Data_ScienceUse_Cases/misc/Generation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3a904562",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b20a5ceb",
   "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>Column_1</th>\n",
       "      <th>Column_2</th>\n",
       "      <th>Column_3</th>\n",
       "      <th>Column_4</th>\n",
       "      <th>Column_5</th>\n",
       "      <th>Column_6</th>\n",
       "      <th>Column_7</th>\n",
       "      <th>Column_8</th>\n",
       "      <th>Column_9</th>\n",
       "      <th>Column_10</th>\n",
       "      <th>Column_11</th>\n",
       "      <th>Column_12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column_1  Column_2  Column_3  Column_4  Column_5  Column_6  Column_7  \\\n",
       "0         5         3         4         3         3         2         3   \n",
       "1         3         2         4         5         3         2         2   \n",
       "2         4         4         3         2         5         4         3   \n",
       "3         3         4         4         2         1         2         5   \n",
       "4         4         4         2         2         4         3         2   \n",
       "\n",
       "   Column_8  Column_9  Column_10  Column_11  Column_12  \n",
       "0         1         4          4          3          4  \n",
       "1         3         4          3          1          4  \n",
       "2         4         4          3          3          3  \n",
       "3         3         3          2          4          5  \n",
       "4         4         3          4          1          2  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generating random data with a normal distribution\n",
    "data = np.random.normal(loc=3, scale=1, size=(500, 12))  # mean=3, standard deviation=1\n",
    "\n",
    "# Rounding the values and ensuring they are between 1 and 5\n",
    "data = np.round(data)\n",
    "data[data < 1] = 1\n",
    "data[data > 5] = 5\n",
    "\n",
    "# Converting to integers\n",
    "data = data.astype(int)\n",
    "\n",
    "# Creating a DataFrame\n",
    "df = pd.DataFrame(data, columns=[f'Column_{i}' for i in range(1, 13)])\n",
    "\n",
    "# Displaying the DataFrame\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b558e59",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Setting up the subplots\n",
    "fig, axes = plt.subplots(3, 4, figsize=(15, 10))\n",
    "fig.suptitle('Histograms for Each Column')\n",
    "\n",
    "# Visualizing/histogram for each column\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    column = df.columns[i]\n",
    "    ax.hist(df[column], bins=[1, 2, 3, 4, 5, 6], alpha=0.5, edgecolor='black')\n",
    "    ax.set_title(f'{column}')\n",
    "    ax.set_xlabel('Value')\n",
    "    ax.set_ylabel('Frequency')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dfa7fe98",
   "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>Column_1</th>\n",
       "      <th>Column_2</th>\n",
       "      <th>Column_3</th>\n",
       "      <th>Column_4</th>\n",
       "      <th>Column_5</th>\n",
       "      <th>Column_6</th>\n",
       "      <th>Column_7</th>\n",
       "      <th>Column_8</th>\n",
       "      <th>Column_9</th>\n",
       "      <th>Column_10</th>\n",
       "      <th>Column_11</th>\n",
       "      <th>Column_12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column_1  Column_2  Column_3  Column_4  Column_5  Column_6  Column_7  \\\n",
       "0         5         3         4         3         2         1         3   \n",
       "1         3         2         4         5         2         1         2   \n",
       "2         4         4         3         2         4         3         3   \n",
       "3         3         4         4         2         0         1         5   \n",
       "4         4         4         2         2         3         2         2   \n",
       "\n",
       "   Column_8  Column_9  Column_10  Column_11  Column_12  \n",
       "0         1         4          4          2          4  \n",
       "1         3         4          3          0          4  \n",
       "2         4         4          3          2          3  \n",
       "3         3         3          2          3          5  \n",
       "4         4         3          4          0          2  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting random columns\n",
    "skew_left = np.random.choice(df.columns, 3, replace=False)\n",
    "\n",
    "# Introducing skewness to the selected columns\n",
    "for column in skew_left:\n",
    "    skewness_factor = np.random.uniform(0.1, 0.5)  # Random skewness factor between 0.1 and 0.5\n",
    "    df[column] -= int(skewness_factor * 4)  # Shifting values towards 1\n",
    "\n",
    "# Displaying the modified DataFrame\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb2aabc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting up the subplots\n",
    "fig, axes = plt.subplots(3, 4, figsize=(15, 10))\n",
    "fig.suptitle('Histograms for Each Column')\n",
    "\n",
    "# Visualizing/histogram for each column\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    column = df.columns[i]\n",
    "    ax.hist(df[column], bins=[1, 2, 3, 4, 5, 6], alpha=0.5, edgecolor='black')\n",
    "    ax.set_title(f'{column}')\n",
    "    ax.set_xlabel('Value')\n",
