HW01 - Mustafa Ali.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "deee8267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row_n           0\n",
       "id              1\n",
       "name            0\n",
       "gender          0\n",
       "species         0\n",
       "birthday        0\n",
       "personality     0\n",
       "song           11\n",
       "phrase          0\n",
       "full_id         0\n",
       "url             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "url = \"https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/villagers.csv\"\n",
    "df = pd.read_csv(url)\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e26f5a80",
   "metadata": {},
   "outputs": [],
   "source": [
    " #The following is from ChatGPT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b4e17cbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of columns: 11\n",
      "Number of rows: 391\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the dataset\n",
    "url = 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/villagers.csv'\n",
    "data = pd.read_csv(url)\n",
    "\n",
    "# Display the number of columns and rows\n",
    "print(f'Number of columns: {data.shape[1]}')\n",
    "print(f'Number of rows: {data.shape[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0ad14f91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In the context of my dataset, Observations = Villagers, Variables = Attributes of those Villagers. \n",
    "# Observations refer to specific instances, like specific villagers, and each of the variables is one of their\n",
    "# attributes, whether that be their gender, their name, their ID, or their birthday."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c05bf361",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary statistics for all columns:\n",
      "             row_n       id     name gender species birthday personality  \\\n",
      "count   391.000000      390      391    391     391      391         391   \n",
      "unique         NaN      390      391      2      35      361           8   \n",
      "top            NaN  admiral  Admiral   male     cat     1-27        lazy   \n",
      "freq           NaN        1        1    204      23        2          60   \n",
      "mean    239.902813      NaN      NaN    NaN     NaN      NaN         NaN   \n",
      "std     140.702672      NaN      NaN    NaN     NaN      NaN         NaN   \n",
      "min       2.000000      NaN      NaN    NaN     NaN      NaN         NaN   \n",
      "25%     117.500000      NaN      NaN    NaN     NaN      NaN         NaN   \n",
      "50%     240.000000      NaN      NaN    NaN     NaN      NaN         NaN   \n",
      "75%     363.500000      NaN      NaN    NaN     NaN      NaN         NaN   \n",
      "max     483.000000      NaN      NaN    NaN     NaN      NaN         NaN   \n",
      "\n",
      "                song   phrase           full_id  \\\n",
      "count            380      391               391   \n",
      "unique            92      388               391   \n",
      "top     K.K. Country  wee one  villager-admiral   \n",
      "freq              10        2                 1   \n",
      "mean             NaN      NaN               NaN   \n",
      "std              NaN      NaN               NaN   \n",
      "min              NaN      NaN               NaN   \n",
      "25%              NaN      NaN               NaN   \n",
      "50%              NaN      NaN               NaN   \n",
      "75%              NaN      NaN               NaN   \n",
      "max              NaN      NaN               NaN   \n",
      "\n",
      "                                                      url  \n",
      "count                                                 391  \n",
      "unique                                                391  \n",
      "top     https://villagerdb.com/images/villagers/thumb/...  \n",
      "freq                                                    1  \n",
      "mean                                                  NaN  \n",
      "std                                                   NaN  \n",
      "min                                                   NaN  \n",
      "25%                                                   NaN  \n",
      "50%                                                   NaN  \n",
      "75%                                                   NaN  \n",
      "max                                                   NaN  \n",
      "\n",
      "Value counts for numeric columns:\n",
      "\n",
      "Column: row_n\n",
      "row_n\n",
      "2      1\n",
      "303    1\n",
      "331    1\n",
      "330    1\n",
      "328    1\n",
      "      ..\n",
      "151    1\n",
      "150    1\n",
      "149    1\n",
      "148    1\n",
      "483    1\n",
      "Name: count, Length: 391, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the dataset\n",
    "url = 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/villagers.csv'\n",
