PandasTutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e04228ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c65c5510",
   "metadata": {},
   "source": [
    "## pd.Series\n",
    "Seriest is one-dimensional labeled array capable of holding data of any type. Initiation of Series looks like \n",
    "```\n",
    "class pandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "44626d5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1.0\n",
      "1    3.0\n",
      "2    5.0\n",
      "3    NaN\n",
      "4    6.0\n",
      "5    8.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "some_list = [1, 3, 5, np.nan, 6, 8]; #np.nan is not taken in all computations. Also its necessary to mention that nan in float type\n",
    "series_1 = pd.Series(data = some_list);\n",
    "print(series_1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a861c9f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1st day    1.0\n",
      "2nd day    3.0\n",
      "3rd day    5.0\n",
      "4th day    NaN\n",
      "5th day    6.0\n",
      "6th day    8.0\n",
      "Name: Temperature, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "ind = [\"1st day\", \"2nd day\", \"3rd day\", \"4th day\", \"5th day\", \"6th day\"];\n",
    "\n",
    "series_2 = pd.Series(data = some_list, name = \"Temperature\", index = ind);\n",
    "print(series_2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0801a72a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan\n"
     ]
    }
   ],
   "source": [
    "print(series_2['4th day']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ac835a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 \n",
      "\n",
      "2nd day    3.0\n",
      "3rd day    5.0\n",
      "Name: Temperature, dtype: float64 \n",
      "\n",
      "6th day    8.0\n",
      "5th day    6.0\n",
      "4th day    NaN\n",
      "3rd day    5.0\n",
      "2nd day    3.0\n",
      "1st day    1.0\n",
      "Name: Temperature, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#indexes work with the same syntax as lists or numpy arrays\n",
    "\n",
    "print(series_2[0], \"\\n\");\n",
    "print(series_2[1:3], \"\\n\");\n",
    "print(series_2[::-1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cba68875",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-01-01    0.841734\n",
      "2019-01-02    0.809161\n",
      "2019-01-03    0.342323\n",
      "2019-01-04    0.936081\n",
      "2019-01-05    0.351614\n",
      "2019-01-06    0.206163\n",
      "2019-01-07    0.304646\n",
      "2019-01-08    0.422872\n",
      "2019-01-09    0.625746\n",
      "2019-01-10    0.365136\n",
      "Freq: D, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#indexes with conditions \n",
    "date_range = pd.date_range('20190101', periods = 10);\n",
    "series_4 = pd.Series(data = np.random.rand(10), index = date_range);\n",
    "print(series_4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f49c674",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-01-01     True\n",
      "2019-01-02     True\n",
      "2019-01-03    False\n",
      "2019-01-04     True\n",
      "2019-01-05    False\n",
      "2019-01-06    False\n",
      "2019-01-07    False\n",
      "2019-01-08    False\n",
      "2019-01-09     True\n",
      "2019-01-10    False\n",
      "Freq: D, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "#condition \n",
    "print(series_4 > 0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d4027dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-01-01    0.841734\n",
      "2019-01-02    0.809161\n",
      "2019-01-04    0.936081\n",
      "2019-01-09    0.625746\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#also u can make calls in such way\n",
    "print(series_4[series_4 > 0.5]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3b0856bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], Freq: D, dtype: float64)\n"
     ]
    }
   ],
   "source": [
    "print(series_4[(series_4 > 0.6) & (series_4 < 0.2)]);\n",
    "# AND = &, OR = | in pandas syntax "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b40d9430",
   "metadata": {},
   "source": [
    "### Sorts \n",
    "pd.Series can be sorted by values or by indexes \n",
    "```\n",
    "pd.Series.sort_index()\n",
    "pd.Series.sort_values()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ce9cc96d",
   "metadata": {},
   "outputs": [],
   "source": [
    "series_4.sort_index();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e571c049",
   "metadata": {},
   "outputs": [],
   "source": [
    "series_4.sort_values();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5d49637",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-01-01    0.745799\n",
      "2019-01-02    0.723708\n",
      "2019-01-03    0.335676\n",
      "2019-01-04    0.805240\n",
      "2019-01-05    0.344413\n",
      "2019-01-06    0.204706\n",
      "2019-01-07    0.299955\n",
      "2019-01-08    0.410381\n",
      "2019-01-09    0.585702\n",
      "2019-01-10    0.357076\n",
      "Freq: D, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Data in series can be treated as numpy array\n",
    "series_4 + series_1;\n",
    "print(np.sin(series_4));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b18a7e4",
   "metadata": {},
   "source": [
    "### pd.DataFrame\n",
    "pd.DataFrame is 2 dimensional structure with indexes and columns. DataFrame could be built from Dictionary or array[x][y]\n",
    "```\n",
    "class pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=None)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a0159158",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   one  two  three\n",
