Session-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6d15e7fb-b843-4b44-926e-1e87a752706b",
   "metadata": {},
   "source": [
    "## Pandas Intro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1776911f-7be6-4e57-8613-688a06d7fdb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d9f72514-b6f4-4c43-a972-8448612b2349",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://raw.githubusercontent.com/pandas-dev/pandas/master/doc/data/titanic.csv\"\n",
    "titanic = pd.read_csv(url, index_col='Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cb82a30e-b6b2-44e0-aeb1-b9669a020b84",
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the first 5 lines of the dataframe\n",
    "titanic.head()\n",
    "\n",
    "# print summary statistics for each column\n",
    "titanic.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d309fd71-18e3-463b-868c-752d4e3d0218",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived\n",
      "0    30.626179\n",
      "1    28.343690\n",
      "Name: Age, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(titanic.groupby(\"Survived\")[\"Age\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "129882fb-dd58-4be9-970a-200f90d94ed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.hist(column='Age', by='Survived', bins=25, figsize=(8,10),\n",
    "             layout=(2,1), zorder=2, sharex=True, rwidth=0.9);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a840f0ce-184a-4ce3-a379-62d63d7cfcca",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m\n",
       "\u001b[0mtitanic\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mcolumn\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'IndexLabel'\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mby\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mgrid\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'bool'\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mxlabelsize\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'int | None'\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mxrot\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'float | None'\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mylabelsize\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'int | None'\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0myrot\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'float | None'\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0msharex\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'bool'\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0msharey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'bool'\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mfigsize\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'tuple[int, int] | None'\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mlayout\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'tuple[int, int] | None'\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mbins\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'int | Sequence[int]'\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mbackend\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'str | None'\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mlegend\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'bool'\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
       "\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m\n",
       "Make a histogram of the DataFrame's columns.\n",
       "\n",
       "A `histogram`_ is a representation of the distribution of data.\n",
       "This function calls :meth:`matplotlib.pyplot.hist`, on each series in\n",
       "the DataFrame, resulting in one histogram per column.\n",
       "\n",
       ".. _histogram: https://en.wikipedia.org/wiki/Histogram\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "data : DataFrame\n",
       "    The pandas object holding the data.\n",
       "column : str or sequence, optional\n",
       "    If passed, will be used to limit data to a subset of columns.\n",
       "by : object, optional\n",
       "    If passed, then used to form histograms for separate groups.\n",
       "grid : bool, default True\n",
       "    Whether to show axis grid lines.\n",
       "xlabelsize : int, default None\n",
       "    If specified changes the x-axis label size.\n",
       "xrot : float, default None\n",
       "    Rotation of x axis labels. For example, a value of 90 displays the\n",
       "    x labels rotated 90 degrees clockwise.\n",
       "ylabelsize : int, default None\n",
       "    If specified changes the y-axis label size.\n",
       "yrot : float, default None\n",
       "    Rotation of y axis labels. For example, a value of 90 displays the\n",
       "    y labels rotated 90 degrees clockwise.\n",
       "ax : Matplotlib axes object, default None\n",
       "    The axes to plot the histogram on.\n",
       "sharex : bool, default True if ax is None else False\n",
       "    In case subplots=True, share x axis and set some x axis labels to\n",
       "    invisible; defaults to True if ax is None otherwise False if an ax\n",
       "    is passed in.\n",
       "    Note that passing in both an ax and sharex=True will alter all x axis\n",
       "    labels for all subplots in a figure.\n",
       "sharey : bool, default False\n",
       "    In case subplots=True, share y axis and set some y axis labels to\n",
       "    invisible.\n",
       "figsize : tuple, optional\n",
       "    The size in inches of the figure to create. Uses the value in\n",
       "    `matplotlib.rcParams` by default.\n",
       "layout : tuple, optional\n",
       "    Tuple of (rows, columns) for the layout of the histograms.\n",
       "bins : int or sequence, default 10\n",
       "    Number of histogram bins to be used. If an integer is given, bins + 1\n",
       "    bin edges are calculated and returned. If bins is a sequence, gives\n",
       "    bin edges, including left edge of first bin and right edge of last\n",
       "    bin. In this case, bins is returned unmodified.\n",
       "\n",
       "backend : str, default None\n",
       "    Backend to use instead of the backend specified in the option\n",
       "    ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to\n",
       "    specify the ``plotting.backend`` for the whole session, set\n",
       "    ``pd.options.plotting.backend``.\n",
       "\n",
       "    .. versionadded:: 1.0.0\n",
       "\n",
       "legend : bool, default False\n",
       "    Whether to show the legend.\n",
       "\n",
       "    .. versionadded:: 1.1.0\n",
       "\n",
       "**kwargs\n",
       "    All other plotting keyword arguments to be passed to\n",
       "    :meth:`matplotlib.pyplot.hist`.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "matplotlib.AxesSubplot or numpy.ndarray of them\n",
       "\n",
       "See Also\n",
       "--------\n",
       "matplotlib.pyplot.hist : Plot a histogram using matplotlib.\n",
       "\n",
       "Examples\n",
       "--------\n",
       "This example draws a histogram based on the length and width of\n",
       "some animals, displayed in three bins\n",
       "\n",
       ".. plot::\n",
       "    :context: close-figs\n",
       "\n",
       "    >>> df = pd.DataFrame({\n",
       "    ...     'length': [1.5, 0.5, 1.2, 0.9, 3],\n",
       "    ...     'width': [0.7, 0.2, 0.15, 0.2, 1.1]\n",
       "    ...     }, index=['pig', 'rabbit', 'duck', 'chicken', 'horse'])\n",
       "    >>> hist = df.hist(bins=3)\n",
       "\u001b[1;31mFile:\u001b[0m      c:\\users\\henpo22\\documents\\projects\\python course ht2022\\venv\\lib\\site-packages\\pandas\\plotting\\_core.py\n",
       "\u001b[1;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.hist?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c34409f0-c2d1-4ad3-83f3-d8ed2fd88fa0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 891 entries, Braund, Mr. Owen Harris to Dooley, Mr. Patrick\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Sex          891 non-null    object \n",
      " 4   Age          714 non-null    float64\n",
      " 5   SibSp        891 non-null    int64  \n",
      " 6   Parch        891 non-null    int64  \n",
      " 7   Ticket       891 non-null    object \n",
      " 8   Fare         891 non-null    float64\n",
      " 9   Cabin        204 non-null    object \n",
      " 10  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 83.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# print some information about the columns\n",
    "titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "130e168f-bb76-4374-8674-79520b253b57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"Age\"]\n",
    "titanic.Age          # same as above\n",
    "type(titanic[\"Age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ad70a310-d245-4c12-8dd4-517d6b577d08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch',\n",
       "       'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "03ee51cd-137e-4be0-befb-0cecd1a9bcdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Braund, Mr. Owen Harris',\n",
       "       'Cumings, Mrs. John Bradley (Florence Briggs Thayer)',\n",
       "       'Heikkinen, Miss. Laina',\n",
       "       'Futrelle, Mrs. Jacques Heath (Lily May Peel)',\n",
       "       'Allen, Mr. William Henry', 'Moran, Mr. James',\n",
       "       'McCarthy, Mr. Timothy J', 'Palsson, Master. Gosta Leonard',\n",
       "       'Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)',\n",
       "       'Nasser, Mrs. Nicholas (Adele Achem)',\n",
       "       ...\n",
       "       'Markun, Mr. Johann', 'Dahlberg, Miss. Gerda Ulrika',\n",
       "       'Banfield, Mr. Frederick James', 'Sutehall, Mr. Henry Jr',\n",
       "       'Rice, Mrs. William (Margaret Norton)', 'Montvila, Rev. Juozas',\n",
       "       'Graham, Miss. Margaret Edith',\n",
       "       'Johnston, Miss. Catherine Helen \"Carrie\"', 'Behr, Mr. Karl Howell',\n",
       "       'Dooley, Mr. Patrick'],\n",
       "      dtype='object', name='Name', length=891)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c364d7e0-ff74-4ee9-b064-21dca8ae9dc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic.loc['Lam, Mr. Ali',\"Age\"]          # select single value by row and column\n",
    "titanic.loc[:'Lam, Mr. Ali',\"Age\"]         # slice the dataframe by row and column *names*\n",
    "titanic.iloc[0:2,3:6]                      # same slice as above by row and column *numbers*\n",
    "\n",
    "titanic.at['Lam, Mr. Ali',\"Age\"] = 42      # set single value by row and column *name* (fast)\n",
