",
       "28           920         0       1   \n",
       "..           ...       ...     ...   \n",
       "404         1296         0       1   \n",
       "405         1297         0       2   \n",
       "407         1299         0       1   \n",
       "411         1303         1       1   \n",
       "414         1306         1       1   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "12       Snyder, Mrs. John Pillsbury (Nelle Stevenson)  female  23.0      1   \n",
       "14   Chaffee, Mrs. Herbert Fuller (Carrie Constance...  female  47.0      1   \n",
       "24     Ryerson, Mrs. Arthur Larned (Emily Maria Borie)  female  48.0      1   \n",
       "26                        Ostby, Miss. Helene Ragnhild  female  22.0      0   \n",
       "28                             Brady, Mr. John Bertram    male  41.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "404                       Frauenthal, Mr. Isaac Gerald    male  43.0      1   \n",
       "405       Nourney, Mr. Alfred (Baron von Drachstedt\")\"    male  20.0      0   \n",
       "407                         Widener, Mr. George Dunton    male  50.0      1   \n",
       "411    Minahan, Mrs. William Edward (Lillian E Thorpe)  female  37.0      1   \n",
       "414                       Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
       "\n",
       "     Parch         Ticket      Fare            Cabin Embarked  \n",
       "12       0          21228   82.2667              B45        S  \n",
       "14       0    W.E.P. 5734   61.1750              E31        S  \n",
       "24       3       PC 17608  262.3750  B57 B59 B63 B66        C  \n",
       "26       1         113509   61.9792              B36        C  \n",
       "28       0         113054   30.5000              A21        S  \n",
       "..     ...            ...       ...              ...      ...  \n",
       "404      0          17765   27.7208              D40        C  \n",
       "405      0  SC/PARIS 2166   13.8625              D38        C  \n",
       "407      1         113503  211.5000              C80        C  \n",
       "411      0          19928   90.0000              C78        Q  \n",
       "414      0       PC 17758  108.9000             C105        C  \n",
       "\n",
       "[91 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.Cabin.notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `.isnull()` / `.isna()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die beiden Methoden `isnull` und `.isna()` geben dir die Zeilen deines `DataFrames` zurück, die `Nan` Values enthalten. Mit diesen Methoden kannst du Werte hervorheben, die leer sind (`NaN`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./pic/selecting/built_in/4_isnull.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Um zum Beispiel Passagiere herauszufilter, deren Alter nicht angegeben ist, würden wir wie folgt vorgehen:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zuerst kann das ganze `DataFrame` abgefragt werden und man erhält einen (nicht ganz so übersichtliche) Überblick über alle Zeilen und alle Spalten und ob sich in der jeweiligen Zelle ein `Nan` Value befindet oder nicht"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>423 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
       "0          False     False   False  False  False  False  False  False   False   \n",
       "1          False     False   False  False  False   True  False  False   False   \n",
       "2          False     False   False  False  False  False  False  False   False   \n",
       "3          False     False   False  False  False   True  False  False   False   \n",
       "4          False     False   False  False  False  False  False  False   False   \n",
       "..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n",
       "418        False     False   False  False  False  False  False  False   False   \n",
       "419        False     False   False  False  False  False  False  False   False   \n",
       "420        False     False   False  False  False   True  False  False   False   \n",
       "421        False     False   False  False  False  False  False  False   False   \n",
       "422        False     False   False  False  False  False  False  False   False   \n",
       "\n",
       "      Fare  Cabin  Embarked  \n",
       "0    False  False     False  \n",
       "1    False   True     False  \n",
       "2    False  False     False  \n",
       "3    False   True     False  \n",
       "4    False   True     False  \n",
       "..     ...    ...       ...  \n",
       "418  False   True     False  \n",
       "419  False   True     False  \n",
       "420  False   True     False  \n",
       "421  False   True     False  \n",
       "422  False   True     False  \n",
       "\n",
       "[423 rows x 12 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Danach kann man sich die spezielle Spalte ansehen, die einen interessert:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "413     True\n",
       "414    False\n",
       "415    False\n",
       "416     True\n",
       "417     True\n",
       "Name: Age, Length: 418, dtype: bool"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diese Abfrage können wir in `[]` packen, um die jeweiligen Zeilen des `DataFrames` zu erhalten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Samaan, Mr. Elias</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2662</td>\n",
