Analyzing_iris.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_titanic = pd.read_csv(\"Dataset/titanic.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing and handling data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['sepal_length','sepal_width','petal_length','petal_width','class']\n",
    "df = pd.read_csv(\"Dataset\\iris (1).data\", header=None, names=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width        class\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform the calculation of mean, median, maximum, minimum, standard deviation for each numerical attribute in the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sepal_length  sepal_width  petal_length  petal_width\n",
       "mean      5.843333     3.054000      3.758667     1.198667\n",
       "std       0.828066     0.433594      1.764420     0.763161\n",
       "min       4.300000     2.000000      1.000000     0.100000\n",
       "max       7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table1 = df.describe().loc[['mean','std','min','max'],:]\n",
    "table1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_sepal_len = np.median(df.iloc[:,0])\n",
    "median_sepal_wid = np.median(df.iloc[:,1])\n",
    "median_petal_len = np.median(df.iloc[:,2])\n",
    "median_petal_wid = np.median(df.iloc[:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "median = pd.DataFrame(\n",
    "{\"sepal_length\" : [median_sepal_len],\n",
    "\"sepal_width\" : [median_sepal_wid],\n",
    "\"petal_length\" : [median_petal_len],\n",
    "\"petal_width\" : [median_petal_wid]},\n",
    "index=[\"median\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>5.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.35</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sepal_length  sepal_width  petal_length  petal_width\n",
       "median           5.8          3.0          4.35          1.3"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sepal_length  sepal_width  petal_length  petal_width\n",
       "mean        5.843333     3.054000      3.758667     1.198667\n",
       "std         0.828066     0.433594      1.764420     0.763161\n",
       "min         4.300000     2.000000      1.000000     0.100000\n",
       "max         7.900000     4.400000      6.900000     2.500000\n",
       "median      5.800000     3.000000      4.350000     1.300000"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfc = pd.concat([table1,median])\n",
    "dfc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot data from the chosen database in 2D.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfc.plot(kind=\"bar\",figsize=(15,10), grid=True, fontsize=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='sepal_length',y='sepal_width', figsize=(15,10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x='petal_length',y='petal_width', figsize=(15,10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for the presence of outliers (e.g., value greater than 3 times the base standard deviation) of the chosen database. If not, insert one or more lines to create outliers, and check again for the presence of outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfc.boxplot(figsize=(15,10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove the outliers and calculate the mean, median, mode, standard deviation for each numerical attribute in the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Desvio padrão:6.666243970162908\n",
      "Antes 150 150\n",
      "Depois 129 129\n"
     ]
    }
   ],
   "source": [
    "#Remove the outliers\n",
    "val = dfc.iloc[:,:4]\n",
    "std_all = []\n",
    "\n",
    "for i in val:\n",
    "    for j in val[i]:\n",
    "        std_all.append(j)\n",
    "        \n",
    "std_data = np.std(std_all)*3\n",
    "print(f'Desvio padrão:{std_data}')\n",
    "\n",
    "vet_val = df.iloc[:,0:4].values.tolist()\n",
    "vet_class = df.iloc[:,4].values.tolist()\n",
    "print('Antes', len(vet_val),len(vet_class))\n",
    "\n",
    "for i, v in enumerate(vet_val):\n",
    "    pop = False\n",
    "    for j in v:\n",
    "        if j > std_data:\n",
    "            pop = True\n",
    "    if pop == True:\n",
    "        del(vet_val[i])\n",
    "        del(vet_class[i])\n",
    "        pop = False\n",
    "print('Depois',len(vet_val),len(vet_class))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[5.1, 3.5, 1.4, 0.2]</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[4.9, 3.0, 1.4, 0.2]</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[4.7, 3.2, 1.3, 0.2]</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[4.6, 3.1, 1.5, 0.2]</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[5.0, 3.6, 1.4, 0.2]</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      0            1\n",
       "0  [5.1, 3.5, 1.4, 0.2]  Iris-setosa\n",
       "1  [4.9, 3.0, 1.4, 0.2]  Iris-setosa\n",
       "2  [4.7, 3.2, 1.3, 0.2]  Iris-setosa\n",
       "3  [4.6, 3.1, 1.5, 0.2]  Iris-setosa\n",
       "4  [5.0, 3.6, 1.4, 0.2]  Iris-setosa"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_outliers = pd.DataFrame(list(zip(vet_val,vet_class)))\n",
    "df_outliers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.637984</td>\n",
       "      <td>3.038760</td>\n",
       "      <td>3.451163</td>\n",
       "      <td>1.073643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.685815</td>\n",
       "      <td>0.452995</td>\n",
       "      <td>1.692913</td>\n",
       "      <td>0.738614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1         2         3\n",
