DataScienceAndBigDataAnalytics/Notebooks/Notebook-A9 (Data Visualization-2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c2cafe5e-1c33-4e2e-a1c7-276f6826d02b",
   "metadata": {},
   "source": [
    "# Notebook-A9 (Data Visualization-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b20e46b3-fff9-49bb-a7ab-0730fe4527bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0f724ef2-8e1e-4f8c-bc97-4e29379434e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load built-in dataset\n",
    "df= sns.load_dataset('titanic')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2cac59fd-e73e-4587-ab2c-e3cf04dc9532",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         survived      pclass         age       sibsp       parch        fare\n",
      "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
      "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
      "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
      "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
      "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
      "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
      "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
      "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200\n",
      "          count       mean        std   min      25%      50%   75%       max\n",
      "survived  891.0   0.383838   0.486592  0.00   0.0000   0.0000   1.0    1.0000\n",
      "pclass    891.0   2.308642   0.836071  1.00   2.0000   3.0000   3.0    3.0000\n",
      "age       714.0  29.699118  14.526497  0.42  20.1250  28.0000  38.0   80.0000\n",
      "sibsp     891.0   0.523008   1.102743  0.00   0.0000   0.0000   1.0    8.0000\n",
      "parch     891.0   0.381594   0.806057  0.00   0.0000   0.0000   0.0    6.0000\n",
      "fare      891.0  32.204208  49.693429  0.00   7.9104  14.4542  31.0  512.3292\n"
     ]
    }
   ],
   "source": [
    "# Describe\n",
    "print(df.describe())\n",
    "# Describe - transposed, i.e. rows and columns swapped\n",
    "print(df.describe().transpose())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ae603f12-36f5-4f98-ba0c-14a9c4b6a4b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean age is: 29.69911764705882\n",
      "The median age is: 28.0\n",
      "The mode age is: 24.0\n"
     ]
    }
   ],
   "source": [
    "# Mean, median, mode\n",
    "age_data = df['age'].dropna() # Drop missing values in age & store in age_data var\n",
    "\n",
    "sorted_age_data = sorted(age_data) # Store sorted age_data\n",
    "n = len(sorted_age_data) # Store length of age_data\n",
    "\n",
    "# Calculate mean\n",
    "mean_age = sum(age_data) / len(age_data)\n",
    "\n",
    "# Calculate median\n",
    "if n % 2 == 1:  # odd\n",
    "    median_age = sorted_age_data[n // 2]\n",
    "else:  # even\n",
    "    median_age = (sorted_age_data[n // 2 - 1] + sorted_age_data[n // 2]) / 2\n",
    "\n",
    "# Calculate mode\n",
    "age_counts = Counter(age_data)  # Count occurrences of each age\n",
    "mode_age = age_counts.most_common(1)[0][0]  # Get the most common value\n",
    "\n",
    "# Print\n",
    "print(f\"The mean age is: {mean_age}\")\n",
    "print(f\"The median age is: {median_age}\")\n",
    "print(f\"The mode age is: {mode_age}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9d8b90a2-2dc4-4863-8e1c-d86f12d3292d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot\n",
    "plt.figure(figsize=(8,4)) # 8 by 4 inches\n",
    "sns.boxplot(x=\"sex\", y=\"age\", hue=\"survived\", data= df, palette=\"viridis\")\n",
    "plt.title(\"Distribution of age with respect to each gender and survival Status\")\n",
    "plt.xlabel(\"Sex\")\n",
    "plt.ylabel(\"Age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a1136cdb-5017-409d-9c9c-38fc6cd389cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sex', ylabel='age'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Violin plot\n",
    "sns.violinplot(x='sex',y='age',data=df, hue= 'survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "98d8af0f-e26f-4daf-9d2a-2ffe19478010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x725c785ce640>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 570.486x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Catplot\n",
    "sns.catplot(x=\"sex\", hue=\"survived\", data=df, kind=\"count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73a704aa-2162-4a76-b709-31713e2c4ca5",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "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": "3.8.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}