       "      <td>21.6792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>925</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Mrs. Andrew G (Elizabeth Lily\" Watson)\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>928</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Roth, Miss. Sarah A</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>342712</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>1300</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Riordan, Miss. Johanna Hannah\"\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>334915</td>\n",
       "      <td>7.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>1302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Naughton, Miss. Hannah</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>365237</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "10           902         0       3   \n",
       "22           914         1       1   \n",
       "29           921         0       3   \n",
       "33           925         1       3   \n",
       "36           928         1       3   \n",
       "..           ...       ...     ...   \n",
       "408         1300         1       3   \n",
       "410         1302         1       3   \n",
       "413         1305         0       3   \n",
       "416         1308         0       3   \n",
       "417         1309         0       3   \n",
       "\n",
       "                                                  Name     Sex  Age  SibSp  \\\n",
       "10                                    Ilieff, Mr. Ylio    male  NaN      0   \n",
       "22                Flegenheim, Mrs. Alfred (Antoinette)  female  NaN      0   \n",
       "29                                   Samaan, Mr. Elias    male  NaN      2   \n",
       "33   Johnston, Mrs. Andrew G (Elizabeth Lily\" Watson)\"  female  NaN      1   \n",
       "36                                 Roth, Miss. Sarah A  female  NaN      0   \n",
       "..                                                 ...     ...  ...    ...   \n",
       "408                    Riordan, Miss. Johanna Hannah\"\"  female  NaN      0   \n",
       "410                             Naughton, Miss. Hannah  female  NaN      0   \n",
       "413                                 Spector, Mr. Woolf    male  NaN      0   \n",
       "416                                Ware, Mr. Frederick    male  NaN      0   \n",
       "417                           Peter, Master. Michael J    male  NaN      1   \n",
       "\n",
       "     Parch      Ticket     Fare Cabin Embarked  \n",
       "10       0      349220   7.8958   NaN        S  \n",
       "22       0    PC 17598  31.6833   NaN        S  \n",
       "29       0        2662  21.6792   NaN        C  \n",
       "33       2  W./C. 6607  23.4500   NaN        S  \n",
       "36       0      342712   8.0500   NaN        S  \n",
       "..     ...         ...      ...   ...      ...  \n",
       "408      0      334915   7.7208   NaN        Q  \n",
       "410      0      365237   7.7500   NaN        Q  \n",
       "413      0   A.5. 3236   8.0500   NaN        S  \n",
       "416      0      359309   8.0500   NaN        S  \n",
       "417      1        2668  22.3583   NaN        C  \n",
       "\n",
       "[86 rows x 12 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age_na = df[df.Age.isnull()]\n",
    "df_age_na"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wenn dich speziell der Index interessiert, wo in welchem Fall ein `Nan` Value zu finden ist, kannst du den `.index` Operator an dein `DataFrame` anfügen, und dir werden alle Indexe zurück gegeben, die sich in dem `DataFrame` befinden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 10,  22,  29,  33,  36,  39,  41,  47,  54,  58,  65,  76,  83,\n",
       "             84,  85,  88,  91,  93, 102, 107, 108, 111, 116, 121, 124, 127,\n",
       "            132, 133, 146, 148, 151, 160, 163, 168, 170, 173, 183, 188, 191,\n",
       "            199, 200, 205, 211, 216, 219, 225, 227, 233, 243, 244, 249, 255,\n",
       "            256, 265, 266, 267, 268, 271, 273, 274, 282, 286, 288, 289, 290,\n",
       "            292, 297, 301, 304, 312, 332, 339, 342, 344, 357, 358, 365, 366,\n",
       "            380, 382, 384, 408, 410, 413, 416, 417],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age_na.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age_na.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age_na.index[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dabei spielt es überhaupt keine Rolle, welche der beiden Funktionen du verwendest. Bei Recherchen bin ich auf den Pandas Code gestoßen ([Official Pandas Code auf Github](https://github.com/pandas-dev/pandas/blob/0409521665bd436a10aea7e06336066bf07ff057/pandas/core/dtypes/missing.py#L109)) wo eindeutig hinterlegt ist, dass `.isnull()` eine Referenz auf `.isna()` darstellt. Daher kannst du frei zwischen einen der beiden wählen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./pic/selecting/built_in/7_isna_isnull.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `.duplicated()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diese Methode macht genau das, nachdem sie sich anhört: sie gibt dir Duplikate zurück. Ist also ein Passagier versehentlich zwei mal eingetragen worden, kann dieser die Statistik verfälschen. Solche Duplikate will man direkt zu Beginn sämtlicher analysen erkennen und eliminieren."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vorbereitung: Duplikate einbauen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "367 1259\n",