      "a  1.0    1      5\n",
      "b  2.0    2      6\n",
      "c  3.0    3      7\n",
      "d  NaN    4      8\n"
     ]
    }
   ],
   "source": [
    "#Dictionary initialization \n",
    "some_dict = {'one': pd.Series([1, 2, 3], index = ['a','b', 'c']),\n",
    "             'two': pd.Series([1, 2, 3, 4], index = ['a','b', 'c', 'd']),\n",
    "             'three': pd.Series([5, 6, 7, 8], index = ['a','b', 'c', 'd'])};\n",
    "frame_1 = pd.DataFrame(some_dict);\n",
    "print(frame_1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e4d352b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   one  two  three\n",
      "a  1.0    1      5\n",
      "b  2.0    2      6\n",
      "c  3.0    7      7\n",
      "d  NaN    4      8\n"
     ]
    }
   ],
   "source": [
    "#Lists initialization\n",
    "some_array =[[1, 1, 5], [2, 2, 6], [3, 7, 7], [np.nan, 4, 8]];\n",
    "frame_2 = pd.DataFrame(data = some_array, index = ['a', 'b', 'c', 'd'], columns = ['one', 'two', \"three\"]);\n",
    "print(frame_2);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8d0442af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  1.  5.]\n",
      " [ 2.  2.  6.]\n",
      " [ 3.  7.  7.]\n",
      " [nan  4.  8.]]\n"
     ]
    }
   ],
   "source": [
    "print(frame_2.values); #to get values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4939f189",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['one', 'two', 'three'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(frame_2.columns); #to get columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b833f315",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame_2.columns = ['first_column', 'second_column', 'third_column'];\n",
    "frame_2.index = [1, 2, 3, 4];"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "823f8b0d",
   "metadata": {},
   "source": [
    "### Index \n",
    "By columns (returns pd.Series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6ee445d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    1.0\n",
      "2    2.0\n",
      "3    3.0\n",
      "4    NaN\n",
      "Name: first_column, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "first_column = frame_2['first_column'];\n",
    "print(first_column);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "87dbe32c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   first_column  second_column\n",
      "1           1.0              1\n",
      "2           2.0              2\n",
      "3           3.0              7\n",
      "4           NaN              4\n"
     ]
    }
   ],
   "source": [
    "subset_dataframe = frame_2[['first_column', 'second_column']];\n",
    "print(subset_dataframe);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b1d40b1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    1.0\n",
      "2    2.0\n",
      "3    3.0\n",
      "4    NaN\n",
      "Name: first_column, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(subset_dataframe['first_column']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7f52c0e",
   "metadata": {},
   "source": [
    "### .loc and .iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b89d46ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   first_column  second_column\n",
      "2           2.0              2\n",
      "3           3.0              7\n"
     ]
    }
   ],
   "source": [
    "#by index\n",
    "print(frame_2.iloc[1:3, :2]); # [1; 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "319bd9f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   first_column  second_column\n",
      "1           1.0              1\n",
      "2           2.0              2\n",
      "3           3.0              7\n"
     ]
    }
   ],
   "source": [
    "print(frame_2.loc[1:3, ['first_column', 'second_column']]); # [1, 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c08da0ec",
   "metadata": {},
   "source": [
    "### Modification of data_set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "06015e0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   first_column  second_column  third_column  new_column\n",
      "1           1.0              1             5           5\n",
      "2           2.0              2             6           2\n",
      "3           3.0              7             7           1\n",
      "4           NaN              4             8           4\n"
     ]
    }
   ],
   "source": [
    "new_column = [5, 2, 1, 4];\n",
    "frame_2['new_column'] = new_column;\n",
    "print(frame_2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d1fcfd5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   first_column  second_column  third_column  new_column\n",
      "1           0.1              1             5           5\n",
      "2           0.2              2             6           2\n",
      "3           0.3              7             7           1\n",
      "4           NaN              4             8           4\n"
     ]
    }
   ],
   "source": [
    "frame_2['first_column'] /= 10;\n",
    "print(frame_2);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b50cab8b",
   "metadata": {},
   "source": [
    "### Working with Titanic DataSet\n",
    "`titanic_data.csv` - all information of passengers\n",
    "\n",
    "`titanic_surv.csv` - information about surviaval of passenger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5cc5e017",
   "metadata": {},
   "outputs": [],
   "source": [
    "#initialization\n",
    "# df_1 = pd.read_csv('titanic_data.csv');\n",
    "pass_link = 'https://www.dropbox.com/s/lyzcuxu1pdrw5qb/titanic_data.csv?dl=1';\n",