    "data = pd.read_csv(url)\n",
    "\n",
    "# Display summary statistics for all columns\n",
    "print(\"Summary statistics for all columns:\")\n",
    "print(data.describe(include='all'))\n",
    "\n",
    "# Display value counts for numeric columns\n",
    "print(\"\\nValue counts for numeric columns:\")\n",
    "for column in data.select_dtypes(include=['number']).columns:\n",
    "    print(f'\\nColumn: {column}')\n",
    "    print(data[column].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "47847a55",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the dataset\n",
    "url = 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/villagers.csv'\n",
    "data = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bec3ba59",
   "metadata": {
    "scrolled": true
   },
   "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>row_n</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>species</th>\n",
       "      <th>birthday</th>\n",
       "      <th>personality</th>\n",
       "      <th>song</th>\n",
       "      <th>phrase</th>\n",
       "      <th>full_id</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>admiral</td>\n",
       "      <td>Admiral</td>\n",
       "      <td>male</td>\n",
       "      <td>bird</td>\n",
       "      <td>1-27</td>\n",
       "      <td>cranky</td>\n",
       "      <td>Steep Hill</td>\n",
       "      <td>aye aye</td>\n",
       "      <td>villager-admiral</td>\n",
       "      <td>https://villagerdb.com/images/villagers/thumb/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>agent-s</td>\n",
       "      <td>Agent S</td>\n",
       "      <td>female</td>\n",
       "      <td>squirrel</td>\n",
       "      <td>7-2</td>\n",
       "      <td>peppy</td>\n",
       "      <td>DJ K.K.</td>\n",
       "      <td>sidekick</td>\n",
       "      <td>villager-agent-s</td>\n",
       "      <td>https://villagerdb.com/images/villagers/thumb/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>agnes</td>\n",
       "      <td>Agnes</td>\n",
       "      <td>female</td>\n",
       "      <td>pig</td>\n",
       "      <td>4-21</td>\n",
       "      <td>uchi</td>\n",
       "      <td>K.K. House</td>\n",
       "      <td>snuffle</td>\n",
       "      <td>villager-agnes</td>\n",
       "      <td>https://villagerdb.com/images/villagers/thumb/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>al</td>\n",
       "      <td>Al</td>\n",
       "      <td>male</td>\n",
       "      <td>gorilla</td>\n",
       "      <td>10-18</td>\n",
       "      <td>lazy</td>\n",
       "      <td>Steep Hill</td>\n",
       "      <td>Ayyeeee</td>\n",
       "      <td>villager-al</td>\n",
       "      <td>https://villagerdb.com/images/villagers/thumb/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>alfonso</td>\n",
       "      <td>Alfonso</td>\n",
       "      <td>male</td>\n",
       "      <td>alligator</td>\n",
       "      <td>6-9</td>\n",
       "      <td>lazy</td>\n",
       "      <td>Forest Life</td>\n",
       "      <td>it'sa me</td>\n",
       "      <td>villager-alfonso</td>\n",
       "      <td>https://villagerdb.com/images/villagers/thumb/...</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>386</th>\n",
       "      <td>475</td>\n",
       "      <td>winnie</td>\n",
       "      <td>Winnie</td>\n",
       "      <td>female</td>\n",
       "      <td>horse</td>\n",
       "      <td>1-31</td>\n",
       "      <td>peppy</td>\n",
       "      <td>My Place</td>\n",
       "      <td>hay-OK</td>\n",
       "      <td>villager-winnie</td>\n",
       "      <td>https://villagerdb.com/images/villagers/thumb/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>477</td>\n",
       "      <td>wolfgang</td>\n",
       "      <td>Wolfgang</td>\n",
       "      <td>male</td>\n",
       "      <td>wolf</td>\n",
       "      <td>11-25</td>\n",
       "      <td>cranky</td>\n",
       "      <td>K.K. Song</td>\n",
       "      <td>snarrrl</td>\n",
       "      <td>villager-wolfgang</td>\n",
       "      <td>https://villagerdb.com/images/villagers/thumb/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>480</td>\n",
       "      <td>yuka</td>\n",
       "      <td>Yuka</td>\n",
       "      <td>female</td>\n",
       "      <td>koala</td>\n",
       "      <td>7-20</td>\n",
       "      <td>snooty</td>\n",
       "      <td>Soulful K.K.</td>\n",
       "      <td>tsk tsk</td>\n",
       "      <td>villager-yuka</td>\n",
       "      <td>https://villagerdb.com/images/villagers/thumb/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>481</td>\n",
       "      <td>zell</td>\n",
       "      <td>Zell</td>\n",
       "      <td>male</td>\n",
       "      <td>deer</td>\n",
       "      <td>6-7</td>\n",
       "      <td>smug</td>\n",
       "      <td>K.K. D&amp;B</td>\n",
       "      <td>pronk</td>\n",
       "      <td>villager-zell</td>\n",
