answer_final_exercise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "enormous-amber",
   "metadata": {},
   "source": [
    "# Example answers for final exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "interpreted-handle",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>class</th>\n",
       "      <th>embarked</th>\n",
       "      <th>country</th>\n",
       "      <th>ticketno</th>\n",
       "      <th>fare</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbing, Mr. Anthony</td>\n",
       "      <td>male</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3rd</td>\n",
       "      <td>S</td>\n",
       "      <td>United States</td>\n",
       "      <td>5547.0</td>\n",
       "      <td>7.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbott, Mr. Eugene Joseph</td>\n",
       "      <td>male</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3rd</td>\n",
       "      <td>S</td>\n",
       "      <td>United States</td>\n",
       "      <td>2673.0</td>\n",
       "      <td>20.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbott, Mr. Rossmore Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3rd</td>\n",
       "      <td>S</td>\n",
       "      <td>United States</td>\n",
       "      <td>2673.0</td>\n",
       "      <td>20.05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abbott, Mrs. Rhoda Mary 'Rosa'</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>3rd</td>\n",
       "      <td>S</td>\n",
       "      <td>England</td>\n",
       "      <td>2673.0</td>\n",
       "      <td>20.05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abelseth, Miss. Karen Marie</td>\n",
       "      <td>female</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3rd</td>\n",
       "      <td>S</td>\n",
       "      <td>Norway</td>\n",
       "      <td>348125.0</td>\n",
       "      <td>7.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>yes</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2202</th>\n",
       "      <td>Wynn, Mr. Walter</td>\n",
       "      <td>male</td>\n",
       "      <td>41.0</td>\n",
       "      <td>deck crew</td>\n",
       "      <td>B</td>\n",
       "      <td>England</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2203</th>\n",
       "      <td>Yearsley, Mr. Harry</td>\n",
       "      <td>male</td>\n",
       "      <td>40.0</td>\n",
       "      <td>victualling crew</td>\n",
       "      <td>S</td>\n",
       "      <td>England</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>Young, Mr. Francis James</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>engineering crew</td>\n",
       "      <td>S</td>\n",
       "      <td>England</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2205</th>\n",
       "      <td>Zanetti, Sig. Minio</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>restaurant staff</td>\n",
       "      <td>S</td>\n",
       "      <td>England</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2206</th>\n",
       "      <td>Zarracchi, Sig. L.</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>restaurant staff</td>\n",
       "      <td>S</td>\n",
       "      <td>England</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2207 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                name  gender   age             class embarked  \\\n",
       "0                Abbing, Mr. Anthony    male  42.0               3rd        S   \n",
       "1          Abbott, Mr. Eugene Joseph    male  13.0               3rd        S   \n",
       "2        Abbott, Mr. Rossmore Edward    male  16.0               3rd        S   \n",
       "3     Abbott, Mrs. Rhoda Mary 'Rosa'  female  39.0               3rd        S   \n",
       "4        Abelseth, Miss. Karen Marie  female  16.0               3rd        S   \n",
       "...                              ...     ...   ...               ...      ...   \n",
       "2202                Wynn, Mr. Walter    male  41.0         deck crew        B   \n",
       "2203             Yearsley, Mr. Harry    male  40.0  victualling crew        S   \n",
       "2204        Young, Mr. Francis James    male  32.0  engineering crew        S   \n",
       "2205             Zanetti, Sig. Minio    male  20.0  restaurant staff        S   \n",
       "2206              Zarracchi, Sig. L.    male  26.0  restaurant staff        S   \n",
       "\n",
       "            country  ticketno   fare  sibsp  parch survived  \n",
       "0     United States    5547.0   7.11    0.0    0.0       no  \n",
       "1     United States    2673.0  20.05    0.0    2.0       no  \n",
       "2     United States    2673.0  20.05    1.0    1.0       no  \n",
       "3           England    2673.0  20.05    1.0    1.0      yes  \n",
       "4            Norway  348125.0   7.13    0.0    0.0      yes  \n",
       "...             ...       ...    ...    ...    ...      ...  \n",
       "2202        England       NaN    NaN    NaN    NaN      yes  \n",
       "2203        England       NaN    NaN    NaN    NaN      yes  \n",
       "2204        England       NaN    NaN    NaN    NaN       no  \n",
       "2205        England       NaN    NaN    NaN    NaN       no  \n",