    "titanic_passengers = pd.read_csv(pass_link, index_col = 'PassengerId');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e2c31580",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Passengers:  891\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
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       "      <th>Cabin</th>\n",
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       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pclass                                               Name  \\\n",
       "PassengerId                                                              \n",
       "1                 3                            Braund, Mr. Owen Harris   \n",
       "2                 1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "3                 3                             Heikkinen, Miss. Laina   \n",
       "4                 1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "5                 3                           Allen, Mr. William Henry   \n",
       "6                 3                                   Moran, Mr. James   \n",
       "7                 1                            McCarthy, Mr. Timothy J   \n",
       "8                 3                     Palsson, Master. Gosta Leonard   \n",
       "9                 3  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)   \n",
       "10                2                Nasser, Mrs. Nicholas (Adele Achem)   \n",
       "\n",
       "                Sex   Age  SibSp  Parch            Ticket     Fare Cabin  \\\n",
       "PassengerId                                                                \n",
       "1              male  22.0      1      0         A/5 21171   7.2500   NaN   \n",
       "2            female  38.0      1      0          PC 17599  71.2833   C85   \n",
       "3            female  26.0      0      0  STON/O2. 3101282   7.9250   NaN   \n",
       "4            female  35.0      1      0            113803  53.1000  C123   \n",
       "5              male  35.0      0      0            373450   8.0500   NaN   \n",
       "6              male   NaN      0      0            330877   8.4583   NaN   \n",
       "7              male  54.0      0      0             17463  51.8625   E46   \n",
       "8              male   2.0      3      1            349909  21.0750   NaN   \n",
       "9            female  27.0      0      2            347742  11.1333   NaN   \n",
       "10           female  14.0      1      0            237736  30.0708   NaN   \n",
       "\n",
       "            Embarked  \n",
       "PassengerId           \n",
       "1                  S  \n",
       "2                  C  \n",
       "3                  S  \n",
       "4                  S  \n",
       "5                  S  \n",
       "6                  Q  \n",
       "7                  S  \n",
       "8                  S  \n",
       "9                  S  \n",
       "10                 C  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Passengers: ', len(titanic_passengers));\n",
    "titanic_passengers.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2fb593de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 10)\n"
     ]
    }
   ],
   "source": [
    "print(titanic_passengers.shape); #shape of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "833b1dce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 10 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Pclass    891 non-null    int64  \n",
      " 1   Name      891 non-null    object \n",
      " 2   Sex       891 non-null    object \n",
      " 3   Age       714 non-null    float64\n",
      " 4   SibSp     891 non-null    int64  \n",
      " 5   Parch     891 non-null    int64  \n",
      " 6   Ticket    891 non-null    object \n",
      " 7   Fare      891 non-null    float64\n",
      " 8   Cabin     204 non-null    object \n",
      " 9   Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(3), object(5)\n",
      "memory usage: 76.6+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(titanic_passengers.info()); #type and num of elements in column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c6251d90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Pclass         Age       SibSp       Parch        Fare\n",
      "count  891.000000  714.000000  891.000000  891.000000  891.000000\n",
      "mean     2.308642   29.699118    0.523008    0.381594   32.204208\n",
      "std      0.836071   14.526497    1.102743    0.806057   49.693429\n",
      "min      1.000000    0.420000    0.000000    0.000000    0.000000\n",
      "25%      2.000000   20.125000    0.000000    0.000000    7.910400\n",
      "50%      3.000000   28.000000    0.000000    0.000000   14.454200\n",
      "75%      3.000000   38.000000    1.000000    0.000000   31.000000\n",
      "max      3.000000   80.000000    8.000000    6.000000  512.329200\n"
     ]
    }
   ],
   "source": [
    "print(titanic_passengers.describe());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a6b3cab3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Age of Passenger is:  29.69911764705882\n"
     ]
    }
   ],
   "source": [
    "print('Mean Age of Passenger is: ', titanic_passengers['Age'].mean());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ebc039e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "male      577\n",
      "female    314\n",
      "Name: Sex, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(titanic_passengers['Sex'].value_counts());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d78f9db4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2    184\n",
      "1    216\n",
      "3    491\n",