    "titanic.at['Lam, Mr. Ali',\"Age\"]           # select single value by row and column *name* (fast)\n",
    "titanic.at['Lam, Mr. Ali',\"Age\"] = 42      # set single value by row and column *name* (fast)\n",
    "titanic.iat[0,5]                           # select same value by row and column *number* (fast)\n",
    "\n",
    "titanic[\"is_passenger\"] = True             # set a whole column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f158d00c-ff2b-4413-92e9-0bbd355bd75c",
   "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>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",
       "      <th>is_passenger</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Devaney, Miss. Margaret Delia</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330958</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown, Mrs. James Joseph (Margaret Tobin)</th>\n",
       "      <td>195</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17610</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>B4</td>\n",
       "      <td>C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madigan, Miss. Margaret \"Maggie\"</th>\n",
       "      <td>199</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370370</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fleming, Miss. Margaret</th>\n",
       "      <td>307</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17421</td>\n",
       "      <td>110.8833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hays, Miss. Margaret Bechstein</th>\n",
       "      <td>311</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C54</td>\n",
       "      <td>C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)</th>\n",
       "      <td>320</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16966</td>\n",
       "      <td>134.5000</td>\n",
       "      <td>E34</td>\n",
       "      <td>C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burns, Miss. Elizabeth Margaret</th>\n",
       "      <td>338</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16966</td>\n",
       "      <td>134.5000</td>\n",
       "      <td>E40</td>\n",
       "      <td>C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ford, Miss. Doolina Margaret \"Daisy\"</th>\n",
       "      <td>437</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6608</td>\n",
       "      <td>34.3750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Murphy, Miss. Margaret Jane</th>\n",
       "      <td>613</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>367230</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mannion, Miss. Margareth</th>\n",
       "      <td>728</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36866</td>\n",
       "      <td>7.7375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ford, Mrs. Edward (Margaret Ann Watson)</th>\n",
       "      <td>737</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>W./C. 6608</td>\n",
       "      <td>34.3750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swift, Mrs. Frederick Joel (Margaret Welles Barron)</th>\n",
       "      <td>863</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17466</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>D17</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rice, Mrs. William (Margaret Norton)</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graham, Miss. Margaret Edith</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Devaney, Miss. Margaret Delia                                45         1   \n",
       "Brown, Mrs. James Joseph (Margaret Tobin)                   195         1   \n",
       "Madigan, Miss. Margaret \"Maggie\"                            199         1   \n",
       "Fleming, Miss. Margaret                                     307         1   \n",
       "Hays, Miss. Margaret Bechstein                              311         1   \n",
       "Spedden, Mrs. Frederic Oakley (Margaretta Corni...          320         1   \n",
       "Burns, Miss. Elizabeth Margaret                             338         1   \n",
       "Ford, Miss. Doolina Margaret \"Daisy\"                        437         0   \n",
       "Murphy, Miss. Margaret Jane                                 613         1   \n",
       "Mannion, Miss. Margareth                                    728         1   \n",
       "Ford, Mrs. Edward (Margaret Ann Watson)                     737         0   \n",
       "Swift, Mrs. Frederick Joel (Margaret Welles Bar...          863         1   \n",
       "Rice, Mrs. William (Margaret Norton)                        886         0   \n",
       "Graham, Miss. Margaret Edith                                888         1   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Devaney, Miss. Margaret Delia                            3  female  19.0   \n",
       "Brown, Mrs. James Joseph (Margaret Tobin)                1  female  44.0   \n",
       "Madigan, Miss. Margaret \"Maggie\"                         3  female   NaN   \n",
       "Fleming, Miss. Margaret                                  1  female   NaN   \n",
       "Hays, Miss. Margaret Bechstein                           1  female  24.0   \n",
       "Spedden, Mrs. Frederic Oakley (Margaretta Corni...       1  female  40.0   \n",
       "Burns, Miss. Elizabeth Margaret                          1  female  41.0   \n",
       "Ford, Miss. Doolina Margaret \"Daisy\"                     3  female  21.0   \n",
       "Murphy, Miss. Margaret Jane                              3  female   NaN   \n",
       "Mannion, Miss. Margareth                                 3  female   NaN   \n",
       "Ford, Mrs. Edward (Margaret Ann Watson)                  3  female  48.0   \n",
       "Swift, Mrs. Frederick Joel (Margaret Welles Bar...       1  female  48.0   \n",
       "Rice, Mrs. William (Margaret Norton)                     3  female  39.0   \n",
       "Graham, Miss. Margaret Edith                             1  female  19.0   \n",
       "\n",
       "                                                    SibSp  Parch      Ticket  \\\n",
       "Name                                                                           \n",
       "Devaney, Miss. Margaret Delia                           0      0      330958   \n",
       "Brown, Mrs. James Joseph (Margaret Tobin)               0      0    PC 17610   \n",
       "Madigan, Miss. Margaret \"Maggie\"                        0      0      370370   \n",
       "Fleming, Miss. Margaret                                 0      0       17421   \n",
       "Hays, Miss. Margaret Bechstein                          0      0       11767   \n",
       "Spedden, Mrs. Frederic Oakley (Margaretta Corni...      1      1       16966   \n",
       "Burns, Miss. Elizabeth Margaret                         0      0       16966   \n",
       "Ford, Miss. Doolina Margaret \"Daisy\"                    2      2  W./C. 6608   \n",
       "Murphy, Miss. Margaret Jane                             1      0      367230   \n",
       "Mannion, Miss. Margareth                                0      0       36866   \n",
       "Ford, Mrs. Edward (Margaret Ann Watson)                 1      3  W./C. 6608   \n",
       "Swift, Mrs. Frederick Joel (Margaret Welles Bar...      0      0       17466   \n",
       "Rice, Mrs. William (Margaret Norton)                    0      5      382652   \n",
       "Graham, Miss. Margaret Edith                            0      0      112053   \n",
       "\n",
       "                                                        Fare Cabin Embarked  \\\n",
       "Name                                                                          \n",
       "Devaney, Miss. Margaret Delia                         7.8792   NaN        Q   \n",
       "Brown, Mrs. James Joseph (Margaret Tobin)            27.7208    B4        C   \n",
       "Madigan, Miss. Margaret \"Maggie\"                      7.7500   NaN        Q   \n",
       "Fleming, Miss. Margaret                             110.8833   NaN        C   \n",
       "Hays, Miss. Margaret Bechstein                       83.1583   C54        C   \n",
       "Spedden, Mrs. Frederic Oakley (Margaretta Corni...  134.5000   E34        C   \n",
       "Burns, Miss. Elizabeth Margaret                     134.5000   E40        C   \n",
       "Ford, Miss. Doolina Margaret \"Daisy\"                 34.3750   NaN        S   \n",
       "Murphy, Miss. Margaret Jane                          15.5000   NaN        Q   \n",
       "Mannion, Miss. Margareth                              7.7375   NaN        Q   \n",
       "Ford, Mrs. Edward (Margaret Ann Watson)              34.3750   NaN        S   \n",
       "Swift, Mrs. Frederick Joel (Margaret Welles Bar...   25.9292   D17        S   \n",
       "Rice, Mrs. William (Margaret Norton)                 29.1250   NaN        Q   \n",
       "Graham, Miss. Margaret Edith                         30.0000   B42        S   \n",
       "\n",
       "                                                    is_passenger  \n",
       "Name                                                              \n",
       "Devaney, Miss. Margaret Delia                               True  \n",
       "Brown, Mrs. James Joseph (Margaret Tobin)                   True  \n",
       "Madigan, Miss. Margaret \"Maggie\"                            True  \n",
       "Fleming, Miss. Margaret                                     True  \n",
       "Hays, Miss. Margaret Bechstein                              True  \n",
       "Spedden, Mrs. Frederic Oakley (Margaretta Corni...          True  \n",
       "Burns, Miss. Elizabeth Margaret                             True  \n",
       "Ford, Miss. Doolina Margaret \"Daisy\"                        True  \n",
       "Murphy, Miss. Margaret Jane                                 True  \n",
       "Mannion, Miss. Margareth                                    True  \n",
       "Ford, Mrs. Edward (Margaret Ann Watson)                     True  \n",
       "Swift, Mrs. Frederick Joel (Margaret Welles Bar...          True  \n",
       "Rice, Mrs. William (Margaret Norton)                        True  \n",
       "Graham, Miss. Margaret Edith                                True  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[titanic[\"Age\"] > 70]\n",
    "# \".str\" creates a string object from a column\n",
    "titanic[titanic.index.str.contains(\"Margaret\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c0c9b240-718f-43a7-bed7-840674fd4472",
   "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>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",
