dataframes.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "23b8855d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad99f65f",
   "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>id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>stroke</th>\n",
       "      <th>govt_worker</th>\n",
       "      <th>never_worked</th>\n",
       "      <th>private_worker</th>\n",
       "      <th>self_employed</th>\n",
       "      <th>rural_residence</th>\n",
       "      <th>urban_residence</th>\n",
       "      <th>smoking_unknown</th>\n",
       "      <th>former_smoker</th>\n",
       "      <th>never_smoked</th>\n",
       "      <th>current_smoker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9046</td>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31112</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60182</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1665</td>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56669</td>\n",
       "      <td>1</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  gender   age  hypertension  heart_disease  ever_married  \\\n",
       "0   9046       1  67.0             0              1             1   \n",
       "1  31112       1  80.0             0              1             1   \n",
       "2  60182       0  49.0             0              0             1   \n",
       "3   1665       0  79.0             1              0             1   \n",
       "4  56669       1  81.0             0              0             1   \n",
       "\n",
       "   avg_glucose_level   bmi  stroke  govt_worker  never_worked  private_worker  \\\n",
       "0             228.69  36.6       1            0             0               1   \n",
       "1             105.92  32.5       1            0             0               1   \n",
       "2             171.23  34.4       1            0             0               1   \n",
       "3             174.12  24.0       1            0             0               0   \n",
       "4             186.21  29.0       1            0             0               1   \n",
       "\n",
       "   self_employed  rural_residence  urban_residence  smoking_unknown  \\\n",
       "0              0                0                1                0   \n",
       "1              0                1                0                0   \n",
       "2              0                0                1                0   \n",
       "3              1                1                0                0   \n",
       "4              0                0                1                0   \n",
       "\n",
       "   former_smoker  never_smoked  current_smoker  \n",
       "0              1             0               0  \n",
       "1              0             1               0  \n",
       "2              0             0               1  \n",
       "3              0             1               0  \n",
       "4              1             0               0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load in DataFrame\n",
    "stroke_df = pd.read_csv(\"Resources/adult.csv\")\n",
    "stroke_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f2bbde2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export Selected Columns to CSV Files\n",
    "individual_columns = ['id','age','gender','ever_married']\n",
    "stroke_df.to_csv(\"Resources/individual.csv\", index=False, columns=individual_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca7cc166",
   "metadata": {},
   "outputs": [],
   "source": [
    "employment_columns = ['id','govt_worker','never_worked','private_worker','self_employed']\n",
    "stroke_df.to_csv(\"Resources/employment.csv\", index=False, columns=employment_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c2f8557c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cardio_columns = ['id','stroke','bmi','avg_glucose_level','hypertension','heart_disease']\n",
    "stroke_df.to_csv(\"Resources/cardiovascular.csv\", index=False, columns=cardio_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dec6b7ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_columns = ['id','rural_residence','urban_residence']\n",
    "stroke_df.to_csv(\"Resources/housing.csv\", index=False, columns=housing_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6742fd65",
   "metadata": {},
   "outputs": [],
   "source": [
    "smoking_columns = ['id','smoking_unknown','former_smoker','never_smoked','current_smoker']\n",
    "stroke_df.to_csv(\"Resources/smoking.csv\", index=False, columns=smoking_columns)"
   ]
  }
 ],
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