fake_data_builder.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4932693e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a92da3bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "id_series = pd.Series(['ID' + str(i).zfill(3) for i in range(800)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7ca2e29f",
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = np.random.choice(['A', 'B', 'C'], size=800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "61a9bd2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_1 = np.random.normal(2500, (2800-2200)/2, size=800)\n",
    "feature_2 = np.random.normal(400, 15, size=800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e32f978",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_3 = np.zeros(800)\n",
    "for i in range(800):\n",
    "    if groups[i] == 'A':\n",
    "        feature_3[i] = 3*feature_1[i] - 5*feature_2[i] + np.random.normal(0, 12)\n",
    "    elif groups[i] == 'B':\n",
    "        feature_3[i] = 2*feature_1[i] + np.random.normal(0, 12)\n",
    "    else:  # Group C\n",
    "        feature_3[i] = -feature_1[i] + feature_2[i] + np.random.normal(0, 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65aec5d2",
   "metadata": {},
   "source": [
    "Question 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "58e3edd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\n",
    "    'ID': id_series,\n",
    "    'Group': groups,\n",
    "    'Feature_1': feature_1,\n",
    "    'Feature_2': feature_2,\n",
    "    'Feature_3': feature_3\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "322d8794",
   "metadata": {},
   "source": [
    "Question 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "706d5e5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "theoretical_count = len(feature_1[feature_1 > 3000])\n",
    "\n",
    "actual_count = len(df[df['Feature_1'] > 3000])\n",
    "\n",
    "group_stats = df.groupby('Group')['Feature_3'].agg(['mean', 'std'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "53c92286",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0f14ad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theoretical_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "95cb345d",
   "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>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>5533.567270</td>\n",
       "      <td>866.698562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5039.541566</td>\n",
       "      <td>643.701262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2101.589827</td>\n",
       "      <td>289.890177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mean         std\n",
       "Group                         \n",
       "A      5533.567270  866.698562\n",
       "B      5039.541566  643.701262\n",
       "C     -2101.589827  289.890177"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4548bd1",
   "metadata": {},
   "source": [
    "Question 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "107ca9ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Feature_1'], df['Feature_3'])\n",
    "plt.xlabel('Feature_1')\n",
    "plt.ylabel('Feature_3')\n",
    "plt.title('Scatterplot of Feature_3 vs Feature_1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2725691e",
   "metadata": {},
   "source": [
    "Question 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdbd7796",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('fake_data.csv', index=False)"
   ]
  }
 ],
 "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}