{
 "cells": [
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   "source": [
    "# Week 3 Tuesday Discussion"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "## Quiz 2 Review"
   ]
  },
  {
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    "execution_millis": 3345,
    "execution_start": 1649822292686,
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   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "0      man        True  NaN  Southampton    no  False  \n",
       "1    woman       False    C    Cherbourg   yes  False  \n",
       "2    woman       False  NaN  Southampton   yes   True  \n",
       "3    woman       False    C  Southampton   yes  False  \n",
       "4      man        True  NaN  Southampton    no   True  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
       "890    man        True  NaN   Queenstown    no   True  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "df = sns.load_dataset(\"titanic\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
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    "cell_id": "7e6805dfbf5849d7bcfb09d5cc965c30",
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   },
   "source": [
    "__Question 1__: \n",
    "\n",
    "-  Write code to determine which columns of `df` contain missing values. Do the same to determine which rows of `df` contain missing values.  \n",
    "\n",
    "- Write code to determine which rows of `df` contain at least 2 missing values.\n",
    "\n",
    "- Create a sub-DataFrame of `df` which contains only rows which have at least one missing value.\n",
    "\n",
    "- Create a sub-DataFrame of `df` which contains only rows which have at least one missing value and `embark_town` listed as Southampton."
   ]
  },
  {
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   },
   "source": [
    "## Code-Along Solutions"
   ]
  },
  {
   "cell_type": "code",
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    "cell_id": "56a54eba3a244b859afa2c1c3906a10d",
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    "deepnote_cell_type": "code",
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     21.1875
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    "execution_millis": 8,
    "execution_start": 1649822296037,
    "source_hash": "98137b40",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age', 'embarked', 'deck', 'embark_town']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[c for c in df.columns if df[c].isna().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "cell_id": "f46d8da6fc5742c5a3d43531821cdc93",
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    "deepnote_cell_type": "code",
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     611
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 1039,
    "execution_start": 1649822296225,
    "is_code_hidden": false,
    "is_output_hidden": true,
    "source_hash": "c99efd9c",
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
    {
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       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>709 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "5           0       3    male   NaN      0      0   8.4583        Q   Third   \n",
       "7           0       3    male   2.0      3      1  21.0750        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "884         0       3    male  25.0      0      0   7.0500        S   Third   \n",
       "885         0       3  female  39.0      0      5  29.1250        Q   Third   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "0      man        True  NaN  Southampton    no  False  \n",
       "2    woman       False  NaN  Southampton   yes   True  \n",
       "4      man        True  NaN  Southampton    no   True  \n",
       "5      man        True  NaN   Queenstown    no   True  \n",
       "7    child       False  NaN  Southampton    no  False  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "884    man        True  NaN  Southampton    no   True  \n",
       "885  woman       False  NaN   Queenstown    no  False  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "890    man        True  NaN   Queenstown    no   True  \n",
       "\n",
       "[709 rows x 15 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[[r for r in df.index if df.loc[r].isna().any()]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "dab3b64be1324e728142fe9f8f1e9c3c",
    "deepnote_cell_height": 82,
    "deepnote_cell_type": "markdown",
    "owner_user_id": "e590e996-b52f-4e48-8e79-35525bbb42f6",
    "tags": []
   },
   "source": [
    "## Pre-Written Solutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "cell_id": "39520c12d9dd4bf79ddd26fe182f76df",
    "deepnote_cell_height": 136.1875,
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     21.1875
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 11,
    "execution_start": 1649822297676,
    "source_hash": "e79b2bd4",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age', 'embarked', 'deck', 'embark_town']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Missing columns\n",
    "[c for c in df.columns if df[c].isna().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "cell_id": "577f4136b1b14478b7158ae6d516a850",
    "deepnote_cell_height": 129.796875,
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 603,
    "execution_start": 1649822297914,
    "is_output_hidden": true,
    "source_hash": "3d32729d",
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
    {
     "data": {
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       " 886,\n",
       " 888,\n",
       " 890]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Missing rows\n",
    "[r for r in df.index if df.loc[r].isna().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "cell_id": "7412b55af3744e06b10941b13ae95f02",
    "deepnote_cell_height": 365.34375,
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     232.34375
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 1586,
    "execution_start": 1649822298519,
    "source_hash": "2802b08",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5      2\n",
       "17     2\n",
       "19     2\n",
       "26     2\n",
       "28     2\n",
       "      ..\n",
       "859    2\n",
       "863    2\n",