    "    ax.set_ylabel('Frequency')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cebcf6cb",
   "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>Column_1</th>\n",
       "      <th>Column_2</th>\n",
       "      <th>Column_3</th>\n",
       "      <th>Column_4</th>\n",
       "      <th>Column_5</th>\n",
       "      <th>Column_6</th>\n",
       "      <th>Column_7</th>\n",
       "      <th>Column_8</th>\n",
       "      <th>Column_9</th>\n",
       "      <th>Column_10</th>\n",
       "      <th>Column_11</th>\n",
       "      <th>Column_12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column_1  Column_2  Column_3  Column_4  Column_5  Column_6  Column_7  \\\n",
       "0         5         3         4         4         2         1         3   \n",
       "1         3         2         4         6         2         1         2   \n",
       "2         4         4         3         3         4         3         3   \n",
       "3         3         4         4         3         0         1         5   \n",
       "4         4         4         2         3         3         2         2   \n",
       "\n",
       "   Column_8  Column_9  Column_10  Column_11  Column_12  \n",
       "0         1         4          4          2          4  \n",
       "1         3         4          3          0          4  \n",
       "2         4         4          3          2          3  \n",
       "3         3         3          2          3          5  \n",
       "4         4         3          4          0          2  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting random columns for right skewness, excluding the ones already skewed left\n",
    "skew_right = np.random.choice([col for col in df.columns if col not in skew_left], 2, replace=False)\n",
    "\n",
    "# Introducing skewness to the selected columns\n",
    "for column in skew_right:\n",
    "    skewness_factor = np.random.uniform(0.1, 0.5)  # Random skewness factor between 0.1 and 0.5\n",
    "    df[column] += int(skewness_factor * 4)  # Shifting values towards 5\n",
    "\n",
    "# Displaying the modified DataFrame\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "69a10ec6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting up the subplots\n",
    "fig, axes = plt.subplots(3, 4, figsize=(15, 10))\n",
    "fig.suptitle('Histograms for Each Column')\n",
    "\n",
    "# Visualizing/histogram for each column\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    column = df.columns[i]\n",
    "    ax.hist(df[column], bins=[1, 2, 3, 4, 5, 6], alpha=0.5, edgecolor='black')\n",
    "    ax.set_title(f'{column}')\n",
    "    ax.set_xlabel('Value')\n",
    "    ax.set_ylabel('Frequency')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "50833ea0",
   "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>Column_1</th>\n",
       "      <th>Column_2</th>\n",
       "      <th>Column_3</th>\n",
       "      <th>Column_4</th>\n",
       "      <th>Column_5</th>\n",
       "      <th>Column_6</th>\n",
       "      <th>Column_7</th>\n",
       "      <th>Column_8</th>\n",
       "      <th>Column_9</th>\n",
       "      <th>Column_10</th>\n",
       "      <th>Column_11</th>\n",
       "      <th>Column_12</th>\n",
       "      <th>Staff_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>SA75310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>SP54242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>SA54434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>MA69977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>SA59502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column_1  Column_2  Column_3  Column_4  Column_5  Column_6  Column_7  \\\n",
       "0         5         3         4         4         2         1         3   \n",
       "1         3         2         4         6         2         1         2   \n",
       "2         4         4         3         3         4         3         3   \n",
       "3         3         4         4         3         0         1         5   \n",
       "4         4         4         2         3         3         2         2   \n",
       "\n",
       "   Column_8  Column_9  Column_10  Column_11  Column_12 Staff_Id  \n",
       "0         1         4          4          2          4  SA75310  \n",
       "1         3         4          3          0          4  SP54242  \n",
       "2         4         4          3          2          3  SA54434  \n",
       "3         3         3          2          3          5  MA69977  \n",
       "4         4         3          4          0          2  SA59502  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "# Function to generate staff ID\n",
    "def generate_staff_id():\n",
    "    level_codes = ['DR'] * 3 + ['MA'] * 50 + ['SP'] * 75 + ['SA'] * 372  # Level codes distribution\n",
    "    level_code = random.choice(level_codes)  # Randomly choose a level code\n",
    "    random_numbers = ''.join(str(random.randint(0, 9)) for _ in range(5))  # Generate 5 random numbers\n",
    "    return f\"{level_code}{random_numbers}\"\n",
    "\n",
    "# Add \"Staff_Id\" column to DataFrame\n",
    "df['Staff_Id'] = [generate_staff_id() for _ in range(500)]\n",
    "\n",
    "# Display the DataFrame with the new \"Staff_Id\" columns\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "268636d1",