      "Name: Pclass, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(titanic_passengers['Pclass'].value_counts().sort_values());"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21353aa9",
   "metadata": {},
   "source": [
    "##### Usage of `pandas.DataFrame.groupby` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6e1fed72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Age     SibSp     Parch       Fare\n",
      "Pclass                                          \n",
      "1       38.233441  0.416667  0.356481  84.154687\n",
      "2       29.877630  0.402174  0.380435  20.662183\n",
      "3       25.140620  0.615071  0.393075  13.675550\n"
     ]
    }
   ],
   "source": [
    "print(titanic_passengers.groupby(['Pclass']).mean());"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d4407c",
   "metadata": {},
   "source": [
    "##### Usage of `pandas.join` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1142747f",
   "metadata": {},
   "outputs": [],
   "source": [
    "survival = pd.read_csv('https://www.dropbox.com/s/v35x9i6a1tc7emm/titanic_surv.csv?dl=1');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "de1f9423",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Survived\n",
       "1          0\n",
       "2          1\n",
       "3          1\n",
       "4          1\n",
       "5          0\n",
       "6          0\n",
       "7          0\n",
       "8          0\n",
       "9          1\n",
       "10         1"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival.index = np.arange(1, 892);\n",
    "survival.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d6d5dffa",
   "metadata": {},
   "outputs": [],
   "source": [
    "survival = survival.sample(frac = 1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b4f635ab",
   "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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pclass                                               Name  \\\n",
       "PassengerId                                                              \n",
       "1                 3                            Braund, Mr. Owen Harris   \n",
       "2                 1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "3                 3                             Heikkinen, Miss. Laina   \n",
       "4                 1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "5                 3                           Allen, Mr. William Henry   \n",
       "6                 3                                   Moran, Mr. James   \n",
       "7                 1                            McCarthy, Mr. Timothy J   \n",
       "8                 3                     Palsson, Master. Gosta Leonard   \n",
       "9                 3  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)   \n",
       "10                2                Nasser, Mrs. Nicholas (Adele Achem)   \n",
       "\n",
       "                Sex   Age  SibSp  Parch            Ticket     Fare Cabin  \\\n",
       "PassengerId                                                                \n",
       "1              male  22.0      1      0         A/5 21171   7.2500   NaN   \n",
       "2            female  38.0      1      0          PC 17599  71.2833   C85   \n",
       "3            female  26.0      0      0  STON/O2. 3101282   7.9250   NaN   \n",
       "4            female  35.0      1      0            113803  53.1000  C123   \n",
       "5              male  35.0      0      0            373450   8.0500   NaN   \n",
       "6              male   NaN      0      0            330877   8.4583   NaN   \n",
       "7              male  54.0      0      0             17463  51.8625   E46   \n",
       "8              male   2.0      3      1            349909  21.0750   NaN   \n",
       "9            female  27.0      0      2            347742  11.1333   NaN   \n",
       "10           female  14.0      1      0            237736  30.0708   NaN   \n",
       "\n",
       "            Embarked  Survived  \n",
       "PassengerId                     \n",
       "1                  S         0  \n",
       "2                  C         1  \n",
       "3                  S         1  \n",
       "4                  S         1  \n",
       "5                  S         0  \n",
       "6                  Q         0  \n",
       "7                  S         0  \n",
       "8                  S         0  \n",
       "9                  S         1  \n",
       "10                 C         1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_passengers = titanic_passengers.join(survival);\n",
    "titanic_passengers.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c336966b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    549\n",
      "1    342\n",
      "Name: Survived, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(titanic_passengers['Survived'].value_counts());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0a83e416",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex\n",
      "female    233\n",
      "male      109\n",
      "Name: Survived, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data_group_by_sex = titanic_passengers.groupby(['Sex']);\n",
    "print(data_group_by_sex['Survived'].sum());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2ff23114",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6406/1122245882.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  correlation_data['Sex'] = (correlation_data['Sex'] == 'female').astype(int)\n"
     ]
    },
    {
     "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Sex   Age  Survived\n",
       "PassengerId                     \n",