       "      <th>is_passenger</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Braund, Mr. Owen Harris</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heikkinen, Miss. Laina</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>C85</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allen, Mr. William Henry</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montvila, Rev. Juozas</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>C50</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graham, Miss. Margaret Edith</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnston, Miss. Catherine Helen \"Carrie\"</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Behr, Mr. Karl Howell</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dooley, Mr. Patrick</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>C148</td>\n",
       "      <td>Q</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                                       1         0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2         1   \n",
       "Heikkinen, Miss. Laina                                        3         1   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  4         1   \n",
       "Allen, Mr. William Henry                                      5         0   \n",
       "...                                                         ...       ...   \n",
       "Montvila, Rev. Juozas                                       887         0   \n",
       "Graham, Miss. Margaret Edith                                888         1   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                    889         0   \n",
       "Behr, Mr. Karl Howell                                       890         1   \n",
       "Dooley, Mr. Patrick                                         891         0   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Braund, Mr. Owen Harris                                  3    male  22.0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female  38.0   \n",
       "Heikkinen, Miss. Laina                                   3  female  26.0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)             1  female  35.0   \n",
       "Allen, Mr. William Henry                                 3    male  35.0   \n",
       "...                                                    ...     ...   ...   \n",
       "Montvila, Rev. Juozas                                    2    male  27.0   \n",
       "Graham, Miss. Margaret Edith                             1  female  19.0   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                 3  female  19.0   \n",
       "Behr, Mr. Karl Howell                                    1    male  26.0   \n",
       "Dooley, Mr. Patrick                                      3    male  32.0   \n",
       "\n",
       "                                                    SibSp  Parch  \\\n",
       "Name                                                               \n",
       "Braund, Mr. Owen Harris                                 1      0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...      1      0   \n",
       "Heikkinen, Miss. Laina                                  0      0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)            1      0   \n",
       "Allen, Mr. William Henry                                0      0   \n",
       "...                                                   ...    ...   \n",
       "Montvila, Rev. Juozas                                   0      0   \n",
       "Graham, Miss. Margaret Edith                            0      0   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                1      2   \n",
       "Behr, Mr. Karl Howell                                   0      0   \n",
       "Dooley, Mr. Patrick                                     0      0   \n",
       "\n",
       "                                                              Ticket     Fare  \\\n",
       "Name                                                                            \n",
       "Braund, Mr. Owen Harris                                    A/5 21171   7.2500   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...          PC 17599  71.2833   \n",
       "Heikkinen, Miss. Laina                              STON/O2. 3101282   7.9250   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  113803  53.1000   \n",
       "Allen, Mr. William Henry                                      373450   8.0500   \n",
       "...                                                              ...      ...   \n",
       "Montvila, Rev. Juozas                                         211536  13.0000   \n",
       "Graham, Miss. Margaret Edith                                  112053  30.0000   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                  W./C. 6607  23.4500   \n",
       "Behr, Mr. Karl Howell                                         111369  30.0000   \n",
       "Dooley, Mr. Patrick                                           370376   7.7500   \n",
       "\n",
       "                                                   Cabin Embarked  \\\n",
       "Name                                                                \n",
       "Braund, Mr. Owen Harris                              NaN        S   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...   C85        C   \n",
       "Heikkinen, Miss. Laina                               C85        S   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)        C123        S   \n",
       "Allen, Mr. William Henry                            C123        S   \n",
       "...                                                  ...      ...   \n",
       "Montvila, Rev. Juozas                                C50        S   \n",
       "Graham, Miss. Margaret Edith                         B42        S   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"             B42        S   \n",
       "Behr, Mr. Karl Howell                               C148        C   \n",
       "Dooley, Mr. Patrick                                 C148        Q   \n",
       "\n",
       "                                                    is_passenger  \n",
       "Name                                                              \n",
       "Braund, Mr. Owen Harris                                     True  \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...          True  \n",
       "Heikkinen, Miss. Laina                                      True  \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                True  \n",
       "Allen, Mr. William Henry                                    True  \n",
       "...                                                          ...  \n",
       "Montvila, Rev. Juozas                                       True  \n",
       "Graham, Miss. Margaret Edith                                True  \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                    True  \n",
       "Behr, Mr. Karl Howell                                       True  \n",
       "Dooley, Mr. Patrick                                         True  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isna()                    # returns boolean mask of NaN values\n",
    "titanic.dropna()                  # drop missing values\n",
    "titanic.dropna(how=\"any\")         # or how=\"all\"\n",
    "titanic.dropna(subset=[\"Cabin\"])  # only drop NaNs from one column\n",
    "titanic.fillna(0)                 # replace NaNs with zero\n",
    "titanic.fillna(method='ffill')    # forward-fill NaNs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8316a78-3971-42bb-85b8-5349cc7d55a4",
   "metadata": {},
   "source": [
    "## Exercise 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "82194e61-9ea2-4c85-8383-af848b574645",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mType:\u001b[0m        property\n",
       "\u001b[1;31mString form:\u001b[0m <property object at 0x000002B4CBC1AE00>\n",
       "\u001b[1;31mDocstring:\u001b[0m  \n",
       "Purely integer-location based indexing for selection by position.\n",
       "\n",
       "``.iloc[]`` is primarily integer position based (from ``0`` to\n",
       "``length-1`` of the axis), but may also be used with a boolean\n",
       "array.\n",
       "\n",
       "Allowed inputs are:\n",
       "\n",
       "- An integer, e.g. ``5``.\n",
       "- A list or array of integers, e.g. ``[4, 3, 0]``.\n",
       "- A slice object with ints, e.g. ``1:7``.\n",
       "- A boolean array.\n",
       "- A ``callable`` function with one argument (the calling Series or\n",
       "  DataFrame) and that returns valid output for indexing (one of the above).\n",
       "  This is useful in method chains, when you don't have a reference to the\n",
       "  calling object, but would like to base your selection on some value.\n",
       "- A tuple of row and column indexes. The tuple elements consist of one of the\n",
       "  above inputs, e.g. ``(0, 1)``.\n",
       "\n",
       "``.iloc`` will raise ``IndexError`` if a requested indexer is\n",
       "out-of-bounds, except *slice* indexers which allow out-of-bounds\n",
       "indexing (this conforms with python/numpy *slice* semantics).\n",
       "\n",
       "See more at :ref:`Selection by Position <indexing.integer>`.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "DataFrame.iat : Fast integer location scalar accessor.\n",
       "DataFrame.loc : Purely label-location based indexer for selection by label.\n",
       "Series.iloc : Purely integer-location based indexing for\n",
       "               selection by position.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4},\n",
       "...           {'a': 100, 'b': 200, 'c': 300, 'd': 400},\n",
       "...           {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000 }]\n",
       ">>> df = pd.DataFrame(mydict)\n",
       ">>> df\n",
       "      a     b     c     d\n",
       "0     1     2     3     4\n",
       "1   100   200   300   400\n",
       "2  1000  2000  3000  4000\n",
       "\n",
       "**Indexing just the rows**\n",
       "\n",
       "With a scalar integer.\n",
       "\n",
       ">>> type(df.iloc[0])\n",
       "<class 'pandas.core.series.Series'>\n",
       ">>> df.iloc[0]\n",
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "Name: 0, dtype: int64\n",
       "\n",
       "With a list of integers.\n",
       "\n",
       ">>> df.iloc[[0]]\n",
       "   a  b  c  d\n",
       "0  1  2  3  4\n",
       ">>> type(df.iloc[[0]])\n",
       "<class 'pandas.core.frame.DataFrame'>\n",
       "\n",
       ">>> df.iloc[[0, 1]]\n",
       "     a    b    c    d\n",
       "0    1    2    3    4\n",
       "1  100  200  300  400\n",
       "\n",
       "With a `slice` object.\n",
       "\n",