   "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>Column_1</th>\n",
       "      <th>Column_2</th>\n",
       "      <th>Column_3</th>\n",
       "      <th>Column_4</th>\n",
       "      <th>Column_5</th>\n",
       "      <th>Column_6</th>\n",
       "      <th>Column_7</th>\n",
       "      <th>Column_8</th>\n",
       "      <th>Column_9</th>\n",
       "      <th>Column_10</th>\n",
       "      <th>Column_11</th>\n",
       "      <th>Column_12</th>\n",
       "      <th>Staff_Id</th>\n",
       "      <th>Month_Of_Service</th>\n",
       "      <th>Years_Of_Service</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>SA75310</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>SP54242</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>SA54434</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>MA69977</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>SA59502</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column_1  Column_2  Column_3  Column_4  Column_5  Column_6  Column_7  \\\n",
       "0         5         3         4         4         2         1         3   \n",
       "1         3         2         4         6         2         1         2   \n",
       "2         4         4         3         3         4         3         3   \n",
       "3         3         4         4         3         0         1         5   \n",
       "4         4         4         2         3         3         2         2   \n",
       "\n",
       "   Column_8  Column_9  Column_10  Column_11  Column_12 Staff_Id  \\\n",
       "0         1         4          4          2          4  SA75310   \n",
       "1         3         4          3          0          4  SP54242   \n",
       "2         4         4          3          2          3  SA54434   \n",
       "3         3         3          2          3          5  MA69977   \n",
       "4         4         3          4          0          2  SA59502   \n",
       "\n",
       "   Month_Of_Service  Years_Of_Service  \n",
       "0                50                 4  \n",
       "1                34                 2  \n",
       "2                17                 1  \n",
       "3                 7                 0  \n",
       "4                52                 4  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generating random values for Month_Of_Service\n",
    "df['Month_Of_Service'] = [random.randint(0, 66) for _ in range(500)]  # 66 months = 5 years 6 months\n",
    "\n",
    "# Generating Years_Of_Service based on Month_Of_Service\n",
    "df['Years_Of_Service'] = df['Month_Of_Service'] // 12  # Integer division to get years\n",
    "\n",
    "# Adjusting Years_Of_Service for people with less than a year of service\n",
    "df.loc[df['Years_Of_Service'] == 5, 'Years_Of_Service'] = 4\n",
    "\n",
    "# Displaying the DataFrame with the new columns\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "73aeb01d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column_1</th>\n",
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       "      <th>Column_3</th>\n",
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       "      <th>Column_10</th>\n",
       "      <th>Column_11</th>\n",
       "      <th>Column_12</th>\n",
       "      <th>Staff_Id</th>\n",
       "      <th>Month_Of_Service</th>\n",
       "      <th>Years_Of_Service</th>\n",
       "      <th>Residence</th>\n",
       "      <th>Residence_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
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       "      <td>2</td>\n",
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       "      <td>Jakarta</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>SP54242</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>SA54434</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>MA69977</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>Tangerang</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>SA59502</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "      <td>Depok</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column_1  Column_2  Column_3  Column_4  Column_5  Column_6  Column_7  \\\n",
       "0         5         3         4         4         2         1         3   \n",
       "1         3         2         4         6         2         1         2   \n",
       "2         4         4         3         3         4         3         3   \n",
       "3         3         4         4         3         0         1         5   \n",
       "4         4         4         2         3         3         2         2   \n",
       "\n",
       "   Column_8  Column_9  Column_10  Column_11  Column_12 Staff_Id  \\\n",
       "0         1         4          4          2          4  SA75310   \n",
       "1         3         4          3          0          4  SP54242   \n",
       "2         4         4          3          2          3  SA54434   \n",
       "3         3         3          2          3          5  MA69977   \n",
       "4         4         3          4          0          2  SA59502   \n",
       "\n",
       "   Month_Of_Service  Years_Of_Service  Residence  Residence_Code  \n",
       "0                50                 4    Jakarta               1  \n",
       "1                34                 2    Jakarta               1  \n",
       "2                17                 1    Jakarta               1  \n",