       "868    2\n",
       "878    2\n",
       "888    2\n",
       "Length: 160, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#At least 2 missing rows\n",
    "more_1 = [r for r in df.index if df.loc[r].isna().sum() > 1]\n",
    "df.loc[more_1].isna().sum(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "cell_id": "28196e6c23624fe18008c6ebdb801c70",
    "deepnote_cell_height": 618,
    "deepnote_cell_type": "code",
    "deepnote_table_loading": false,
    "deepnote_table_state": {
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       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
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       "5           0       3    male   NaN      0      0   8.4583        Q   Third   \n",
       "7           0       3    male   2.0      3      1  21.0750        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
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       "\n",
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       "..     ...         ...  ...          ...   ...    ...  \n",
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       "\n",
       "[709 rows x 15 columns]"
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      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "7           0       3    male   2.0      3      1  21.0750        S   Third   \n",
       "8           1       3  female  27.0      0      2  11.1333        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
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       "\n",
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       "4      man        True  NaN  Southampton    no   True  \n",
       "7    child       False  NaN  Southampton    no  False  \n",
       "8    woman       False  NaN  Southampton   yes  False  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "882  woman       False  NaN  Southampton    no   True  \n",
       "883    man        True  NaN  Southampton    no   True  \n",
       "884    man        True  NaN  Southampton    no   True  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "\n",
       "[529 rows x 15 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Missing + embark_town\n",
    "df.loc[[r for r in df.index if df.loc[r].isna().any() and df.loc[r,\"embark_town\"] == \"Southampton\"]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "97be3f244cfd421a9a065f5caa7fb645",
    "deepnote_cell_height": 125.1875,
    "deepnote_cell_type": "markdown",
    "tags": []
   },
   "source": [
    "__Question 2__:\n",
    " - Consider the following Altair chart. Explain how you could use it to determine where `parch` is the largest and how many different unique `embark_town` values there are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "cell_id": "138a87757c3747d9a948f58ff0b7e096",
    "deepnote_cell_height": 561,
    "deepnote_cell_type": "code",
    "deepnote_output_heights": [
     356
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 693,
    "execution_start": 1649822302014,
    "source_hash": "f0c30795",
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\"age\": 5.0, \"sibsp\": 0, \"parch\": 0, \"fare\": 12.475, \"embarked\": \"S\", \"class\": \"Third\", \"who\": \"child\", \"adult_male\": false, \"deck\": null, \"embark_town\": \"Southampton\", \"alive\": \"yes\", \"alone\": true}, {\"survived\": 1, \"pclass\": 3, \"sex\": \"female\", \"age\": 13.0, \"sibsp\": 0, \"parch\": 0, \"fare\": 7.2292, \"embarked\": \"C\", \"class\": \"Third\", \"who\": \"child\", \"adult_male\": false, \"deck\": null, \"embark_town\": \"Cherbourg\", \"alive\": \"yes\", \"alone\": true}, {\"survived\": 1, \"pclass\": 1, \"sex\": \"female\", \"age\": 17.0, \"sibsp\": 1, \"parch\": 0, \"fare\": 57.0, \"embarked\": \"S\", \"class\": \"First\", \"who\": \"woman\", \"adult_male\": false, \"deck\": \"B\", \"embark_town\": \"Southampton\", \"alive\": \"yes\", \"alone\": false}, {\"survived\": 1, \"pclass\": 3, \"sex\": \"female\", \"age\": 18.0, \"sibsp\": 0, \"parch\": 0, \"fare\": 7.4958, \"embarked\": \"S\", \"class\": \"Third\", \"who\": \"woman\", 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\"sex\": \"male\", \"age\": 11.0, \"sibsp\": 1, \"parch\": 2, \"fare\": 120.0, \"embarked\": \"S\", \"class\": \"First\", \"who\": \"child\", \"adult_male\": false, \"deck\": \"B\", \"embark_town\": \"Southampton\", \"alive\": \"yes\", \"alone\": false}, {\"survived\": 1, \"pclass\": 3, \"sex\": \"male\", \"age\": 0.42, \"sibsp\": 0, \"parch\": 1, \"fare\": 8.5167, \"embarked\": \"C\", \"class\": \"Third\", \"who\": \"child\", \"adult_male\": false, \"deck\": null, \"embark_town\": \"Cherbourg\", \"alive\": \"yes\", \"alone\": false}, {\"survived\": 0, \"pclass\": 3, \"sex\": \"female\", \"age\": 18.0, \"sibsp\": 0, \"parch\": 0, \"fare\": 7.775, \"embarked\": \"S\", \"class\": \"Third\", \"who\": \"woman\", \"adult_male\": false, \"deck\": null, \"embark_town\": \"Southampton\", \"alive\": \"no\", \"alone\": true}, {\"survived\": 0, \"pclass\": 3, \"sex\": \"female\", \"age\": 6.0, \"sibsp\": 4, \"parch\": 2, \"fare\": 31.275, \"embarked\": \"S\", \"class\": \"Third\", \"who\": 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\"Third\", \"who\": \"child\", \"adult_male\": false, \"deck\": null, \"embark_town\": \"Cherbourg\", \"alive\": \"yes\", \"alone\": true}, {\"survived\": 0, \"pclass\": 3, \"sex\": \"male\", \"age\": 19.0, \"sibsp\": 0, \"parch\": 0, \"fare\": 7.8958, \"embarked\": \"S\", \"class\": \"Third\", \"who\": \"man\", \"adult_male\": true, \"deck\": null, \"embark_town\": \"Southampton\", \"alive\": \"no\", \"alone\": true}, {\"survived\": 1, \"pclass\": 1, \"sex\": \"female\", \"age\": 19.0, \"sibsp\": 0, \"parch\": 0, \"fare\": 30.0, \"embarked\": \"S\", \"class\": \"First\", \"who\": \"woman\", \"adult_male\": false, \"deck\": \"B\", \"embark_town\": \"Southampton\", \"alive\": \"yes\", \"alone\": true}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import altair as alt\n",
    "alt.Chart(df[df[\"age\"] < 20]).mark_circle().encode(\n",
    "    x = alt.X(\"age\"),\n",
    "    y = alt.Y(\"fare\"),\n",
    "    color = alt.Color(\"embark_town\", legend = None),\n",
    "    size = alt.Size(\"parch\", legend = None)\n",
    ")"
   ]
  }
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