       "      <td>https://villagerdb.com/images/villagers/thumb/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>483</td>\n",
       "      <td>zucker</td>\n",
       "      <td>Zucker</td>\n",
       "      <td>male</td>\n",
       "      <td>octopus</td>\n",
       "      <td>3-8</td>\n",
       "      <td>lazy</td>\n",
       "      <td>Spring Blossoms</td>\n",
       "      <td>bloop</td>\n",
       "      <td>villager-zucker</td>\n",
       "      <td>https://villagerdb.com/images/villagers/thumb/...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>391 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     row_n        id      name  gender    species birthday personality  \\\n",
       "0        2   admiral   Admiral    male       bird     1-27      cranky   \n",
       "1        3   agent-s   Agent S  female   squirrel      7-2       peppy   \n",
       "2        4     agnes     Agnes  female        pig     4-21        uchi   \n",
       "3        6        al        Al    male    gorilla    10-18        lazy   \n",
       "4        7   alfonso   Alfonso    male  alligator      6-9        lazy   \n",
       "..     ...       ...       ...     ...        ...      ...         ...   \n",
       "386    475    winnie    Winnie  female      horse     1-31       peppy   \n",
       "387    477  wolfgang  Wolfgang    male       wolf    11-25      cranky   \n",
       "388    480      yuka      Yuka  female      koala     7-20      snooty   \n",
       "389    481      zell      Zell    male       deer      6-7        smug   \n",
       "390    483    zucker    Zucker    male    octopus      3-8        lazy   \n",
       "\n",
       "                song    phrase            full_id  \\\n",
       "0         Steep Hill   aye aye   villager-admiral   \n",
       "1            DJ K.K.  sidekick   villager-agent-s   \n",
       "2         K.K. House   snuffle     villager-agnes   \n",
       "3         Steep Hill   Ayyeeee        villager-al   \n",
       "4        Forest Life  it'sa me   villager-alfonso   \n",
       "..               ...       ...                ...   \n",
       "386         My Place    hay-OK    villager-winnie   \n",
       "387        K.K. Song   snarrrl  villager-wolfgang   \n",
       "388     Soulful K.K.   tsk tsk      villager-yuka   \n",
       "389         K.K. D&B     pronk      villager-zell   \n",
       "390  Spring Blossoms     bloop    villager-zucker   \n",
       "\n",
       "                                                   url  \n",
       "0    https://villagerdb.com/images/villagers/thumb/...  \n",
       "1    https://villagerdb.com/images/villagers/thumb/...  \n",
       "2    https://villagerdb.com/images/villagers/thumb/...  \n",
       "3    https://villagerdb.com/images/villagers/thumb/...  \n",
       "4    https://villagerdb.com/images/villagers/thumb/...  \n",
       "..                                                 ...  \n",
       "386  https://villagerdb.com/images/villagers/thumb/...  \n",
       "387  https://villagerdb.com/images/villagers/thumb/...  \n",
       "388  https://villagerdb.com/images/villagers/thumb/...  \n",
       "389  https://villagerdb.com/images/villagers/thumb/...  \n",
       "390  https://villagerdb.com/images/villagers/thumb/...  \n",
       "\n",
       "[391 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7034b93c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in numeric columns:\n",
      "Series([], dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the dataset\n",
    "url = 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/villagers.csv'\n",
    "data = pd.read_csv(url, encoding='utf-8')\n",
    "\n",
    "# Check for missing values in numeric columns\n",
    "missing_values = data.select_dtypes(include=['number']).isnull().sum()\n",
    "\n",
    "# Display columns with missing values\n",
    "print(\"Missing values in numeric columns:\")\n",
    "print(missing_values[missing_values > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "145ad8ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This data set has no missing numeric values so I will simply take Chat's code and replace it with a different data set (the Titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1e469eec",
   "metadata": {},
   "outputs": [],
   "source": [
    "url2 = 'https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv'\n",
    "data2 = pd.read_csv(url2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1672d5a0",
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5e7f626e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in numeric columns:\n",
      "age    177\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Check for missing values in numeric columns\n",
    "missing_values2 = data2.select_dtypes(include=['number']).isnull().sum()\n",
    "\n",
    "# Display columns with missing values\n",
    "print(\"Missing values in numeric columns:\")\n",
    "print(missing_values2[missing_values2 > 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a7ceb545",