       "mean  5.637984  3.038760  3.451163  1.073643\n",
       "std   0.685815  0.452995  1.692913  0.738614\n",
       "min   4.300000  2.000000  1.000000  0.100000\n",
       "max   7.700000  4.400000  6.900000  2.500000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_outliers.iloc[:,0]\n",
    "col0 = []\n",
    "col1 = []\n",
    "col2 = []\n",
    "col3 = []\n",
    "for i in x:\n",
    "    col0.append(i[0])\n",
    "    col1.append(i[1])\n",
    "    col2.append(i[2])\n",
    "    col3.append(i[3])\n",
    "\n",
    "matrix = pd.DataFrame([col0,col1,col2,col3])\n",
    "columns = [\"sepal_length\",\"sepal_width\",\"petal_length\",\"petal_width\"]\n",
    "matrix.transpose().describe().loc[['mean','std','min','max'],:]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load a new database that has categorical attributes, merge two dataframes (one for each database), and create a new column to indicate which dataframe/database each row belongs to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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>...</th>\n",
       "      <th>WikiId</th>\n",
       "      <th>Name_wiki</th>\n",
       "      <th>Age_wiki</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>Boarded</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Lifeboat</th>\n",
       "      <th>Body</th>\n",
       "      <th>Class</th>\n",
       "      <th>Part</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\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>...</td>\n",
       "      <td>691.0</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Bridgerule, Devon, England</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Qu'Appelle Valley, Saskatchewan, Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\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>...</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Cumings, Mrs. Florence Briggs (née Thayer)</td>\n",
       "      <td>35.0</td>\n",
       "      <td>New York, New York, US</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>New York, New York, US</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\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>...</td>\n",
       "      <td>865.0</td>\n",
       "      <td>Heikkinen, Miss Laina</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Jyväskylä, Finland</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>New York City</td>\n",
       "      <td>14?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\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>...</td>\n",
       "      <td>127.0</td>\n",
       "      <td>Futrelle, Mrs. Lily May (née Peel)</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Scituate, Massachusetts, US</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Scituate, Massachusetts, US</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\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>...</td>\n",
       "      <td>627.0</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Birmingham, West Midlands, England</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>New York City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1       0.0       3   \n",
       "1            2       1.0       1   \n",
       "2            3       1.0       3   \n",
       "3            4       1.0       1   \n",
       "4            5       0.0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare  ... WikiId  \\\n",
       "0      0         A/5 21171   7.2500  ...  691.0   \n",
       "1      0          PC 17599  71.2833  ...   90.0   \n",
       "2      0  STON/O2. 3101282   7.9250  ...  865.0   \n",
       "3      0            113803  53.1000  ...  127.0   \n",
       "4      0            373450   8.0500  ...  627.0   \n",
       "\n",
       "                                    Name_wiki  Age_wiki  \\\n",
       "0                     Braund, Mr. Owen Harris      22.0   \n",
       "1  Cumings, Mrs. Florence Briggs (née Thayer)      35.0   \n",
       "2                       Heikkinen, Miss Laina      26.0   \n",
       "3          Futrelle, Mrs. Lily May (née Peel)      35.0   \n",
       "4                    Allen, Mr. William Henry      35.0   \n",
       "\n",
       "                             Hometown      Boarded  \\\n",
       "0          Bridgerule, Devon, England  Southampton   \n",
       "1              New York, New York, US    Cherbourg   \n",
       "2                  Jyväskylä, Finland  Southampton   \n",
       "3         Scituate, Massachusetts, US  Southampton   \n",
       "4  Birmingham, West Midlands, England  Southampton   \n",
       "\n",
       "                               Destination Lifeboat Body Class Part  \n",
       "0  Qu'Appelle Valley, Saskatchewan, Canada      NaN  NaN   3.0    1  \n",
       "1                   New York, New York, US        4  NaN   1.0    1  \n",
       "2                            New York City      14?  NaN   3.0    1  \n",
       "3              Scituate, Massachusetts, US        D  NaN   1.0    1  \n",
       "4                            New York City      NaN  NaN   3.0    1  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "half = len(df_titanic)//2\n",
    "df_titanic1 = df_titanic.iloc[:half,:]\n",
    "df_titanic1 = df_titanic1.assign(Part = [1 for x in range(0,len(df_titanic1))])\n",
    "df_titanic2 = df_titanic.iloc[half:,:]\n",
    "df_titanic2 = df_titanic2.assign(Part = [2 for x in range(0,len(df_titanic2))])\n",
    "\n",
    "df_titanic_concat = pd.concat((df_titanic1,df_titanic2), axis=0)\n",