       "2206        England       NaN    NaN    NaN    NaN       no  \n",
       "\n",
       "[2207 rows x 11 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "titanic = pd.read_csv(\"https://milliams.com/courses/data_analysis_python/titanic.csv\")\n",
    "titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abstract-legend",
   "metadata": {},
   "source": [
    "## Summarising\n",
    "\n",
    "### Find the average age of all people on board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "handy-gregory",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.436734693877504"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"age\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "perceived-effects",
   "metadata": {},
   "source": [
    "### Use a filter to select only the males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "convenient-horizontal",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_males = titanic[titanic[\"gender\"] == \"male\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cooked-soccer",
   "metadata": {},
   "source": [
    "### Find the average age of the males on board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "atomic-bikini",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.83231351981346"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_males[\"age\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "invalid-corruption",
   "metadata": {},
   "source": [
    "## Filtering\n",
    "\n",
    "### Select on the people in 3<sup>rd</sup> class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "accompanied-ideal",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>class</th>\n",
       "      <th>embarked</th>\n",
       "      <th>country</th>\n",
       "      <th>ticketno</th>\n",
       "      <th>fare</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbing, Mr. Anthony</td>\n",
       "      <td>male</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3rd</td>\n",
       "      <td>S</td>\n",
       "      <td>United States</td>\n",
       "      <td>5547.0</td>\n",
       "      <td>7.1100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbott, Mr. Eugene Joseph</td>\n",
       "      <td>male</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3rd</td>\n",
       "      <td>S</td>\n",
       "      <td>United States</td>\n",
       "      <td>2673.0</td>\n",
       "      <td>20.0500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbott, Mr. Rossmore Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3rd</td>\n",
       "      <td>S</td>\n",
       "      <td>United States</td>\n",
       "      <td>2673.0</td>\n",
       "      <td>20.0500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abbott, Mrs. Rhoda Mary 'Rosa'</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>3rd</td>\n",
       "      <td>S</td>\n",
       "      <td>England</td>\n",
       "      <td>2673.0</td>\n",
       "      <td>20.0500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abelseth, Miss. Karen Marie</td>\n",
       "      <td>female</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3rd</td>\n",
       "      <td>S</td>\n",
       "      <td>Norway</td>\n",
       "      <td>348125.0</td>\n",
       "      <td>7.1300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>yes</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1313</th>\n",
       "      <td>Yūsuf, Mrs. Kātrīn</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3rd</td>\n",
       "      <td>C</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>2668.0</td>\n",
       "      <td>22.0702</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>Zakarian, Mr. Mapriededer</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3rd</td>\n",
       "      <td>C</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>2656.0</td>\n",
       "      <td>7.0406</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>Zakarian, Mr. Ortin</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3rd</td>\n",
       "      <td>C</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>2670.0</td>\n",
       "      <td>7.0406</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>Zenni, Mr. Philip</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3rd</td>\n",
       "      <td>C</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>7.0406</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>Zimmermann, Mr. Leo</td>\n",
       "      <td>male</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3rd</td>\n",
       "      <td>S</td>\n",
       "      <td>Germany</td>\n",
       "      <td>315082.0</td>\n",
       "      <td>7.1706</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>709 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                name  gender   age class embarked  \\\n",
       "0                Abbing, Mr. Anthony    male  42.0   3rd        S   \n",
       "1          Abbott, Mr. Eugene Joseph    male  13.0   3rd        S   \n",
       "2        Abbott, Mr. Rossmore Edward    male  16.0   3rd        S   \n",
       "3     Abbott, Mrs. Rhoda Mary 'Rosa'  female  39.0   3rd        S   \n",