   "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>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>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>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>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>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>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>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>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>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": [
       "         survived      pclass         age       sibsp       parch        fare\n",
       "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aaf675f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 15)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a3dbbb4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reason for Discrepancy:\n",
    "\n",
    "# The discrepancy occurs because data2.describe() only counts the non-missing values for each column.\n",
    "# Columns with a count of 891 have no missing values, while columns with a count less than 891 have missing data.\n",
    "# Specifically, in this case, the \"age\" column has missing values, which is why its count is lower (714)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "85ad543a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# It seems that the difference between Methods and Attributes may be that Attributes are simply values associated\n",
    "# with a dataset, whether that be the number of columns or rows, while Methods are functions which actually perform\n",
    "# an action on the dataset, whether that be determing the Mean using .describe() or dropping null values using .dropna()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "679396d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here's a summary of the statistical metrics `count`, `mean`, `std`, `min`, `25%`, `50%`, `75%`, and `max`, \n",
    "# which are included in the output of the `df.describe()` method:\n",
    "\n",
    "# 1. **Count**: number of items\n",
    "   # - Represents the number of non-missing (non-NaN) values in each column.\n",
    "   # - It tells you how many data points are available for each variable.\n",
    "\n",
    "# 2. **Mean**: the average of numeric columns\n",
    "   # - The average value of the numeric data in each column.\n",
    "   # - It is calculated by summing all the values and dividing by the count of non-missing values.\n",
    "\n",
    "# 3. **Standard Deviation (std)**: indicates the amount of variation in data\n",
    "   # - Measures the amount of variation or dispersion in the data.\n",
    "   # - A high standard deviation indicates that the values are spread out over a wider range, \n",
    "   #   while a low standard deviation means they are closer to the mean.\n",
    "\n",
    "# 4. **Minimum (min)**: minimum value\n",
    "   # - The smallest value in each column.\n",
    "   # - It indicates the lower bound of the data range.\n",
    "\n",
    "# 5. **25% (First Quartile)**: Q1\n",
    "   # - Represents the 25th percentile of the data.\n",
    "   # - It indicates that 25% of the data points are below this value.\n",
    "\n",
    "# 6. **50% (Median)**: Median (50th percentile)\n",
    "   # - Represents the 50th percentile, also known as the median.\n",
    "   # - It is the middle value of the data, splitting the dataset into two equal halves.\n",
    "\n",
    "# 7. **75% (Third Quartile)**: Q3\n",
    "   # - Represents the 75th percentile of the data.\n",
    "   # - It indicates that 75% of the data points are below this value, and 25% are above.\n",
    "\n",
    "# 8. **Maximum (max)**: maximum value\n",
    "   # - The largest value in each column.\n",
    "   # - It indicates the upper bound of the data range.\n",
    "\n",
    "# These metrics provide a quick summary of the distribution, central tendency, and spread \n",
    "# of numeric values within each column of the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a6317db6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 7.1 and 7.2\n",
    "# df.dropna() might be peferred over using del df['col'] when the user wants to remove null values from\n",
    "# a specific row or column, while someone might prefer vice versa when completing deleting a column\n",
    "# if it is for example, unnecessary.\n",
    "# 7.3\n",
    "# If there are columns that you wish to delete and null values that you wish to remove in rows and/or columns\n",
    "# it might be preferred to delete the columns first as to remove the irrelevant data that is taking up a whole column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "67cd3517",
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e31b0093",
   "metadata": {},
   "outputs": [],
   "source": [
    "del data2['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "48b6038a",