      "42 934\n",
      "299 1191\n",
      "255 1147\n",
      "117 1009\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, 5):\n",
    "    random_index = random.randint(0, len(df))\n",
    "    print(random_index, df.loc[random_index, 'PassengerId'])\n",
    "    \n",
    "    df = df.append(df.loc[random_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>1259</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Riihivouri, Miss. Susanna Juhantytar Sanni\"\"</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3101295</td>\n",
       "      <td>39.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>934</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Goldsmith, Mr. Nathan</td>\n",
       "      <td>male</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101263</td>\n",
       "      <td>7.8500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>1191</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johansson, Mr. Nils</td>\n",
       "      <td>male</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347467</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>1147</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>MacKay, Mr. George William</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 42795</td>\n",
       "      <td>7.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1009</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Beatrice Irene</td>\n",
       "      <td>female</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>423 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0            892         0       3   \n",
       "1            893         1       3   \n",
       "2            894         0       2   \n",
       "3            895         0       3   \n",
       "4            896         1       3   \n",
       "..           ...       ...     ...   \n",
       "367         1259         1       3   \n",
       "42           934         0       3   \n",
       "299         1191         0       3   \n",
       "255         1147         0       3   \n",
       "117         1009         1       3   \n",
       "\n",
       "                                             Name     Sex   Age  SibSp  Parch  \\\n",
       "0                                Kelly, Mr. James    male  34.5      0      0   \n",
       "1                Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
       "2                       Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
       "3                                Wirz, Mr. Albert    male  27.0      0      0   \n",
       "4    Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
       "..                                            ...     ...   ...    ...    ...   \n",
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       "42                          Goldsmith, Mr. Nathan    male  41.0      0      0   \n",
       "299                           Johansson, Mr. Nils    male  29.0      0      0   \n",
       "255                    MacKay, Mr. George William    male   NaN      0      0   \n",
       "117               Sandstrom, Miss. Beatrice Irene  female   1.0      1      1   \n",
       "\n",
       "                 Ticket     Fare Cabin Embarked  \n",
       "0                330911   7.8292   NaN        Q  \n",
       "1                363272   7.0000   NaN        S  \n",
       "2                240276   9.6875   NaN        Q  \n",
       "3                315154   8.6625   NaN        S  \n",
       "4               3101298  12.2875   NaN        S  \n",
       "..                  ...      ...   ...      ...  \n",
       "367             3101295  39.6875   NaN        S  \n",
       "42   SOTON/O.Q. 3101263   7.8500   NaN        S  \n",
       "299              347467   7.8542   NaN        S  \n",
       "255          C.A. 42795   7.5500   NaN        S  \n",
       "117             PP 9549  16.7000    G6        S  \n",
       "\n",
       "[423 rows x 12 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "<p>423 rows × 12 columns</p>\n",
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      "text/plain": [
       "     PassengerId  Survived  Pclass                                       Name  \\\n",
       "0            918         1       1               Ostby, Miss. Helene Ragnhild   \n",
       "1           1024         1       3              Lefebre, Mrs. Frank (Frances)   \n",
       "2            956         0       1                Ryerson, Master. John Borie   \n",
       "3           1136         0       3  Johnston, Master. William Arthur Willie\"\"   \n",
       "4           1252         0       3                Sage, Master. William Henry   \n",
       "..           ...       ...     ...                                        ...   \n",
       "418         1172         1       3                     Oreskovic, Miss. Jelka   \n",
       "419         1095         1       2                 Quick, Miss. Winifred Vera   \n",