       "1              0  22.0         0\n",
       "2              1  38.0         1\n",
       "3              1  26.0         1\n",
       "4              1  35.0         1\n",
       "5              0  35.0         0\n",
       "6              0   NaN         0\n",
       "7              0  54.0         0\n",
       "8              0   2.0         0\n",
       "9              1  27.0         1\n",
       "10             1  14.0         1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_data = titanic_passengers[['Sex', 'Age', 'Survived']];\n",
    "correlation_data['Sex'] = (correlation_data['Sex'] == 'female').astype(int)\n",
    "correlation_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8d6837af",
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.093254</td>\n",
       "      <td>0.543351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.093254</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.077221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>0.543351</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sex       Age  Survived\n",
       "Sex       1.000000 -0.093254  0.543351\n",
       "Age      -0.093254  1.000000 -0.077221\n",
       "Survived  0.543351 -0.077221  1.000000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e49eed8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "325d1f68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(correlation_data.corr(), annot = True, cmap = 'coolwarm',\n",
    "            vmin = -1, vmax = 1, annot_kws = {'size' : 16})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "543a1eae",
   "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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pclass                                               Name  \\\n",
       "PassengerId                                                              \n",
       "1                 3                            Braund, Mr. Owen Harris   \n",
       "2                 1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "3                 3                             Heikkinen, Miss. Laina   \n",
       "4                 1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "5                 3                           Allen, Mr. William Henry   \n",
       "\n",
       "                Sex   Age  SibSp  Parch            Ticket     Fare Cabin  \\\n",
       "PassengerId                                                                \n",
       "1              male  22.0      1      0         A/5 21171   7.2500   NaN   \n",
       "2            female  38.0      1      0          PC 17599  71.2833   C85   \n",
       "3            female  26.0      0      0  STON/O2. 3101282   7.9250   NaN   \n",
       "4            female  35.0      1      0            113803  53.1000  C123   \n",
       "5              male  35.0      0      0            373450   8.0500   NaN   \n",
       "\n",
       "            Embarked  Survived  \n",
       "PassengerId                     \n",
       "1                  S         0  \n",
       "2                  C         1  \n",
       "3                  S         1  \n",
       "4                  S         1  \n",
       "5                  S         0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_passengers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "65cd2dbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalization Pclass value\n",
    "titanic_passengers['Pclass'] = round(titanic_passengers['Pclass'] / 3, 2)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f416cdee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalization of Fare value\n",
    "titanic_passengers['Fare'] = round(titanic_passengers['Fare'] / titanic_passengers['Fare'].max(), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "7e2c08f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating new feature 'Rich'\n",
    "titanic_passengers['Rich'] = titanic_passengers['Fare'] * titanic_passengers['Pclass']\n",
    "titanic_passengers['Rich'] = round(titanic_passengers['Rich'] / titanic_passengers['Rich'].max(), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0116515d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Rich</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>0.014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>0.139</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>0.104</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0.104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>0.017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "      <td>0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.3</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>0.101</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>0.041</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>0.022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.7</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>0.059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>0.033</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.3</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>0.052</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>0.016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>0.061</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>0.015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.7</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>0.031</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>0.057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "      <td>0.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.7</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>0.025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>0.035</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>0.014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.7</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>0.051</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.7</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>0.025</td>\n",