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>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>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>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</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>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>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>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>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 × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex  sibsp  parch     fare embarked   class    who  \\\n",
       "0           0       3    male      1      0   7.2500        S   Third    man   \n",
       "1           1       1  female      1      0  71.2833        C   First  woman   \n",
       "2           1       3  female      0      0   7.9250        S   Third  woman   \n",
       "3           1       1  female      1      0  53.1000        S   First  woman   \n",
       "4           0       3    male      0      0   8.0500        S   Third    man   \n",
       "..        ...     ...     ...    ...    ...      ...      ...     ...    ...   \n",
       "886         0       2    male      0      0  13.0000        S  Second    man   \n",
       "887         1       1  female      0      0  30.0000        S   First  woman   \n",
       "888         0       3  female      1      2  23.4500        S   Third  woman   \n",
       "889         1       1    male      0      0  30.0000        C   First    man   \n",
       "890         0       3    male      0      0   7.7500        Q   Third    man   \n",
       "\n",
       "     adult_male deck  embark_town alive  alone  \n",
       "0          True  NaN  Southampton    no  False  \n",
       "1         False    C    Cherbourg   yes  False  \n",
       "2         False  NaN  Southampton   yes   True  \n",
       "3         False    C  Southampton   yes  False  \n",
       "4          True  NaN  Southampton    no   True  \n",
       "..          ...  ...          ...   ...    ...  \n",
       "886        True  NaN  Southampton    no   True  \n",
       "887       False    B  Southampton   yes   True  \n",
       "888       False  NaN  Southampton    no  False  \n",
       "889        True    C    Cherbourg   yes   True  \n",
       "890        True  NaN   Queenstown    no   True  \n",
       "\n",
       "[891 rows x 14 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "51bdf199",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The reason I did this was because I noticed that most of the data in the age column was missing\n",
    "# so instead of deleting all null values in it I deleted the entire column as it was unnecessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "14d50bb0",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</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>First</th>\n",
       "      <td>216.0</td>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.484026</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Second</th>\n",
       "      <td>184.0</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.500623</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Third</th>\n",
       "      <td>491.0</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>0.428949</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count      mean       std  min  25%  50%  75%  max\n",
       "class                                                     \n",
       "First   216.0  0.629630  0.484026  0.0  0.0  1.0  1.0  1.0\n",
       "Second  184.0  0.472826  0.500623  0.0  0.0  0.0  1.0  1.0\n",
       "Third   491.0  0.242363  0.428949  0.0  0.0  0.0  0.0  1.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.groupby(\"class\")[\"survived\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4196cd18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 8.1\n",
    "# This is useful because it tells us, for example, using the mean, that\n",
    "# 62.9630% of First Class survived, 47.2826% of Second and\t24.2363% of Third.\n",
    "\n",
    "# This grouped the survived column by the class column and then determined the\n",
    "# statistics on the groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "61f8c5bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 8.2\n",
    "# The 'df.groupby(\"col1\")[\"col2\"].describe()' code determines the statistics specific to one column\n",
    "# and broken into groups by another column, while df.describe() provides the statistics of the\n",
    "# entire dataset and the count value is not counting based on the value of a certain column (like class above),\n",
    "# but rather it is counting every non-missing value in the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e4f2e417",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "'(' was never closed (3569174409.py, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[2], line 3\u001b[0;36m\u001b[0m\n\u001b[0;31m    data2 = pd.read_csv(url2\u001b[0m\n\u001b[0m                       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m '(' was never closed\n"
