Data_ScienceUse_Cases/Classification/Data/Generation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3a904562",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</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         3         4         5         2         2         3         3   \n",
       "1         2         3         1         2         4         3         4   \n",
       "2         3         3         2         2         2         5         4   \n",
       "3         3         3         4         4         3         1         4   \n",
       "4         3         2         4         3         3         2         3   \n",
       "\n",
       "   Column_8  Column_9  Column_10  Column_11  Column_12  \n",
       "0         3         2          3          4          3  \n",
       "1         3         2          2          2          3  \n",
       "2         4         3          2          4          4  \n",
       "3         4         4          5          3          4  \n",
       "4         4         2          3          2          2  "
      ]
     },
     "execution_count": 2,
     "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": 4,
   "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": [
    "# 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": 5,
   "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>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</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         3         4         5         2         2         3         3   \n",
       "1         2         3         1         2         4         3         4   \n",
       "2         3         3         2         2         2         5         4   \n",
       "3         3         3         4         4         3         1         4   \n",
       "4         3         2         4         3         3         2         3   \n",
       "\n",
       "   Column_8  Column_9  Column_10  Column_11  Column_12  \n",
       "0         3         2          3          4          3  \n",
       "1         3         2          2          2          3  \n",
       "2         4         3          2          4          4  \n",
       "3         4         4          5          3          4  \n",
       "4         4         2          3          2          2  "
      ]
     },
     "execution_count": 5,
     "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": 6,
   "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": 7,
   "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>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</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         3         4         5         2         2         3         3   \n",
       "1         2         3         1         2         4         3         4   \n",
       "2         3         3         2         2         2         5         4   \n",
       "3         3         3         4         4         3         1         4   \n",
       "4         3         2         4         3         3         2         3   \n",
       "\n",
       "   Column_8  Column_9  Column_10  Column_11  Column_12  \n",
       "0         3         2          3          4          4  \n",
       "1         3         2          2          2          4  \n",
       "2         4         3          2          4          5  \n",
       "3         4         4          5          3          5  \n",
       "4         4         2          3          2          3  "
      ]
     },
     "execution_count": 7,
     "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": 8,
   "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": 9,
   "id": "7add2a67",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>SA63171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>SP10211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>SA79627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>SA02310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>SA98565</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         3         4         5         2         2         3         3   \n",
       "1         2         3         1         2         4         3         4   \n",
       "2         3         3         2         2         2         5         4   \n",
       "3         3         3         4         4         3         1         4   \n",
       "4         3         2         4         3         3         2         3   \n",
       "\n",
       "   Column_8  Column_9  Column_10  Column_11  Column_12 Staff_Id  \n",
       "0         3         2          3          4          4  SA63171  \n",
       "1         3         2          2          2          4  SP10211  \n",
       "2         4         3          2          4          5  SA79627  \n",
       "3         4         4          5          3          5  SA02310  \n",
       "4         4         2          3          2          3  SA98565  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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": 11,
   "id": "268636d1",
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column_1</th>\n",
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       "      <td>0</td>\n",
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       "      <td>4</td>\n",
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       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>SA02310</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>SA98565</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "   Column_1  Column_2  Column_3  Column_4  Column_5  Column_6  Column_7  \\\n",
       "0         3         4         5         2         2         3         3   \n",
       "1         2         3         1         2         4         3         4   \n",
       "2         3         3         2         2         2         5         4   \n",
       "3         3         3         4         4         3         1         4   \n",
       "4         3         2         4         3         3         2         3   \n",
       "\n",
       "   Column_8  Column_9  Column_10  Column_11  Column_12 Staff_Id  \\\n",
       "0         3         2          3          4          4  SA63171   \n",
       "1         3         2          2          2          4  SP10211   \n",
       "2         4         3          2          4          5  SA79627   \n",
       "3         4         4          5          3          5  SA02310   \n",
       "4         4         2          3          2          3  SA98565   \n",
       "\n",
       "   Month_Of_Service  Years_Of_Service  \n",
       "0                 1                 0  \n",
       "1                43                 3  \n",
       "2                10                 0  \n",
       "3                17                 1  \n",
       "4                17                 1  "
      ]
     },
     "execution_count": 11,
     "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": 12,
   "id": "73aeb01d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4</td>\n",
       "      <td>SP10211</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>SA79627</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>Bekasi</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>SA02310</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>Depok</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
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       "      <td>SA98565</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   Column_1  Column_2  Column_3  Column_4  Column_5  Column_6  Column_7  \\\n",
       "0         3         4         5         2         2         3         3   \n",
       "1         2         3         1         2         4         3         4   \n",
       "2         3         3         2         2         2         5         4   \n",
       "3         3         3         4         4         3         1         4   \n",
       "4         3         2         4         3         3         2         3   \n",
       "\n",
       "   Column_8  Column_9  Column_10  Column_11  Column_12 Staff_Id  \\\n",
       "0         3         2          3          4          4  SA63171   \n",
       "1         3         2          2          2          4  SP10211   \n",
       "2         4         3          2          4          5  SA79627   \n",
       "3         4         4          5          3          5  SA02310   \n",
       "4         4         2          3          2          3  SA98565   \n",
       "\n",
       "   Month_Of_Service  Years_Of_Service Residence  Residence_Code  \n",
       "0                 1                 0     Depok               4  \n",
       "1                43                 3   Jakarta               1  \n",
       "2                10                 0    Bekasi               3  \n",
       "3                17                 1     Depok               4  \n",
       "4                17                 1   Jakarta               1  "
      ]
     },
     "execution_count": 12,
     "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": 13,
   "id": "39e7083a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Residence</th>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
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       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Depok</td>\n",
       "      <td>4</td>\n",
       "      <td>5582218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>SP10211</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>1</td>\n",
       "      <td>9213443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>SA79627</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>Bekasi</td>\n",
       "      <td>3</td>\n",
       "      <td>5836455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>SA02310</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>Depok</td>\n",
       "      <td>4</td>\n",
       "      <td>6035466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>SA98565</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>1</td>\n",
       "      <td>5568101</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         3         4         5         2         2         3         3   \n",
       "1         2         3         1         2         4         3         4   \n",
       "2         3         3         2         2         2         5         4   \n",
       "3         3         3         4         4         3         1         4   \n",
       "4         3         2         4         3         3         2         3   \n",
       "\n",
       "   Column_8  Column_9  Column_10  Column_11  Column_12 Staff_Id  \\\n",
       "0         3         2          3          4          4  SA63171   \n",
       "1         3         2          2          2          4  SP10211   \n",
       "2         4         3          2          4          5  SA79627   \n",
       "3         4         4          5          3          5  SA02310   \n",
       "4         4         2          3          2          3  SA98565   \n",
       "\n",
       "   Month_Of_Service  Years_Of_Service Residence  Residence_Code  Net_Salary  \n",
       "0                 1                 0     Depok               4     5582218  \n",
       "1                43                 3   Jakarta               1     9213443  \n",
       "2                10                 0    Bekasi               3     5836455  \n",
       "3                17                 1     Depok               4     6035466  \n",
       "4                17                 1   Jakarta               1     5568101  "
      ]
     },
     "execution_count": 13,
     "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": 14,
   "id": "8861e640",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Staff_Id\n",
      "DR    54436305.0\n",
      "MA    18489651.0\n",
      "SA     5938218.5\n",
      "SP     9349631.0\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": 15,
   "id": "a04382c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the DataFrame to Excel\n",
    "df.to_excel('D:\\\\for python use\\\\HRD_Survey.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16c8d27f",
   "metadata": {},
   "outputs": [],
   "source": [
    "    "
   ]
  }
 ],
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