       ">>> df.iloc[:3]\n",
       "      a     b     c     d\n",
       "0     1     2     3     4\n",
       "1   100   200   300   400\n",
       "2  1000  2000  3000  4000\n",
       "\n",
       "With a boolean mask the same length as the index.\n",
       "\n",
       ">>> df.iloc[[True, False, True]]\n",
       "      a     b     c     d\n",
       "0     1     2     3     4\n",
       "2  1000  2000  3000  4000\n",
       "\n",
       "With a callable, useful in method chains. The `x` passed\n",
       "to the ``lambda`` is the DataFrame being sliced. This selects\n",
       "the rows whose index label even.\n",
       "\n",
       ">>> df.iloc[lambda x: x.index % 2 == 0]\n",
       "      a     b     c     d\n",
       "0     1     2     3     4\n",
       "2  1000  2000  3000  4000\n",
       "\n",
       "**Indexing both axes**\n",
       "\n",
       "You can mix the indexer types for the index and columns. Use ``:`` to\n",
       "select the entire axis.\n",
       "\n",
       "With scalar integers.\n",
       "\n",
       ">>> df.iloc[0, 1]\n",
       "2\n",
       "\n",
       "With lists of integers.\n",
       "\n",
       ">>> df.iloc[[0, 2], [1, 3]]\n",
       "      b     d\n",
       "0     2     4\n",
       "2  2000  4000\n",
       "\n",
       "With `slice` objects.\n",
       "\n",
       ">>> df.iloc[1:3, 0:3]\n",
       "      a     b     c\n",
       "1   100   200   300\n",
       "2  1000  2000  3000\n",
       "\n",
       "With a boolean array whose length matches the columns.\n",
       "\n",
       ">>> df.iloc[:, [True, False, True, False]]\n",
       "      a     c\n",
       "0     1     3\n",
       "1   100   300\n",
       "2  1000  3000\n",
       "\n",
       "With a callable function that expects the Series or DataFrame.\n",
       "\n",
       ">>> df.iloc[:, lambda df: [0, 2]]\n",
       "      a     c\n",
       "0     1     3\n",
       "1   100   300\n",
       "2  1000  3000\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.iloc?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b61e99d3-a84c-457d-bf14-af31f8acbb06",
   "metadata": {
    "tags": []
   },
   "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>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",
       "      <th>is_passenger</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Braund, Mr. Owen Harris</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heikkinen, Miss. Laina</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allen, Mr. William Henry</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moran, Mr. James</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>McCarthy, Mr. Timothy J</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Palsson, Master. Gosta Leonard</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</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",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nasser, Mrs. Nicholas (Adele Achem)</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                                       1         0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2         1   \n",
       "Heikkinen, Miss. Laina                                        3         1   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  4         1   \n",
       "Allen, Mr. William Henry                                      5         0   \n",
       "Moran, Mr. James                                              6         0   \n",
       "McCarthy, Mr. Timothy J                                       7         0   \n",
       "Palsson, Master. Gosta Leonard                                8         0   \n",
       "Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)             9         1   \n",
       "Nasser, Mrs. Nicholas (Adele Achem)                          10         1   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Braund, Mr. Owen Harris                                  3    male  22.0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female  38.0   \n",
       "Heikkinen, Miss. Laina                                   3  female  26.0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)             1  female  35.0   \n",
       "Allen, Mr. William Henry                                 3    male  35.0   \n",
       "Moran, Mr. James                                         3    male   NaN   \n",
       "McCarthy, Mr. Timothy J                                  1    male  54.0   \n",
       "Palsson, Master. Gosta Leonard                           3    male   2.0   \n",
       "Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)        3  female  27.0   \n",
       "Nasser, Mrs. Nicholas (Adele Achem)                      2  female  14.0   \n",
       "\n",
       "                                                    SibSp  Parch  \\\n",
       "Name                                                               \n",
       "Braund, Mr. Owen Harris                                 1      0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...      1      0   \n",
       "Heikkinen, Miss. Laina                                  0      0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)            1      0   \n",
       "Allen, Mr. William Henry                                0      0   \n",
       "Moran, Mr. James                                        0      0   \n",
       "McCarthy, Mr. Timothy J                                 0      0   \n",
       "Palsson, Master. Gosta Leonard                          3      1   \n",
       "Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)       0      2   \n",
       "Nasser, Mrs. Nicholas (Adele Achem)                     1      0   \n",
       "\n",
       "                                                              Ticket     Fare  \\\n",
       "Name                                                                            \n",
       "Braund, Mr. Owen Harris                                    A/5 21171   7.2500   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...          PC 17599  71.2833   \n",
       "Heikkinen, Miss. Laina                              STON/O2. 3101282   7.9250   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  113803  53.1000   \n",
       "Allen, Mr. William Henry                                      373450   8.0500   \n",
       "Moran, Mr. James                                              330877   8.4583   \n",
       "McCarthy, Mr. Timothy J                                        17463  51.8625   \n",
       "Palsson, Master. Gosta Leonard                                349909  21.0750   \n",
       "Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)             347742  11.1333   \n",
       "Nasser, Mrs. Nicholas (Adele Achem)                           237736  30.0708   \n",
       "\n",
       "                                                   Cabin Embarked  \\\n",
       "Name                                                                \n",
       "Braund, Mr. Owen Harris                              NaN        S   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...   C85        C   \n",
       "Heikkinen, Miss. Laina                               NaN        S   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)        C123        S   \n",
       "Allen, Mr. William Henry                             NaN        S   \n",
       "Moran, Mr. James                                     NaN        Q   \n",
       "McCarthy, Mr. Timothy J                              E46        S   \n",
       "Palsson, Master. Gosta Leonard                       NaN        S   \n",
       "Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)    NaN        S   \n",
       "Nasser, Mrs. Nicholas (Adele Achem)                  NaN        C   \n",
       "\n",
       "                                                    is_passenger  \n",
       "Name                                                              \n",
       "Braund, Mr. Owen Harris                                     True  \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...          True  \n",
       "Heikkinen, Miss. Laina                                      True  \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                True  \n",
       "Allen, Mr. William Henry                                    True  \n",
       "Moran, Mr. James                                            True  \n",
       "McCarthy, Mr. Timothy J                                     True  \n",
       "Palsson, Master. Gosta Leonard                              True  \n",
       "Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)           True  \n",
       "Nasser, Mrs. Nicholas (Adele Achem)                         True  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.iloc[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3a371964-8506-4e93-88dd-0e0c534cad9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.11111111111111"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.iloc[:10][\"Age\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a11a4a1b-210b-43c3-8cc1-748fc82225d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_age = titanic.iloc[:10][\"Age\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aa22fdfd-146a-438f-a395-72b493ee1fc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                0\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    1\n",
       "Heikkinen, Miss. Laina                                 1\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)           1\n",
       "Allen, Mr. William Henry                               0\n",
       "                                                      ..\n",
       "Montvila, Rev. Juozas                                  0\n",
       "Graham, Miss. Margaret Edith                           1\n",
       "Johnston, Miss. Catherine Helen \"Carrie\"               0\n",
       "Behr, Mr. Karl Howell                                  1\n",
       "Dooley, Mr. Patrick                                    0\n",
       "Name: Survived, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4599f304-30b4-4db0-acbc-fdeaf85fbf1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                False\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)     True\n",
       "Heikkinen, Miss. Laina                                  True\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)            True\n",
       "Allen, Mr. William Henry                               False\n",
       "                                                       ...  \n",
       "Montvila, Rev. Juozas                                  False\n",
       "Graham, Miss. Margaret Edith                            True\n",