       "3                 7                 0  Tangerang               2  \n",
       "4                52                 4      Depok               4  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the possible residence locations\n",
    "residence_locations = ['Jakarta', 'Tangerang', 'Bekasi', 'Depok', 'Bogor']\n",
    "\n",
    "# Generating random values for Residence\n",
    "df['Residence'] = [random.choice(residence_locations) for _ in range(500)]\n",
    "\n",
    "# Creating Residence_Code based on Residence\n",
    "residence_mapping = {location: i+1 for i, location in enumerate(residence_locations)}\n",
    "df['Residence_Code'] = df['Residence'].map(residence_mapping)\n",
    "\n",
    "# Displaying the DataFrame with the new columns\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39e7083a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>Column_1</th>\n",
       "      <th>Column_2</th>\n",
       "      <th>Column_3</th>\n",
       "      <th>Column_4</th>\n",
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       "      <th>Month_Of_Service</th>\n",
       "      <th>Years_Of_Service</th>\n",
       "      <th>Residence</th>\n",
       "      <th>Residence_Code</th>\n",
       "      <th>Net_Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>SA75310</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>1</td>\n",
       "      <td>6504819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>SP54242</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>1</td>\n",
       "      <td>9050238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>SA54434</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>1</td>\n",
       "      <td>5485486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>MA69977</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>Tangerang</td>\n",
       "      <td>2</td>\n",
       "      <td>19505881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>SA59502</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "      <td>Depok</td>\n",
       "      <td>4</td>\n",
       "      <td>5633594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Column_1  Column_2  Column_3  Column_4  Column_5  Column_6  Column_7  \\\n",
       "0         5         3         4         4         2         1         3   \n",
       "1         3         2         4         6         2         1         2   \n",
       "2         4         4         3         3         4         3         3   \n",
       "3         3         4         4         3         0         1         5   \n",
       "4         4         4         2         3         3         2         2   \n",
       "\n",
       "   Column_8  Column_9  Column_10  Column_11  Column_12 Staff_Id  \\\n",
       "0         1         4          4          2          4  SA75310   \n",
       "1         3         4          3          0          4  SP54242   \n",
       "2         4         4          3          2          3  SA54434   \n",
       "3         3         3          2          3          5  MA69977   \n",
       "4         4         3          4          0          2  SA59502   \n",
       "\n",
       "   Month_Of_Service  Years_Of_Service  Residence  Residence_Code  Net_Salary  \n",
       "0                50                 4    Jakarta               1     6504819  \n",
       "1                34                 2    Jakarta               1     9050238  \n",
       "2                17                 1    Jakarta               1     5485486  \n",
       "3                 7                 0  Tangerang               2    19505881  \n",
       "4                52                 4      Depok               4     5633594  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define salary ranges for each staff level\n",
    "salary_ranges = {'SA': (5070000, 7004030),  # Salary range for Staff (SA)\n",
    "                 'SP': (8100075, 10240060), # Salary range for Supervisor (SP)\n",
    "                 'MA': (15562000, 21053011), # Salary range for Manager (MA)\n",
    "                 'DR': (53010000, 55020000)} # Salary range for Director (DR)\n",
    "\n",
    "# Function to generate net salary based on staff level\n",
    "def generate_net_salary(level_code):\n",
    "    lower_bound, upper_bound = salary_ranges[level_code]\n",
    "    return random.randint(lower_bound, upper_bound)\n",
    "\n",
    "# Add \"Net_Salary\" column to DataFrame\n",
    "df['Net_Salary'] = [generate_net_salary(staff_id[:2]) for staff_id in df['Staff_Id']]\n",
    "\n",
    "# Display the DataFrame with the new \"Net_Salary\" column\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8861e640",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Staff_Id\n",
      "DR    54434974.0\n",
      "MA    18732872.0\n",
      "SA     5966061.0\n",
      "SP     9149982.0\n",
      "Name: Net_Salary, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Grouping by staff level and calculating median net salary\n",
    "median_salary_by_level = df.groupby(df['Staff_Id'].str[:2])['Net_Salary'].median()\n",
    "\n",
    "# Displaying the median net salary for each staff level\n",
    "print(median_salary_by_level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a04382c5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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