       "      <td>D56</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.0</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>0.016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.3</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>0.069</td>\n",
       "      <td>A6</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0.069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>female</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>0.041</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>0.061</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0.203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>0.014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.3</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>0.513</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.0</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>0.015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>0.015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pclass                                               Name  \\\n",
       "PassengerId                                                              \n",
       "1               1.0                            Braund, Mr. Owen Harris   \n",
       "2               0.3  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "3               1.0                             Heikkinen, Miss. Laina   \n",
       "4               0.3       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "5               1.0                           Allen, Mr. William Henry   \n",
       "6               1.0                                   Moran, Mr. James   \n",
       "7               0.3                            McCarthy, Mr. Timothy J   \n",
       "8               1.0                     Palsson, Master. Gosta Leonard   \n",
       "9               1.0  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)   \n",
       "10              0.7                Nasser, Mrs. Nicholas (Adele Achem)   \n",
       "11              1.0                    Sandstrom, Miss. Marguerite Rut   \n",
       "12              0.3                           Bonnell, Miss. Elizabeth   \n",
       "13              1.0                     Saundercock, Mr. William Henry   \n",
       "14              1.0                        Andersson, Mr. Anders Johan   \n",
       "15              1.0               Vestrom, Miss. Hulda Amanda Adolfina   \n",
       "16              0.7                   Hewlett, Mrs. (Mary D Kingcome)    \n",
       "17              1.0                               Rice, Master. Eugene   \n",
       "18              0.7                       Williams, Mr. Charles Eugene   \n",
       "19              1.0  Vander Planke, Mrs. Julius (Emelia Maria Vande...   \n",
       "20              1.0                            Masselmani, Mrs. Fatima   \n",
       "21              0.7                               Fynney, Mr. Joseph J   \n",
       "22              0.7                              Beesley, Mr. Lawrence   \n",
       "23              1.0                        McGowan, Miss. Anna \"Annie\"   \n",
       "24              0.3                       Sloper, Mr. William Thompson   \n",
       "25              1.0                      Palsson, Miss. Torborg Danira   \n",
       "26              1.0  Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...   \n",
       "27              1.0                            Emir, Mr. Farred Chehab   \n",
       "28              0.3                     Fortune, Mr. Charles Alexander   \n",
       "29              1.0                      O'Dwyer, Miss. Ellen \"Nellie\"   \n",
       "30              1.0                                Todoroff, Mr. Lalio   \n",
       "\n",
       "                Sex   Age  SibSp  Parch            Ticket   Fare        Cabin  \\\n",
       "PassengerId                                                                     \n",
       "1              male  22.0      1      0         A/5 21171  0.014          NaN   \n",
       "2            female  38.0      1      0          PC 17599  0.139          C85   \n",
       "3            female  26.0      0      0  STON/O2. 3101282  0.015          NaN   \n",
       "4            female  35.0      1      0            113803  0.104         C123   \n",
       "5              male  35.0      0      0            373450  0.016          NaN   \n",
       "6              male   NaN      0      0            330877  0.017          NaN   \n",
       "7              male  54.0      0      0             17463  0.101          E46   \n",
       "8              male   2.0      3      1            349909  0.041          NaN   \n",
       "9            female  27.0      0      2            347742  0.022          NaN   \n",
       "10           female  14.0      1      0            237736  0.059          NaN   \n",
       "11           female   4.0      1      1           PP 9549  0.033           G6   \n",
       "12           female  58.0      0      0            113783  0.052         C103   \n",
       "13             male  20.0      0      0         A/5. 2151  0.016          NaN   \n",
       "14             male  39.0      1      5            347082  0.061          NaN   \n",
       "15           female  14.0      0      0            350406  0.015          NaN   \n",