       "Johnston, Miss. Catherine Helen \"Carrie\"               False\n",
       "Behr, Mr. Karl Howell                                   True\n",
       "Dooley, Mr. Patrick                                    False\n",
       "Name: Survived, Length: 891, dtype: bool"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"Survived\"]>0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cfc72175-37e9-4bf7-b4a5-ee3077bf575b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                 True\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    False\n",
       "Heikkinen, Miss. Laina                                  True\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)           False\n",
       "Allen, Mr. William Henry                               False\n",
       "                                                       ...  \n",
       "Montvila, Rev. Juozas                                   True\n",
       "Graham, Miss. Margaret Edith                            True\n",
       "Johnston, Miss. Catherine Helen \"Carrie\"               False\n",
       "Behr, Mr. Karl Howell                                   True\n",
       "Dooley, Mr. Patrick                                    False\n",
       "Name: Age, Length: 891, dtype: bool"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"Age\"] < avg_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "16685c00-1e45-442b-bec3-1fbaedb58086",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                 True\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    False\n",
       "Heikkinen, Miss. Laina                                  True\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)           False\n",
       "Allen, Mr. William Henry                               False\n",
       "                                                       ...  \n",
       "Montvila, Rev. Juozas                                   True\n",
       "Graham, Miss. Margaret Edith                            True\n",
       "Johnston, Miss. Catherine Helen \"Carrie\"               False\n",
       "Behr, Mr. Karl Howell                                   True\n",
       "Dooley, Mr. Patrick                                    False\n",
       "Name: Age, Length: 891, dtype: bool"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"Age\"] < titanic.iloc[:][\"Age\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aacc361a-7ebf-4b7d-b0f3-32c5153900d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                 True\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    False\n",
       "Heikkinen, Miss. Laina                                  True\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)           False\n",
       "Allen, Mr. William Henry                               False\n",
       "                                                       ...  \n",
       "Montvila, Rev. Juozas                                   True\n",
       "Graham, Miss. Margaret Edith                            True\n",
       "Johnston, Miss. Catherine Helen \"Carrie\"               False\n",
       "Behr, Mr. Karl Howell                                   True\n",
       "Dooley, Mr. Patrick                                    False\n",
       "Name: Age, Length: 891, dtype: bool"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"Age\"] < avg_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ca2cf173-5336-4cd8-a191-07881060bf86",
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_age = titanic.iloc[:][\"Age\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "57c3419d-ef1a-4ebe-a74c-69de4dc4e65c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.716321678321677"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0fbe7146-45e0-4b65-afe9-13dca7d47a14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                 True\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    False\n",
       "Heikkinen, Miss. Laina                                  True\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)           False\n",
       "Allen, Mr. William Henry                               False\n",
       "                                                       ...  \n",
       "Montvila, Rev. Juozas                                   True\n",
       "Graham, Miss. Margaret Edith                            True\n",
       "Johnston, Miss. Catherine Helen \"Carrie\"               False\n",
       "Behr, Mr. Karl Howell                                   True\n",
       "Dooley, Mr. Patrick                                    False\n",
       "Name: Age, Length: 891, dtype: bool"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bool index < avg_age\n",
    "titanic[\"Age\"] < avg_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9d261462-b571-43cb-b6fe-37e91e0b2c6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                              0\n",
       "Heikkinen, Miss. Laina                               1\n",
       "Palsson, Master. Gosta Leonard                       0\n",
       "Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)    1\n",
       "Nasser, Mrs. Nicholas (Adele Achem)                  1\n",
       "                                                    ..\n",
       "Banfield, Mr. Frederick James                        0\n",
       "Sutehall, Mr. Henry Jr                               0\n",
       "Montvila, Rev. Juozas                                0\n",
       "Graham, Miss. Margaret Edith                         1\n",
       "Behr, Mr. Karl Howell                                1\n",
       "Name: Survived, Length: 384, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# survived tag for the ones < avg_age\n",
    "titanic[titanic[\"Age\"] < avg_age][\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "84cffaaa-8d46-48ec-a8b2-f87a1e74b679",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40625"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# chance of surviving below avg_age\n",
    "titanic[titanic[\"Age\"] < avg_age][\"Survived\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3cce5582-7185-4d12-88b1-49e31ef5ccd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4078549848942598"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# chance of surviving above avg_age\n",
    "titanic[titanic[\"Age\"] > avg_age][\"Survived\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adddbcac-e37e-4c32-95a1-baadde269bfa",
   "metadata": {},
   "source": [
    "## Tidy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9e9b1165-d50d-47cc-9999-034c90ac44e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fb9bdad2-b14b-47e6-b84d-aae7c3a0bf45",
   "metadata": {},
   "outputs": [],
   "source": [
    "runners = pd.DataFrame([\n",
    "              {'Runner': 'Runner 1', 400: 64, 800: 128, 1200: 192, 1500: 240},\n",
    "              {'Runner': 'Runner 2', 400: 80, 800: 160, 1200: 240, 1500: 300},\n",
    "              {'Runner': 'Runner 3', 400: 96, 800: 192, 1200: 288, 1500: 360},\n",
    "          ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2e89611a-e011-468a-ba42-dcd8cd737f61",
   "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>Runner</th>\n",
       "      <th>400</th>\n",
       "      <th>800</th>\n",
       "      <th>1200</th>\n",
       "      <th>1500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Runner 1</td>\n",
       "      <td>64</td>\n",
       "      <td>128</td>\n",
       "      <td>192</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Runner 2</td>\n",
       "      <td>80</td>\n",
       "      <td>160</td>\n",
       "      <td>240</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Runner 3</td>\n",
       "      <td>96</td>\n",
       "      <td>192</td>\n",
       "      <td>288</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Runner  400  800  1200  1500\n",
       "0  Runner 1   64  128   192   240\n",
       "1  Runner 2   80  160   240   300\n",
       "2  Runner 3   96  192   288   360"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "378195c8-a1f7-4ec9-a061-abbb815333bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "runners = pd.melt(runners, id_vars=\"Runner\",\n",
    "              value_vars=[400, 800, 1200, 1500],\n",
    "              var_name=\"distance\",\n",
    "              value_name=\"time\"\n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "817ff802-304e-4828-8234-3c73c9de5bcb",
   "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>Runner</th>\n",
       "      <th>distance</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Runner 1</td>\n",
       "      <td>400</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Runner 2</td>\n",
       "      <td>400</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Runner 3</td>\n",
       "      <td>400</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Runner 1</td>\n",
       "      <td>800</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Runner 2</td>\n",
       "      <td>800</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Runner 3</td>\n",
       "      <td>800</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Runner 1</td>\n",
       "      <td>1200</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Runner 2</td>\n",
       "      <td>1200</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Runner 3</td>\n",
       "      <td>1200</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Runner 1</td>\n",
       "      <td>1500</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Runner 2</td>\n",
       "      <td>1500</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Runner 3</td>\n",
       "      <td>1500</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Runner distance  time\n",
       "0   Runner 1      400    64\n",
       "1   Runner 2      400    80\n",
       "2   Runner 3      400    96\n",
       "3   Runner 1      800   128\n",
       "4   Runner 2      800   160\n",
       "5   Runner 3      800   192\n",
       "6   Runner 1     1200   192\n",
       "7   Runner 2     1200   240\n",
       "8   Runner 3     1200   288\n",
       "9   Runner 1     1500   240\n",
       "10  Runner 2     1500   300\n",
       "11  Runner 3     1500   360"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4446929a-0815-4c0e-8dec-a3dbc180d040",
   "metadata": {},
   "outputs": [],
   "source": [
    "age = pd.DataFrame([\n",
    "    {\"Runner\": \"Runner 4\", \"Age\": 18},\n",
    "    {\"Runner\": \"Runner 2\", \"Age\": 21},\n",
    "    {\"Runner\": \"Runner 1\", \"Age\": 23},\n",
    "    {\"Runner\": \"Runner 3\", \"Age\": 19},\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2e4cc57c-5db0-4fe8-a90c-b123881fbdb5",