     ]
    }
   ],
   "source": [
    "# 8.3\n",
    "url2 = 'https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanics.csv'\n",
    "data2 = pd.read_csv(url2\n",
    "\n",
    "DATA2.group_by(\"class\")[didtheysurvive?].describle()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d408946",
   "metadata": {},
   "outputs": [],
   "source": [
    "# After the first ChatGPT prompt it fixed A, B, C, D, E, F, and G, all at once. I am impressed with ChatGPT. \n",
    "# I found the same answers on Google as well but I had to go and find the solution to each problem individually, \n",
    "# also not every problem was easily describable online and therefore it was hard to find answers specific to my situation.\n",
    "# The amount of time I saved just by using ChatGPT is incredible. Below is the code that ChatGPT outputted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ab16792",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        count      mean       std  min  25%  50%  75%  max\n",
      "class                                                     \n",
      "First   216.0  0.629630  0.484026  0.0  0.0  1.0  1.0  1.0\n",
      "Second  184.0  0.472826  0.500623  0.0  0.0  0.0  1.0  1.0\n",
      "Third   491.0  0.242363  0.428949  0.0  0.0  0.0  0.0  1.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Correct URL (typo fixed: 'titanic.csv' instead of 'titanics.csv')\n",
    "url2 = 'https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv'\n",
    "\n",
    "# Properly close the parentheses in pd.read_csv\n",
    "data2 = pd.read_csv(url2)\n",
    "\n",
    "# Correct method chaining and syntax\n",
    "# Fix group_by to groupby, correct column name 'didtheysurvive?' to the actual column name 'survived'\n",
    "# Correct describle() to describe()\n",
    "result = data2.groupby(\"class\")[\"survived\"].describe()\n",
    "\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "80905f55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 9 Yes I have, and I have found them to be quite useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b5415eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Below is the summary of the Chatbot conversation and the link to the session. The summary is written by ChatGPT.\n",
    "\n",
    "# Link: https://chatgpt.com/share/66e39db6-5028-8003-8d9e-23d68190298a\n",
    "\n",
    "# Initial Dataset and Analysis:\n",
    "# You initially shared a link to the villagers.csv dataset and asked for Python code using pandas to display \n",
    "# the number of columns and rows. We discussed the definitions of \"Observations\" (villagers) and \"Variables\" \n",
    "# (attributes of those villagers) in the context of your dataset. You confirmed that Observations = Villagers \n",
    "# and Variables = Attributes of those Villagers.\n",
    "\n",
    "# Summarizing Columns:\n",
    "# I provided Python code to summarize each column in the dataset using \n",
    "# df.describe() for general statistics and df['column'].value_counts() for numeric values.\n",
    "\n",
    "#Dataset Encoding:\n",
    "# We discussed that the correct encoding for the dataset is UTF-8, and I provided code to read \n",
    "# the dataset with the correct encoding.\n",
    "\n",
    "# Checking for Missing Numeric Values:\n",
    "# I provided Python code to check if any numeric values are missing in the dataset using isnull().sum().\n",
    "\n",
    "# Switching to Titanic Dataset:\n",
    "# You switched to using the Titanic dataset (titanic.csv) and asked about discrepancies between data2.shape \n",
    "# and the counts reported by data2.describe(). I explained that data2.describe() shows the count of non-missing \n",
    "# values for each numeric column, and discrepancies occur because some columns, like \"age,\" have missing values.\n",
    "\n",
    "# Attributes vs. Methods:\n",
    "# I explained the difference between attributes (e.g., df.shape) and methods (e.g., df.describe()):\n",
    "# Attributes are properties of an object that do not perform actions and are accessed without parentheses.\n",
    "# Methods are functions associated with an object that perform actions or calculations and are called with parentheses.\n",
    "\n",
    "# Groupby and Describe Explanation:\n",
    "# We discussed what df.groupby(\"col1\")[\"col2\"].describe() does: it groups the DataFrame by unique values in col1, \n",
    "# selects col2, and provides descriptive statistics for col2 within each group defined by col1.\n",
    "\n",
    "# Error Troubleshooting:\n",
    "# You encountered a SyntaxError with code involving reading the Titanic dataset and grouping data.\n",
    "# I provided a corrected version of your code, fixing issues with the URL, missing parentheses, method names, and \n",
    "# ensuring the correct column was used."
   ]
  }
 ],
 "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
}