    "df_titanic_concat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If the new database is 'titanic': select and show the number of passengers on the titanic who are over 27 years old and who survived the accident.If it is another database: choose a filtering condition.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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>...</th>\n",
       "      <th>WikiId</th>\n",
       "      <th>Name_wiki</th>\n",
       "      <th>Age_wiki</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>Boarded</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Lifeboat</th>\n",
       "      <th>Body</th>\n",
       "      <th>Class</th>\n",
       "      <th>Part</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\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>...</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Cumings, Mrs. Florence Briggs (née Thayer)</td>\n",
       "      <td>35.0</td>\n",
       "      <td>New York, New York, US</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>New York, New York, US</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\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>...</td>\n",
       "      <td>127.0</td>\n",
       "      <td>Futrelle, Mrs. Lily May (née Peel)</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Scituate, Massachusetts, US</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Scituate, Massachusetts, US</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</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>26.5500</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Bonnell, Miss Elizabeth</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Youngstown, Ohio, US</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Youngstown, Ohio, US</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</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>16.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>446.0</td>\n",
       "      <td>Hewlett, Mrs. Mary Dunbar (née Kingcome)</td>\n",
       "      <td>56.0</td>\n",
       "      <td>Lucknow, British India, UK[note 3]</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Rapid City, South Dakota, US</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</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>13.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>346.0</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>34.0</td>\n",
       "      <td>London, England, UK</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Toronto, Ontario, Canada</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "1             2       1.0       1   \n",
       "3             4       1.0       1   \n",
       "11           12       1.0       1   \n",
       "15           16       1.0       2   \n",
       "21           22       1.0       2   \n",
       "\n",
       "                                                 Name     Sex   Age  SibSp  \\\n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "15                   Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "21                              Beesley, Mr. Lawrence    male  34.0      0   \n",
       "\n",
       "    Parch    Ticket     Fare  ... WikiId  \\\n",
       "1       0  PC 17599  71.2833  ...   90.0   \n",
       "3       0    113803  53.1000  ...  127.0   \n",
       "11      0    113783  26.5500  ...   35.0   \n",
       "15      0    248706  16.0000  ...  446.0   \n",
       "21      0    248698  13.0000  ...  346.0   \n",
       "\n",
       "                                     Name_wiki  Age_wiki  \\\n",
       "1   Cumings, Mrs. Florence Briggs (née Thayer)      35.0   \n",
       "3           Futrelle, Mrs. Lily May (née Peel)      35.0   \n",
       "11                     Bonnell, Miss Elizabeth      61.0   \n",
       "15    Hewlett, Mrs. Mary Dunbar (née Kingcome)      56.0   \n",
       "21                       Beesley, Mr. Lawrence      34.0   \n",
       "\n",
       "                              Hometown      Boarded  \\\n",
       "1               New York, New York, US    Cherbourg   \n",
       "3          Scituate, Massachusetts, US  Southampton   \n",
       "11                Youngstown, Ohio, US  Southampton   \n",
       "15  Lucknow, British India, UK[note 3]  Southampton   \n",
       "21                 London, England, UK  Southampton   \n",
       "\n",
       "                     Destination Lifeboat Body Class Part  \n",
       "1         New York, New York, US        4  NaN   1.0    1  \n",
       "3    Scituate, Massachusetts, US        D  NaN   1.0    1  \n",
       "11          Youngstown, Ohio, US        8  NaN   1.0    1  \n",
       "15  Rapid City, South Dakota, US       13  NaN   2.0    1  \n",
       "21      Toronto, Ontario, Canada       13  NaN   2.0    1  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_query = df_titanic_concat.query('Age > 27 and Survived == 1 ')\n",
    "df_query.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If the new database is ‘titanic’: transform the continuous titanic age data into categorical data according to a rule.\n",
    "* 0 to 18 years old: child\n",
    "* 18 to 60 years old: adult\n",
    "* greater than 60: elderly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_titanic_values = df_titanic_concat.values.tolist()\n",
    "for i, v in enumerate(df_titanic_values):\n",
    "    if v[5] <= 2:\n",
    "        df_titanic_values[i][5] = \"Baby\"\n",
    "    elif v[5] > 2 and v[5] <= 12:\n",