   "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>Runner</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Runner 4</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Runner 2</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Runner 1</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Runner 3</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Runner  Age\n",
       "0  Runner 4   18\n",
       "1  Runner 2   21\n",
       "2  Runner 1   23\n",
       "3  Runner 3   19"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "64b6fd38-daaa-4251-bb25-660ccf61f512",
   "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>Runner</th>\n",
       "      <th>distance</th>\n",
       "      <th>time</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Runner 1</td>\n",
       "      <td>400</td>\n",
       "      <td>64</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Runner 1</td>\n",
       "      <td>800</td>\n",
       "      <td>128</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Runner 1</td>\n",
       "      <td>1200</td>\n",
       "      <td>192</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Runner 1</td>\n",
       "      <td>1500</td>\n",
       "      <td>240</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Runner 2</td>\n",
       "      <td>400</td>\n",
       "      <td>80</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Runner 2</td>\n",
       "      <td>800</td>\n",
       "      <td>160</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Runner 2</td>\n",
       "      <td>1200</td>\n",
       "      <td>240</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Runner 2</td>\n",
       "      <td>1500</td>\n",
       "      <td>300</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Runner 3</td>\n",
       "      <td>400</td>\n",
       "      <td>96</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Runner 3</td>\n",
       "      <td>800</td>\n",
       "      <td>192</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Runner 3</td>\n",
       "      <td>1200</td>\n",
       "      <td>288</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Runner 3</td>\n",
       "      <td>1500</td>\n",
       "      <td>360</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Runner distance  time  Age\n",
       "0   Runner 1      400    64   23\n",
       "1   Runner 1      800   128   23\n",
       "2   Runner 1     1200   192   23\n",
       "3   Runner 1     1500   240   23\n",
       "4   Runner 2      400    80   21\n",
       "5   Runner 2      800   160   21\n",
       "6   Runner 2     1200   240   21\n",
       "7   Runner 2     1500   300   21\n",
       "8   Runner 3      400    96   19\n",
       "9   Runner 3      800   192   19\n",
       "10  Runner 3     1200   288   19\n",
       "11  Runner 3     1500   360   19"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add the age for each runner\n",
    "runners.merge(age, on=\"Runner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8eee28ee-3fa2-458d-a1b9-59670fea1ae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic[\"Child\"] = titanic[\"Age\"] < 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f9f0a7a7-c97b-45dc-9fe1-bb164c23df62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                False\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    False\n",
       "Heikkinen, Miss. Laina                                 False\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)           False\n",
       "Allen, Mr. William Henry                               False\n",
       "                                                       ...  \n",
       "Montvila, Rev. Juozas                                  False\n",
       "Graham, Miss. Margaret Edith                           False\n",
       "Johnston, Miss. Catherine Helen \"Carrie\"               False\n",
       "Behr, Mr. Karl Howell                                  False\n",
       "Dooley, Mr. Patrick                                    False\n",
       "Name: Child, Length: 891, dtype: bool"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"Child\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9b9dd444-16c0-4ccc-aad5-fe8a900fd079",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Child\n",
       "female  False    0.758865\n",
       "        True     0.593750\n",
       "male    False    0.164510\n",
       "        True     0.555556\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby([\"Sex\", \"Child\"])[\"Survived\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b63dd593-b3f0-4ce4-9048-05d142e4f17e",
   "metadata": {},
   "source": [
    "## Exercise 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2a8d82ba-fc15-4854-b6e0-463e4bbab6bd",
   "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>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",
       "      <th>is_passenger</th>\n",
       "      <th>Child</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Braund, Mr. Owen Harris</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heikkinen, Miss. Laina</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Futrelle, Mrs. Jacques Heath (Lily May Peel)</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allen, Mr. William Henry</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montvila, Rev. Juozas</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graham, Miss. Margaret Edith</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnston, Miss. Catherine Helen \"Carrie\"</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Behr, Mr. Karl Howell</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dooley, Mr. Patrick</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    PassengerId  Survived  \\\n",
       "Name                                                                        \n",
       "Braund, Mr. Owen Harris                                       1         0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...            2         1   \n",
       "Heikkinen, Miss. Laina                                        3         1   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  4         1   \n",
       "Allen, Mr. William Henry                                      5         0   \n",
       "...                                                         ...       ...   \n",
       "Montvila, Rev. Juozas                                       887         0   \n",
       "Graham, Miss. Margaret Edith                                888         1   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                    889         0   \n",
       "Behr, Mr. Karl Howell                                       890         1   \n",
       "Dooley, Mr. Patrick                                         891         0   \n",
       "\n",
       "                                                    Pclass     Sex   Age  \\\n",
       "Name                                                                       \n",
       "Braund, Mr. Owen Harris                                  3    male  22.0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...       1  female  38.0   \n",
       "Heikkinen, Miss. Laina                                   3  female  26.0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)             1  female  35.0   \n",
       "Allen, Mr. William Henry                                 3    male  35.0   \n",
       "...                                                    ...     ...   ...   \n",
       "Montvila, Rev. Juozas                                    2    male  27.0   \n",
       "Graham, Miss. Margaret Edith                             1  female  19.0   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                 3  female   NaN   \n",
       "Behr, Mr. Karl Howell                                    1    male  26.0   \n",
       "Dooley, Mr. Patrick                                      3    male  32.0   \n",
       "\n",
       "                                                    SibSp  Parch  \\\n",
       "Name                                                               \n",
       "Braund, Mr. Owen Harris                                 1      0   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...      1      0   \n",
       "Heikkinen, Miss. Laina                                  0      0   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)            1      0   \n",
       "Allen, Mr. William Henry                                0      0   \n",
       "...                                                   ...    ...   \n",
       "Montvila, Rev. Juozas                                   0      0   \n",
       "Graham, Miss. Margaret Edith                            0      0   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                1      2   \n",
       "Behr, Mr. Karl Howell                                   0      0   \n",
       "Dooley, Mr. Patrick                                     0      0   \n",
       "\n",
       "                                                              Ticket     Fare  \\\n",
       "Name                                                                            \n",
       "Braund, Mr. Owen Harris                                    A/5 21171   7.2500   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...          PC 17599  71.2833   \n",
       "Heikkinen, Miss. Laina                              STON/O2. 3101282   7.9250   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                  113803  53.1000   \n",
       "Allen, Mr. William Henry                                      373450   8.0500   \n",
       "...                                                              ...      ...   \n",
       "Montvila, Rev. Juozas                                         211536  13.0000   \n",
       "Graham, Miss. Margaret Edith                                  112053  30.0000   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                  W./C. 6607  23.4500   \n",
       "Behr, Mr. Karl Howell                                         111369  30.0000   \n",
       "Dooley, Mr. Patrick                                           370376   7.7500   \n",
       "\n",
       "                                                   Cabin Embarked  \\\n",
       "Name                                                                \n",
       "Braund, Mr. Owen Harris                              NaN        S   \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...   C85        C   \n",
       "Heikkinen, Miss. Laina                               NaN        S   \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)        C123        S   \n",