       "420         1274         1       3                 Risien, Mrs. Samuel (Emma)   \n",
       "421         1161         0       3                          Pokrnic, Mr. Mate   \n",
       "422         1281         0       3                Palsson, Master. Paul Folke   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket      Fare            Cabin  \\\n",
       "0    female  22.0      0      1      113509   61.9792              B36   \n",
       "1    female   NaN      0      4        4133   25.4667              NaN   \n",
       "2      male  13.0      2      2    PC 17608  262.3750  B57 B59 B63 B66   \n",
       "3      male   NaN      1      2  W./C. 6607   23.4500              NaN   \n",
       "4      male  14.5      8      2    CA. 2343   69.5500              NaN   \n",
       "..      ...   ...    ...    ...         ...       ...              ...   \n",
       "418  female  23.0      0      0      315085    8.6625              NaN   \n",
       "419  female   8.0      1      1       26360   26.0000              NaN   \n",
       "420  female   NaN      0      0      364498   14.5000              NaN   \n",
       "421    male  17.0      0      0      315095    8.6625              NaN   \n",
       "422    male   6.0      3      1      349909   21.0750              NaN   \n",
       "\n",
       "    Embarked  \n",
       "0          C  \n",
       "1          S  \n",
       "2          C  \n",
       "3          S  \n",
       "4          S  \n",
       "..       ...  \n",
       "418        S  \n",
       "419        S  \n",
       "420        S  \n",
       "421        S  \n",
       "422        S  \n",
       "\n",
       "[423 rows x 12 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sample(frac=1).reset_index(drop=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](./pic/selecting/built_in/5_duplicated.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "169         1259         1       3   \n",
       "184         1191         0       3   \n",
       "185         1147         0       3   \n",
       "237         1009         1       3   \n",
       "347          934         0       3   \n",
       "\n",
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       "169  Riihivouri, Miss. Susanna Juhantytar Sanni\"\"  female  22.0      0      0   \n",
       "184                           Johansson, Mr. Nils    male  29.0      0      0   \n",
       "185                    MacKay, Mr. George William    male   NaN      0      0   \n",
       "237               Sandstrom, Miss. Beatrice Irene  female   1.0      1      1   \n",
       "347                         Goldsmith, Mr. Nathan    male  41.0      0      0   \n",
       "\n",
       "                 Ticket     Fare Cabin Embarked  \n",
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     "execution_count": 17,
     "metadata": {},
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   ],
   "source": [
    "df_duplicates = df[df.duplicated()]\n",
    "df_duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "      <td>14.5</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>1172</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Oreskovic, Miss. Jelka</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315085</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Quick, Miss. Winifred Vera</td>\n",
       "      <td>female</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26360</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>1274</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Risien, Mrs. Samuel (Emma)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>364498</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>1161</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Pokrnic, Mr. Mate</td>\n",
       "      <td>male</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315095</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>1281</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Paul Folke</td>\n",
       "      <td>male</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>423 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                       Name  \\\n",
       "0            918         1       1               Ostby, Miss. Helene Ragnhild   \n",
       "1           1024         1       3              Lefebre, Mrs. Frank (Frances)   \n",
       "2            956         0       1                Ryerson, Master. John Borie   \n",
       "3           1136         0       3  Johnston, Master. William Arthur Willie\"\"   \n",
       "4           1252         0       3                Sage, Master. William Henry   \n",
       "..           ...       ...     ...                                        ...   \n",
       "418         1172         1       3                     Oreskovic, Miss. Jelka   \n",
       "419         1095         1       2                 Quick, Miss. Winifred Vera   \n",
       "420         1274         1       3                 Risien, Mrs. Samuel (Emma)   \n",
       "421         1161         0       3                          Pokrnic, Mr. Mate   \n",
       "422         1281         0       3                Palsson, Master. Paul Folke   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket      Fare            Cabin  \\\n",
       "0    female  22.0      0      1      113509   61.9792              B36   \n",