    "        df_titanic_values[i][5] = \"Children\"\n",
    "    elif v[5] > 12 and v[5] <= 18:\n",
    "        df_titanic_values[i][5] = \"Young\"\n",
    "    elif v[5] > 18 and v[5] <= 65:\n",
    "        df_titanic_values[i][5] = \"Adult\"\n",
    "    elif v[5] > 65:\n",
    "        df_titanic_values[i][5] = \"Old man\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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>...</th>\n",
       "      <th>WikiId</th>\n",
       "      <th>Name_wiki</th>\n",
       "      <th>Age_wiki</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>Boarded</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Lifeboat</th>\n",
       "      <th>Body</th>\n",
       "      <th>Class</th>\n",
       "      <th>Part</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>Adult</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>...</td>\n",
       "      <td>691.0</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Bridgerule, Devon, England</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Qu'Appelle Valley, Saskatchewan, Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>Adult</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>...</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Cumings, Mrs. Florence Briggs (née Thayer)</td>\n",
       "      <td>35.0</td>\n",
       "      <td>New York, New York, US</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>New York, New York, US</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>Adult</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>...</td>\n",
       "      <td>865.0</td>\n",
       "      <td>Heikkinen, Miss Laina</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Jyväskylä, Finland</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>New York City</td>\n",
       "      <td>14?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>Adult</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>...</td>\n",
       "      <td>127.0</td>\n",
       "      <td>Futrelle, Mrs. Lily May (née Peel)</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Scituate, Massachusetts, US</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>Scituate, Massachusetts, US</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>Adult</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>...</td>\n",
       "      <td>627.0</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Birmingham, West Midlands, England</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>New York City</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1       0.0       3   \n",
       "1            2       1.0       1   \n",
       "2            3       1.0       3   \n",
       "3            4       1.0       1   \n",
       "4            5       0.0       3   \n",
       "\n",
       "                                                Name     Sex    Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  Adult      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  Adult      1   \n",
       "2                             Heikkinen, Miss. Laina  female  Adult      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  Adult      1   \n",
       "4                           Allen, Mr. William Henry    male  Adult      0   \n",
       "\n",
       "   Parch            Ticket     Fare  ... WikiId  \\\n",
       "0      0         A/5 21171   7.2500  ...  691.0   \n",
       "1      0          PC 17599  71.2833  ...   90.0   \n",
       "2      0  STON/O2. 3101282   7.9250  ...  865.0   \n",
       "3      0            113803  53.1000  ...  127.0   \n",
       "4      0            373450   8.0500  ...  627.0   \n",
       "\n",
       "                                    Name_wiki  Age_wiki  \\\n",
       "0                     Braund, Mr. Owen Harris      22.0   \n",
       "1  Cumings, Mrs. Florence Briggs (née Thayer)      35.0   \n",
       "2                       Heikkinen, Miss Laina      26.0   \n",
       "3          Futrelle, Mrs. Lily May (née Peel)      35.0   \n",
       "4                    Allen, Mr. William Henry      35.0   \n",
       "\n",
       "                             Hometown      Boarded  \\\n",
       "0          Bridgerule, Devon, England  Southampton   \n",
       "1              New York, New York, US    Cherbourg   \n",
       "2                  Jyväskylä, Finland  Southampton   \n",
       "3         Scituate, Massachusetts, US  Southampton   \n",
       "4  Birmingham, West Midlands, England  Southampton   \n",
       "\n",
       "                               Destination Lifeboat Body Class Part  \n",
       "0  Qu'Appelle Valley, Saskatchewan, Canada      NaN  NaN   3.0    1  \n",
       "1                   New York, New York, US        4  NaN   1.0    1  \n",
       "2                            New York City      14?  NaN   3.0    1  \n",
       "3              Scituate, Massachusetts, US        D  NaN   1.0    1  \n",
       "4                            New York City      NaN  NaN   3.0    1  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(df_titanic_values, columns=['PassengerId','Survived','Pclass','Name','Sex','Age','SibSp','Parch','Ticket','Fare','Cabin','Embarked','WikiId','Name_wiki','Age_wiki','Hometown','Boarded','Destination','Lifeboat','Body','Class','Part'])           \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "28fed176da9af2ea6f007930910871573777e27f84472596c10e5707560e7b41"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit ('ai': conda)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}