       "Allen, Mr. William Henry                             NaN        S   \n",
       "...                                                  ...      ...   \n",
       "Montvila, Rev. Juozas                                NaN        S   \n",
       "Graham, Miss. Margaret Edith                         B42        S   \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"             NaN        S   \n",
       "Behr, Mr. Karl Howell                               C148        C   \n",
       "Dooley, Mr. Patrick                                  NaN        Q   \n",
       "\n",
       "                                                    is_passenger  Child  \n",
       "Name                                                                     \n",
       "Braund, Mr. Owen Harris                                     True  False  \n",
       "Cumings, Mrs. John Bradley (Florence Briggs Tha...          True  False  \n",
       "Heikkinen, Miss. Laina                                      True  False  \n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)                True  False  \n",
       "Allen, Mr. William Henry                                    True  False  \n",
       "...                                                          ...    ...  \n",
       "Montvila, Rev. Juozas                                       True  False  \n",
       "Graham, Miss. Margaret Edith                                True  False  \n",
       "Johnston, Miss. Catherine Helen \"Carrie\"                    True  False  \n",
       "Behr, Mr. Karl Howell                                       True  False  \n",
       "Dooley, Mr. Patrick                                         True  False  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e2abb58c-ad75-4bf5-9b71-7fd100309427",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                1\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    1\n",
       "Heikkinen, Miss. Laina                                 0\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)           1\n",
       "Allen, Mr. William Henry                               0\n",
       "                                                      ..\n",
       "Montvila, Rev. Juozas                                  0\n",
       "Graham, Miss. Margaret Edith                           0\n",
       "Johnston, Miss. Catherine Helen \"Carrie\"               1\n",
       "Behr, Mr. Karl Howell                                  0\n",
       "Dooley, Mr. Patrick                                    0\n",
       "Name: SibSp, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"SibSp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "75cb7865-c6ee-4627-bd93-a48d6f0ec49b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 3, 4, 2, 5, 8], dtype=int64)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of person in each family\n",
    "titanic[\"SibSp\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a995171d-1736-4598-8a80-d0d7fbb04a96",
   "metadata": {},
   "outputs": [],
   "source": [
    "biggest = max(titanic[\"SibSp\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f42dcfa6-ef89-46a8-9b9f-4c14a8b3c8e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sage, Master. Thomas Henry', 'Sage, Miss. Constance Gladys',\n",
       "       'Sage, Mr. Frederick', 'Sage, Mr. George John Jr',\n",
       "       'Sage, Miss. Stella Anna', 'Sage, Mr. Douglas Bullen',\n",
       "       'Sage, Miss. Dorothy Edith \"Dolly\"'],\n",
       "      dtype='object', name='Name')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What are the names of the people in the largest family group?, Index is the name in this instance\n",
    "titanic[titanic[\"SibSp\"] == biggest].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0676b75f-249c-4902-b01e-b27f17f03e0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "Braund, Mr. Owen Harris                                 7.2500\n",
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)    71.2833\n",
       "Heikkinen, Miss. Laina                                  7.9250\n",
       "Futrelle, Mrs. Jacques Heath (Lily May Peel)           53.1000\n",
       "Allen, Mr. William Henry                                8.0500\n",
       "                                                        ...   \n",
       "Montvila, Rev. Juozas                                  13.0000\n",
       "Graham, Miss. Margaret Edith                           30.0000\n",
       "Johnston, Miss. Catherine Helen \"Carrie\"               23.4500\n",
       "Behr, Mr. Karl Howell                                  30.0000\n",
       "Dooley, Mr. Patrick                                     7.7500\n",
       "Name: Fare, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"Fare\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "24d5dcbd-96c8-4775-ae15-80eefe9bc33b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.25"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"Fare\"].loc[\"Braund, Mr. Owen Harris\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b29ac1f6-01b8-4fba-b9ff-a0fe32b6d380",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot: title={'center': 'Poor'}>,\n",
       "       <AxesSubplot: title={'center': 'Rich'}>], dtype=object)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAME6jAs7ChQsVEBCgqVOnurdVVVUpLS1N0dHRat++vVJTU1VaWuqxX1FRkVJSUtS2bVvFxMRo+vTpOnPmTGNKAQAAcLvogLN371794Q9/UK9evTy2T5s2Te+8845effVV7dixQyUlJRo9erS7vba2VikpKaqpqdGuXbu0bt06rV27VrNnz774UQAAAJzlogLOyZMnNXbsWD3//PPq2LGje3tFRYVWrVqlxYsXa+jQoerTp4/WrFmjXbt2KT8/X5L0/vvv6/Dhw/rTn/6k3r17a/jw4Zo/f76ys7NVU1Pjm1EBAIBW7aICTlpamlJSUpSUlOSxvaCgQKdPn/bY3r17d8XFxSkvL0+SlJeXp549e8put7v7JCcnq7KyUocOHWrw9aqrq1VZWemxAAAAnE+wtzts2LBB+/bt0969e89pc7lcCgkJUYcOHTy22+12uVwud5+zw019e31bQ7KysjRv3jxvSwUAAK2UV2dwiouL9dBDD+nFF19UaGhoU9V0joyMDFVUVLiX4uLiZnttAADQ8ngVcAoKClRWVqYbb7xRwcHBCg4O1o4dO7Rs2TIFBwfLbrerpqZG5eXlHvuVlpbK4XBIkhwOxzl3VdWv1/f5IZvNpoiICI8FAADgfLwKOLfffrsOHDig/fv3u5e+fftq7Nix7n+3adNGubm57n0KCwtVVFQkp9MpSXI6nTpw4IDKysrcfbZt26aIiAglJib6aFgAAKA18+oanPDwcF133XUe29q1a6fo6Gj39vHjxys9PV1RUVGKiIjQlClT5HQ6NWDAAEnSsGHDlJiYqHvuuUeLFi2Sy+XSzJkzlZaWJpvN5qNhAQCA1szri4x/zJIlSxQYGKjU1FRVV1crOTlZK1ascLcHBQVp06ZNmjRpkpxOp9q1a6dx48YpMzPT16UAAIBWqtEB58MPP/RYDw0NVXZ2trKzs8+7T3x8vLZs2dLYlwYAAGgQz6ICAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgDjfPnll7r77rsVHR2tsLAw9ezZUx9//LG73bIszZ49W507d1ZYWJiSkpJ09OhRP1YMwNcIOACM8u233+qWW25RmzZt9O677+rw4cP693//d3Xs2NHdZ9GiRVq2bJlWrlyp3bt3q127dkpOTlZVVZUfKwfgS8H+LgAAfOmpp55SbGys1qxZ496WkJDg/rdlWVq6dKlmzpypkSNHSpJeeOEF2e12vfnmmxozZkyz1wzA9ziDA8Aob7/9tvr27atf/vKXiomJ0Q033KDnn3/e3X7s2DG5XC4lJSW5t0VGRqp///7Ky8tr8JjV1dWqrKz0WABc2gg4AIzy+eefKycnR1dddZXee+89TZo0Sb///e+1bt06SZLL5ZIk2e12j/3sdru77YeysrIUGRnpXmJjY5t2EAAajYADwCh1dXW68cYbtWDBAt1www2aOHGiJkyYoJUrV170MTMyMlRRUeFeiouLfVgxgKZAwAFglM6dOysxMdFjW48ePVRUVCRJcjgckqTS0lKPPqWlpe62H7LZbIqIiPBYAFzaCDgAjHLLLbeosLDQY9snn3yi+Ph4Sd9fcOxwOJSbm+tur6ys1O7du+V0Opu1VgBNh7uoABhl2rRpuvnmm7VgwQL96le/0p49e/Tcc8/pueeekyQFBARo6tSpeuKJJ3TVVVcpISFBs2bNUpcuXTRq1Cj/Fg/AZwg4AIzSr18/bdy4URkZGcrMzFRCQoKWLl2qsWPHuvs8+uijOnXqlCZOnKjy8nINHDhQW7duVWhoqB8rB+BLBBwAxrnzzjt15513nrc9ICBAmZmZyszMbMaqADQnrsEBAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIzjVcDJyclRr1693A+bczqdevfdd93tVVVVSktLU3R0tNq3b6/U1NRzHmhXVFSklJQUtW3bVjExMZo+fbrOnDnjm9EAAADIy4Bz+eWXa+HChSooKNDHH3+soUOHauTIkTp06JCk758B88477+jVV1/Vjh07VFJSotGjR7v3r62tVUpKimpqarRr1y6tW7dOa9eu1ezZs307KgAA0Kp59aiGESNGeKw/+eSTysnJUX5+vi6//HKtWrVK69ev19ChQyVJa9asUY8ePZSfn68BAwbo/fff1+HDh7V9+3bZ7Xb17t1b8+fP12OPPaa5c+cqJCSkwdetrq5WdXW1e72ystLbcQIAgFbkoq/Bqa2t1YYNG3Tq1Ck5nU4VFBTo9OnTSkpKcvfp3r274uLilJeXJ0nKy8tTz549Zbfb3X2Sk5NVWVnpPgvUkKysLEVGRrqX2NjYiy0bAAC0Al4HnAMHDqh9+/ay2Wx68MEHtXHjRiUmJsrlcikkJEQdOnTw6G+32+VyuSRJLpfLI9zUt9e3nU9GRoYqKircS3FxsbdlAwCAVsTrp4lfc8012r9/vyoqKvTaa69p3Lhx2rFjR1PU5maz2WSz2Zr0NQAAgDm8DjghISG68sorJUl9+vTR3r179cwzz+jXv/61ampqVF5e7nEWp7S0VA6HQ5LkcDi0Z88ej+PV32VV3wcAAKCxGv13cOrq6lRdXa0+ffqoTZs2ys3NdbcVFhaqqKhITqdTkuR0OnXgwAGVlZW5+2zbtk0RERFKTExsbCkAAACSvDyDk5GRoeHDhysuLk4nTpzQ+vXr9eGHH+q9995TZGSkxo8fr/T0dEVFRSkiIkJTpkyR0+nUgAEDJEnDhg1TYmKi7rnnHi1atEgul0szZ85UWloaX0EBAACf8SrglJWV6d5779Xf/vY3RUZGqlevXnrvvff005/+VJK0ZMkSBQYGKjU1VdXV1UpOTtaKFSvc+wcFBWnTpk2aNGmSnE6n2rVrp3HjxikzM9O3owIAAK2aVwFn1apV/7Q9NDRU2dnZys7OPm+f+Ph4bdmyxZuXBQAA8ArPogIAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAjLZw4UIFBARo6tSp7m1VVVVKS0tTdHS02rdvr9TUVJWWlvqvSAA+R8ABYKy9e/fqD3/4g3r16uWxfdq0aXrnnXf06quvaseOHSopKdHo0aP9VCWApkDAAWCkkydPauzYsXr++efVsWNH9/aKigqtWrVKixcv1tChQ9WnTx+tWbNGu3btUn5+vh8rBuBLBBwARkpLS1NKSoqSkpI8thcUFOj06dMe27t37664uDjl5eU1eKzq6mpVVlZ6LAAubcH+LgAAfG3Dhg3at2+f9u7de06by+VSSEiIOnTo4LHdbrfL5XI1eLysrCzNmzevKUoF0EQ4gwPAKMXFxXrooYf04osvKjQ01CfHzMjIUEVFhXspLi72yXEBNB0CDgCjFBQUqKysTDfeeKOCg4MVHBysHTt2aNmyZQoODpbdbldNTY3Ky8s99istLZXD4WjwmDabTRERER4LgEsbX1EBMMrtt9+uAwcOeGy7//771b17dz322GOKjY1VmzZtlJubq9TUVElSYWGhioqK5HQ6/VEygCZAwAFglPDwcF133XUe29q1a6fo6Gj39vHjxys9PV1RUVGKiIjQlClT5HQ6NWDAAH+UDKAJEHAAtDpLlixRYGCgUlNTVV1dreTkZK1YscLfZQHwIQIOAON9+OGHHuuhoaHKzs5Wdna2fwoC0OS4yBgAABjHq4CTlZWlfv36KTw8XDExMRo1apQKCws9+lzIM16KioqUkpKitm3bKiYmRtOnT9eZM2caPxoAAAB5GXB27NihtLQ05efna9u2bTp9+rSGDRumU6dOufv82DNeamtrlZKSopqaGu3atUvr1q3T2rVrNXv2bN+NCgAAtGpeXYOzdetWj/W1a9cqJiZGBQUFGjRokPsZL+vXr9fQoUMlSWvWrFGPHj2Un5+vAQMG6P3339fhw4e1fft22e129e7dW/Pnz9djjz2muXPnKiQkxHejAwAArVKjrsGpqKiQJEVFRUm6sGe85OXlqWfPnrLb7e4+ycnJqqys1KFDhxp8HZ4DAwAAvHHRAaeurk5Tp07VLbfc4v7bEhfyjBeXy+URburb69sakpWVpcjISPcSGxt7sWUDAIBW4KIDTlpamg4ePKgNGzb4sp4G8RwYAADgjYv6OziTJ0/Wpk2btHPnTl1++eXu7Q6Hw/2Ml7PP4pz9jBeHw6E9e/Z4HK/+Lqt/9hwYm812MaUCAIBWyKszOJZlafLkydq4caM++OADJSQkeLT36dPH/YyXej98xovT6dSBAwdUVlbm7rNt2zZFREQoMTGxMWMBAACQ5OUZnLS0NK1fv15vvfWWwsPD3dfMREZGKiwsTJGRkT/6jJdhw4YpMTFR99xzjxYtWiSXy6WZM2cqLS2NszQAAMAnvAo4OTk5kqQhQ4Z4bF+zZo3uu+8+ST/+jJegoCBt2rRJkyZNktPpVLt27TRu3DhlZmY2biQAAAD/x6uAY1nWj/a5kGe8xMfHa8uWLd68NAAAwAXjWVQAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYJ9jfBUDqOmOzT45zfGGKT44DAEBLxxkcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAMEpWVpb69eun8PBwxcTEaNSoUSosLPToU1VVpbS0NEVHR6t9+/ZKTU1VaWmpnyoG0BQIOACMsmPHDqWlpSk/P1/btm3T6dOnNWzYMJ06dcrdZ9q0aXrnnXf06quvaseOHSopKdHo0aP9WDUAX+Pv4AAwytatWz3W165dq5iYGBUUFGjQoEGqqKjQqlWrtH79eg0dOlSStGbNGvXo0UP5+fkaMGDAOcesrq5WdXW1e72ysrJpBwGg0TiDA8BoFRUVkqSoqChJUkFBgU6fPq2kpCR3n+7duysuLk55eXkNHiMrK0uRkZHuJTY2tukLB9AoBBwAxqqrq9PUqVN1yy236LrrrpMkuVwuhYSEqEOHDh597Xa7XC5Xg8fJyMhQRUWFeykuLm7q0gE0El9RATBWWlqaDh48qI8++qhRx7HZbLLZbD6qCkBz8PoMzs6dOzVixAh16dJFAQEBevPNNz3aLcvS7Nmz1blzZ4WFhSkpKUlHjx716PPNN99o7NixioiIUIcOHTR+/HidPHmyUQMBgLNNnjxZmzZt0n/+53/q8ssvd293OByqqalReXm5R//S0lI5HI5mrhJAU/E64Jw6dUrXX3+9srOzG2xftGiRli1bppUrV2r37t1q166dkpOTVVVV5e4zduxYHTp0SNu