       "1    female   NaN      0      4        4133   25.4667              NaN   \n",
       "2      male  13.0      2      2    PC 17608  262.3750  B57 B59 B63 B66   \n",
       "3      male   NaN      1      2  W./C. 6607   23.4500              NaN   \n",
       "4      male  14.5      8      2    CA. 2343   69.5500              NaN   \n",
       "..      ...   ...    ...    ...         ...       ...              ...   \n",
       "418  female  23.0      0      0      315085    8.6625              NaN   \n",
       "419  female   8.0      1      1       26360   26.0000              NaN   \n",
       "420  female   NaN      0      0      364498   14.5000              NaN   \n",
       "421    male  17.0      0      0      315095    8.6625              NaN   \n",
       "422    male   6.0      3      1      349909   21.0750              NaN   \n",
       "\n",
       "    Embarked  \n",
       "0          C  \n",
       "1          S  \n",
       "2          C  \n",
       "3          S  \n",
       "4          S  \n",
       "..       ...  \n",
       "418        S  \n",
       "419        S  \n",
       "420        S  \n",
       "421        S  \n",
       "422        S  \n",
       "\n",
       "[423 rows x 12 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[134, 'Name'] = 'Neuer Name'\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <td>Sandstrom, Miss. Beatrice Irene</td>\n",
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       "      <td>934</td>\n",
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       "      <td>Goldsmith, Mr. Nathan</td>\n",
       "      <td>male</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101263</td>\n",
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       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "169         1259         1       3   \n",
       "184         1191         0       3   \n",
       "185         1147         0       3   \n",
       "237         1009         1       3   \n",
       "347          934         0       3   \n",
       "\n",
       "                                             Name     Sex   Age  SibSp  Parch  \\\n",
       "169  Riihivouri, Miss. Susanna Juhantytar Sanni\"\"  female  22.0      0      0   \n",
       "184                           Johansson, Mr. Nils    male  29.0      0      0   \n",
       "185                    MacKay, Mr. George William    male   NaN      0      0   \n",
       "237               Sandstrom, Miss. Beatrice Irene  female   1.0      1      1   \n",
       "347                         Goldsmith, Mr. Nathan    male  41.0      0      0   \n",
       "\n",
       "                 Ticket     Fare Cabin Embarked  \n",
       "169             3101295  39.6875   NaN        S  \n",
       "184              347467   7.8542   NaN        S  \n",
       "185          C.A. 42795   7.5500   NaN        S  \n",
       "237             PP 9549  16.7000    G6        S  \n",
       "347  SOTON/O.Q. 3101263   7.8500   NaN        S  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_duplicates = df[df.duplicated()]\n",
    "df_duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "Mit dem Attribut `subset=` kann die Duplikatssuche auf eine oder mehrere Spalten beschränkt werden. Dadurch wird nicht die ganze Zeile mit jeder weiteren Zeile verglichen, sondern lediglich die jeweilige Spalte der Zeile mit der Spalte der anderen Zeilen. Somit kann verfeinert nach Duplikaten gesucht und präziser abgefragt werden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <td>1191</td>\n",
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       "      <td>Sandstrom, Miss. Beatrice Irene</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "169         1259         1       3   \n",
       "184         1191         0       3   \n",
       "185         1147         0       3   \n",
       "237         1009         1       3   \n",
       "347          934         0       3   \n",
       "\n",
       "                                             Name     Sex   Age  SibSp  Parch  \\\n",
       "169  Riihivouri, Miss. Susanna Juhantytar Sanni\"\"  female  22.0      0      0   \n",
       "184                           Johansson, Mr. Nils    male  29.0      0      0   \n",
       "185                    MacKay, Mr. George William    male   NaN      0      0   \n",
       "237               Sandstrom, Miss. Beatrice Irene  female   1.0      1      1   \n",
       "347                         Goldsmith, Mr. Nathan    male  41.0      0      0   \n",
       "\n",
       "                 Ticket     Fare Cabin Embarked  \n",
       "169             3101295  39.6875   NaN        S  \n",
       "184              347467   7.8542   NaN        S  \n",
       "185          C.A. 42795   7.5500   NaN        S  \n",
       "237             PP 9549  16.7000    G6        S  \n",
       "347  SOTON/O.Q. 3101263   7.8500   NaN        S  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_duplicates_col = df[df.duplicated(subset='PassengerId')]\n",
    "df_duplicates_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wie du diese Duplikate anschließend erfolgreich löschen kannst, behandeln wir zu einem späteren Zeitpunkt, da es thematisch nicht zu diesem Notebook passt."
   ]
  }
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