       "16           female  55.0      0      0            248706  0.031          NaN   \n",
       "17             male   2.0      4      1            382652  0.057          NaN   \n",
       "18             male   NaN      0      0            244373  0.025          NaN   \n",
       "19           female  31.0      1      0            345763  0.035          NaN   \n",
       "20           female   NaN      0      0              2649  0.014          NaN   \n",
       "21             male  35.0      0      0            239865  0.051          NaN   \n",
       "22             male  34.0      0      0            248698  0.025          D56   \n",
       "23           female  15.0      0      0            330923  0.016          NaN   \n",
       "24             male  28.0      0      0            113788  0.069           A6   \n",
       "25           female   8.0      3      1            349909  0.041          NaN   \n",
       "26           female  38.0      1      5            347077  0.061          NaN   \n",
       "27             male   NaN      0      0              2631  0.014          NaN   \n",
       "28             male  19.0      3      2             19950  0.513  C23 C25 C27   \n",
       "29           female   NaN      0      0            330959  0.015          NaN   \n",
       "30             male   NaN      0      0            349216  0.015          NaN   \n",
       "\n",
       "            Embarked  Survived   Rich  \n",
       "PassengerId                            \n",
       "1                  S         0  0.047  \n",
       "2                  C         1  0.139  \n",
       "3                  S         1  0.050  \n",
       "4                  S         1  0.104  \n",
       "5                  S         0  0.053  \n",
       "6                  Q         0  0.057  \n",
       "7                  S         0  0.101  \n",
       "8                  S         0  0.137  \n",
       "9                  S         1  0.073  \n",
       "10                 C         1  0.138  \n",
       "11                 S         1  0.110  \n",
       "12                 S         1  0.052  \n",
       "13                 S         0  0.053  \n",
       "14                 S         0  0.203  \n",
       "15                 S         0  0.050  \n",
       "16                 S         1  0.072  \n",
       "17                 Q         0  0.190  \n",
       "18                 S         1  0.058  \n",
       "19                 S         0  0.117  \n",
       "20                 C         1  0.047  \n",
       "21                 S         0  0.119  \n",
       "22                 S         1  0.058  \n",
       "23                 Q         1  0.053  \n",
       "24                 S         1  0.069  \n",
       "25                 S         0  0.137  \n",
       "26                 S         1  0.203  \n",
       "27                 C         0  0.047  \n",
       "28                 S         0  0.513  \n",
       "29                 Q         1  0.050  \n",
       "30                 S         0  0.050  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_passengers.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "42e6045b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6406/3155867218.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_corr['Sex'] = (new_corr['Sex'] == 'female').astype(int)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Rich</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.139</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.050</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.104</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  Sex   Rich  Survived\n",
       "PassengerId                            \n",
       "1            22.0    0  0.047         0\n",
       "2            38.0    1  0.139         1\n",
       "3            26.0    1  0.050         1\n",
       "4            35.0    1  0.104         1\n",
       "5            35.0    0  0.053         0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_corr = titanic_passengers[['Age', 'Sex', 'Rich', 'Survived']]\n",
    "new_corr['Sex'] = (new_corr['Sex'] == 'female').astype(int)\n",
    "new_corr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "04135fd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Rich</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.093254</td>\n",
       "      <td>-0.057475</td>\n",
       "      <td>-0.077221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>-0.093254</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.186055</td>\n",
       "      <td>0.543351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rich</th>\n",
       "      <td>-0.057475</td>\n",
       "      <td>0.186055</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.172340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.077221</td>\n",
       "      <td>0.543351</td>\n",
       "      <td>0.172340</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age       Sex      Rich  Survived\n",
       "Age       1.000000 -0.093254 -0.057475 -0.077221\n",
       "Sex      -0.093254  1.000000  0.186055  0.543351\n",
       "Rich     -0.057475  0.186055  1.000000  0.172340\n",
       "Survived -0.077221  0.543351  0.172340  1.000000"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_corr.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "50aa6940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data = new_corr.corr(), cmap = 'coolwarm', vmin = -1, vmax = 1, annot = True)"
   ]
  }
 ],
 "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": "3.10.4"
  },
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}