2bdOmTZu0c+dOTZw48eJHAQD/x7IsTZ48WRs3btQHH3yghIQEj/Y+ffqoTZs2ys3NdW8rLCxUUVGRnE5nc5cLoIl4/RXV8OHDNXz48AbbLMvS0qVLNXPmTI0cOVKS9MILL8hut+vNN9/UmDFjdOTIEW3dulV79+5V3759JUnPPvus7rjjDj399NPq0qVLI4YDoLVLS0vT+vXr9dZbbyk8PNx9XU1kZKTCwsIUGRmp8ePHKz09XVFRUYqIiNCUKVPkdDobvIMKQMvk04uMjx07JpfL5XF3QmRkpPr37+++OyEvL08dOnRwhxtJSkpKUmBgoHbv3t3gcaurq1VZWemxAEBDcnJyVFFRoSFDhqhz587u5eWXX3b3WbJkie68806lpqZq0KBBcjgceuONN/xYNQBf8+lFxvW/Kdntdo/tZ9+d4HK5FBMT41lEcLCioqLOewdDVlaW5s2b58tSARjKsqwf7RMaGqrs7OzzftUOoOVrEbeJc4smAADwhk8DTv0dCD98psvZdyc4HA6VlZV5tJ85c0bffPPNee9gsNlsioiI8FgAAADOx6cBJyEhQQ6Hw+PuhMrKSu3evdt9d4LT6VR5ebkKCgrcfT744APV1dWpf//+viwHAAC0Ul5fg3Py5El9+umn7vVjx45p//79ioqKUlxcnKZOnaonnnhCV111lRISEjRr1ix16dJFo0aNkiT16NFDP/vZzzRhwgStXLlSp0+f1uTJkzVmzBjuoAIAAD7hdcD5+OOPddttt7nX09PTJUnjxo3T2rVr9eijj+rUqVOaOHGiysvLNXDgQG3dulWhoaHufV588UVNnjxZt99+uwIDA5Wamqply5b5YDgAAAAXEXCGDBnyT+9SCAgIUGZmpjIzM8/bJyoqSuvXr/f2pQEAAC5Ii7iLCgAAwBsEHAAAYBwCDgAAMA4BBwAAGIeAAwAAjEPAAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxgv1dAADAv7rO2OyzYx1fmOKzYwGNwRkcAABgHAIOAAAwDgEHAAAYh4ADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGAcAg4AADAOAQcAABiHgAMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYJxgfxcAAABarq4zNvvkOMcXpvjkOPU4gwMAAIxDwAEAAMYh4AAAAOMQcAAAgHEIOAAAwDgEHAAAYBxuEwcAtEq+vr3ZV8c7+5i4eJzBAQAAxiHgAAAA4xBwAACAcQg4AADAOAQcAABgHAIOAAAwDreJAwB87lJ9wjRaDwIO/IK/FwEAaEp8RQUAAIzDGRzgPFrCWaaWUCMA+ANncAAAgHEIOAAAwDgEHAAAYBwCDgAAMI7fAk52dra6du2q0NBQ9e/fX3v27PFXKQBaKeYhwFx+CTgvv/yy0tPTNWfOHO3bt0/XX3+9kpOTVVZW5o9yALRCzEOA2fxym/jixYs1YcIE3X///ZKklStXavPmzVq9erVmzJhxTv/q6mpVV1e71ysqKiRJlZWVP/paddXf+ajqC3u9i+GrGpuqvqbQmn4ukjk11vexLMtnr+sv3sxDjZmDJN9/xi/V4zXFMZtyXmsJ/z+2BM35s/ZqDrKaWXV1tRUUFGRt3LjRY/u9995r/fznP29wnzlz5liSWFhYLpGluLi4GWaLpuPtPMQcxMJyaS0XMgc1+xmcr7/+WrW1tbLb7R7b7Xa7/vrXvza4T0ZGhtLT093rdXV1+uabbxQdHa2AgIAG96msrFRsbKyKi4sVERHhuwH4AWO5dJk0ngsZi2VZOnHihLp06dLM1fmWt/PQxcxBUut7f7QUjOXS5Os5qEX8JWObzSabzeaxrUOHDhe0b0RERIv/oddjLJcuk8bzY2OJjIxsxmouDY2Zg6TW9f5oSRjLpclXc1CzX2R82WWXKSgoSKWlpR7bS0tL5XA4mrscAK0Q8xBgvmYPOCEhIerTp49yc3Pd2+rq6pSbmyun09nc5QBohZiHAPP55Suq9PR0jRs3Tn379tVNN92kpUuX6tSpU+67GXzBZrNpzpw555xWbokYy6XLpPGYNJYLwTzkHcZyaWIs5xdgWf6533P58uX6t3/7N7lcLvXu3VvLli1T//79/VEKgFaKeQgwl98CDgAAQFPhWVQAAMA4BBwAAGAcAg4AADAOAQcAABiHgANAkox4gCZwIXivtw7G3EX19ddfa/Xq1crLy5PL5ZIkORwO3XzzzbrvvvvUqVMnP1cIXNpCQkL0l7/8RT169PB3KS0Sc1DLwXu9dTAi4Ozdu1fJyclq27atkpKS3A/QKy0tVW5urr777ju999576tu3r58r9Y3i4mLNmTNHq1ev9ncpP+of//iHCgoKFBUVpcTERI+2qqoqvfLKK7r33nv9VJ33jhw5ovz8fDmdTnXv3l1//etf9cwzz6i6ulp33323hg4d6u8Sf9TZD4082zPPPKO7775b0dHRkqTFixc3Z1ktmolzkAmfXdPe6/v27VPHjh2VkJAgSfrjH/+olStXqqioSPHx8Zo8ebLGjBnj5yov3PLly7Vnzx7dcccdGjNmjP74xz8qKytLdXV1Gj16tDIzMxUc3Ii/R/yjzxtvAfr3729NnDjRqqurO6etrq7OmjhxojVgwAA/VNY09u/fbwUGBvq7jB9VWFhoxcfHWwEBAVZgYKA1aNAgq6SkxN3ucrlaxDjqvfvuu1ZISIgVFRVlhYaGWu+++67VqVMnKykpyRo6dKgVFBRk5ebm+rvMHxUQEGD17t3bGjJkiMcSEBBg9evXzxoyZIh12223+bvMFsW0OciUz65p7/VevXpZ27ZtsyzLsp5//nkrLCzM+v3vf2/l5ORYU6dOtdq3b2+tWrXKz1VemPnz51vh4eFWamqq5XA4rIULF1rR0dHWE088YS1YsMDq1KmTNXv27Ea9hhEBJzQ01Dpy5Mh5248cOWKFhoY2Y0WN89Zbb/3TZcmSJS1ichk1apSVkpJiffXVV9bRo0etlJQUKyEhwfriiy8sy2o5k2Q9p9Np/eu//qtlWZb10ksvWR07drQef/xxd/uMGTOsn/70p/4q74JlZWVZCQkJ54Sx4OBg69ChQ36qqmUzbQ4y5bNr2ns9LCzMOn78uGVZlnXDDTdYzz33nEf7iy++aCUmJvqjNK9169bNev311y3L+v6X9qCgIOtPf/qTu/2NN96wrrzyyka9hhEBp2vXrta6devO275u3TorPj6++QpqpPrfmgICAs67tITJJSYmxvrv//5v93pdXZ314IMPWnFxcdZnn33WYibJehEREdbRo0cty7Ks2tpaKzg42Nq3b5+7/cCBA5bdbvdXeV7Zs2ePdfXVV1sPP/ywVVNTY1lWy530LwWmzUEmfXZNeq9HR0dbH3/8sWVZ3/+M9u/f79H+6aefWmFhYf4ozWthYWHuwGxZltWmTRvr4MGD7vXjx49bbdu2bdRrGHEX1SOPPKKJEyfqoYce0ttvv63du3dr9+7devvtt/XQQw/pwQcf1KOPPurvMi9Y586d9cYbb6iurq7BZd++ff4u8YL84x//8Pj+NCAgQDk5ORoxYoQGDx6sTz75xI/VXZyAgABJUmBgoEJDQxUZGeluCw8PV0VFhb9K80q/fv1UUFCgr776Sn379tXBgwfdY4P3TJuDTPrsmvReHz58uHJyciRJgwcP1muvvebR/sorr+jKK6/0R2leczgcOnz4sCTp6NGjqq2tda9L0qFDhxQTE9O4F2lUPLqEbNiwwerfv78VHBzsPssRHBxs9e/f33r55Zf9XZ5XRowYYc2aNeu87fv377cCAgKasaKL069fP+uFF15osC0tLc3q0KFDi/kt0LK+//773Xffda8fOHDAOn36tHt9586dVkJCgj9Ka5SXXnrJstvtVmBgYIv8rfZSYdIcZNpnt15Lf69/+eWXVteuXa1BgwZZ6enpVlhYmDVw4EBrwoQJ1qBBg6yQkBBr8+bN/i7zgsycOdPq1KmT9cADD1gJCQnWjBkzrLi4OCsnJ8dauXKlFRsba02bNq1Rr2FMwKlXU1NjlZSUWCUlJe7TkS3Nzp07Pf5D+kMnT560Pvzww2as6OIsWLDAGj58+HnbJ02a1CKCWr2cnBxr06ZN523PyMiwxo8f34wV+U5xcbH15ptvWidPnvR3KS2eCXOQaZ/ds7X09/q3335rPfbYY1ZiYqIVGhpqhYSEWPHx8dZvfvMba+/evf4u74LV1tZaTz75pHXnnXdaCxYssOrq6qyXXnrJio2NtaKjo6377ruv0T8jI24TBwAAOJsR1+AAAACcjYADAACMQ8ABAADGIeAAAADjEHAAAIBxCDgAAMA4BBwAAGCc/wWKbGRH0NvShQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x itterates over all names in SibSp, where the function checks if this name belongs in Poor/Rich\n",
    "titanic.hist(\"SibSp\",\n",
    "             lambda x: \"Poor\" if titanic[\"Fare\"].loc[x] < titanic[\"Fare\"].mean() else \"Rich\",\n",
    "             rwidth=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4535a89-7c87-4dd6-b473-bd16f40940fd",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Time series superpowers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "bc9b73f8-7ef8-4315-a439-fec6a1a9d1c3",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'laureate.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [77], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# File → Open from URL → enter https://api.nobelprize.org/v1/laureate.csv\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m# This opens it in JupyterLab but also saves it as laureate.csv\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m nobel \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlaureate.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      4\u001b[0m nobel\u001b[38;5;241m.\u001b[39mhead()\n",
      "File \u001b[1;32m~\\Documents\\Projects\\Python course HT2022\\venv\\lib\\site-packages\\pandas\\util\\_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    209\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[1;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\Documents\\Projects\\Python course HT2022\\venv\\lib\\site-packages\\pandas\\util\\_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[0;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[0;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    330\u001b[0m     )\n\u001b[1;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\Documents\\Projects\\Python course HT2022\\venv\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    936\u001b[0m     dialect,\n\u001b[0;32m    937\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    946\u001b[0m     defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[0;32m    947\u001b[0m )\n\u001b[0;32m    948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Documents\\Projects\\Python course HT2022\\venv\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    602\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m    608\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "File \u001b[1;32m~\\Documents\\Projects\\Python course HT2022\\venv\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1439\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m   1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\Documents\\Projects\\Python course HT2022\\venv\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1733\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m   1734\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1735\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1736\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1737\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1738\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1739\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1740\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1741\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1742\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1743\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1744\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1745\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1746\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
      "File \u001b[1;32m~\\Documents\\Projects\\Python course HT2022\\venv\\lib\\site-packages\\pandas\\io\\common.py:856\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    851\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    852\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    853\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    854\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m    855\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 856\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m    857\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    858\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    859\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    860\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    861\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    862\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    863\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    864\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m    865\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'laureate.csv'"
     ]
    }
   ],
   "source": [
    "# File → Open from URL → enter https://api.nobelprize.org/v1/laureate.csv\n",
    "# This opens it in JupyterLab but also saves it as laureate.csv\n",
    "nobel = pd.read_csv(\"laureate.csv\")\n",
    "nobel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4dd4e41-bcc4-4e14-8416-ff151f3f2184",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the errors='coerce' argument is needed because the dataset is a bit messy\n",
    "nobel[\"born\"] = pd.to_datetime(nobel[\"born\"], errors ='coerce')\n",
    "nobel[\"died\"] = pd.to_datetime(nobel[\"died\"], errors ='coerce')\n",
    "nobel[\"year\"] = pd.to_datetime(nobel[\"year\"], format=\"%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e67163df-7585-4fb0-8f71-bad25c0a67b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(nobel[\"born\"].dt.day)\n",
    "print(nobel[\"born\"].dt.year)\n",
    "print(nobel[\"born\"].dt.weekday)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d708899c-802c-4246-9e97-05cdfe0c0d80",
   "metadata": {},
   "outputs": [],
   "source": [
    "nobel[\"lifespan\"] = round((nobel[\"died\"] - nobel[\"born\"]).dt.days / 365, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78622ef2-b8a0-40a1-83bc-dadb984fec9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "nobel.hist(column='lifespan', bins=25, figsize=(8,10), rwidth=0.9)"
   ]
  },
  {
   "cell_type": "code",
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    "nobel.boxplot(column=\"lifespan\", by=\"category\")"
   ]
  }
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