       "4        Abelseth, Miss. Karen Marie  female  16.0   3rd        S   \n",
       "...                              ...     ...   ...   ...      ...   \n",
       "1313              Yūsuf, Mrs. Kātrīn  female  23.0   3rd        C   \n",
       "1315       Zakarian, Mr. Mapriededer    male  22.0   3rd        C   \n",
       "1316             Zakarian, Mr. Ortin    male  27.0   3rd        C   \n",
       "1317               Zenni, Mr. Philip    male  25.0   3rd        C   \n",
       "1318             Zimmermann, Mr. Leo    male  29.0   3rd        S   \n",
       "\n",
       "            country  ticketno     fare  sibsp  parch survived  \n",
       "0     United States    5547.0   7.1100    0.0    0.0       no  \n",
       "1     United States    2673.0  20.0500    0.0    2.0       no  \n",
       "2     United States    2673.0  20.0500    1.0    1.0       no  \n",
       "3           England    2673.0  20.0500    1.0    1.0      yes  \n",
       "4            Norway  348125.0   7.1300    0.0    0.0      yes  \n",
       "...             ...       ...      ...    ...    ...      ...  \n",
       "1313        Lebanon    2668.0  22.0702    0.0    2.0      yes  \n",
       "1315         Turkey    2656.0   7.0406    0.0    0.0       no  \n",
       "1316         Turkey    2670.0   7.0406    0.0    0.0       no  \n",
       "1317        Lebanon    2620.0   7.0406    0.0    0.0      yes  \n",
       "1318        Germany  315082.0   7.1706    0.0    0.0       no  \n",
       "\n",
       "[709 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[titanic[\"class\"] == \"3rd\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "renewable-northeast",
   "metadata": {},
   "source": [
    "### Select just the passengers\n",
    "\n",
    "The technique shown in class was to combine together multiple selectors with `|`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "legislative-unemployment",
   "metadata": {},
   "outputs": [],
   "source": [
    "passengers = titanic[\n",
    "    (titanic[\"class\"] == \"1st\") | \n",
    "    (titanic[\"class\"] == \"2nd\") | \n",
    "    (titanic[\"class\"] == \"3rd\")\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adjusted-vacation",
   "metadata": {},
   "source": [
    "However, it is also possible to use the `isin` method to select from a list of matching options:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "experimental-fitting",
   "metadata": {},
   "outputs": [],
   "source": [
    "passengers = titanic[titanic[\"class\"].isin([\"1st\", \"2nd\", \"3rd\"])]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "trained-carroll",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "\n",
    "### Plot the distribution of ages for males and females\n",
    "\n",
    "Using `displot` with `age` as the main variable shows the distribution. YOu can overlay the two genders using `hue=\"gender\"`. To simplify the view, you can set `kind=\"kde\"`. Since KDE mode smooths the data, you can also set a cutoff of 0 to avoid it showing negative ages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "advisory-discharge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd9a8a5dfd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 598x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(\n",
    "    data=passengers,\n",
    "    x=\"age\",\n",
    "    hue=\"gender\",\n",
    "    kind=\"kde\",\n",
    "    cut=0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "broadband-telling",
   "metadata": {},
   "source": [
    "### How does this differ by class?\n",
    "\n",
    "All that has changed from the last plot is adding in the split by `class` over multiple columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "female-import",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd9a830b710>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1598x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(\n",
    "    data=passengers,\n",
    "    x=\"age\",\n",
    "    hue=\"gender\",\n",
    "    kind=\"kde\",\n",
    "    cut=0,\n",
    "    col=\"class\",\n",
    "    col_order=[\"1st\", \"2nd\", \"3rd\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "royal-fantasy",
   "metadata": {},
   "source": [
    "## Combining\n",
    "\n",
    "To reduce the duplication of effort here, I create a function which, given a set of data, calculated the survived fraction within. This is then called three times, once for each class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "going-carry",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6203703703703703 0.4154929577464789 0.2552891396332863\n"
     ]
    }
   ],
   "source": [
    "def survived_ratio(df):\n",
    "    yes = df[df[\"survived\"] == \"yes\"]\n",
    "    return len(yes) / len(df)\n",
    "\n",
    "ratio_1st = survived_ratio(passengers[passengers[\"class\"] == \"1st\"])\n",
    "ratio_2nd = survived_ratio(passengers[passengers[\"class\"] == \"2nd\"])\n",
    "ratio_3rd = survived_ratio(passengers[passengers[\"class\"] == \"3rd\"])\n",
    "\n",
    "print(ratio_1st, ratio_2nd, ratio_3rd)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}