medical_examination.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ca12180",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "efe61930",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"medical_examination.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5fd984a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 70000 entries, 0 to 69999\n",
      "Data columns (total 13 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   id           70000 non-null  int64  \n",
      " 1   age          70000 non-null  int64  \n",
      " 2   sex          70000 non-null  int64  \n",
      " 3   height       70000 non-null  int64  \n",
      " 4   weight       70000 non-null  float64\n",
      " 5   ap_hi        70000 non-null  int64  \n",
      " 6   ap_lo        70000 non-null  int64  \n",
      " 7   cholesterol  70000 non-null  int64  \n",
      " 8   gluc         70000 non-null  int64  \n",
      " 9   smoke        70000 non-null  int64  \n",
      " 10  alco         70000 non-null  int64  \n",
      " 11  active       70000 non-null  int64  \n",
      " 12  cardio       70000 non-null  int64  \n",
      "dtypes: float64(1), int64(12)\n",
      "memory usage: 6.9 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "47502612",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   id    age  sex  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
       "0   0  18393    2     168    62.0    110     80            1     1      0   \n",
       "1   1  20228    1     156    85.0    140     90            3     1      0   \n",
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       "7  12  22584    2     178    95.0    130     90            3     3      0   \n",
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       "9  14  19834    1     164    68.0    110     60            1     1      0   \n",
       "\n",
       "   alco  active  cardio  \n",
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     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "31196682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 13)"
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     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.shape"
   ]
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  {
   "cell_type": "markdown",
   "id": "74198563",
   "metadata": {},
   "source": [
    "# Add an overweight column to the data. To determine if a person is overweight, first calculate their BMI by dividing their weight in kilograms by the square of their height in meters. If that value is > 25 then the person is overweight. Use the value 0 for NOT overweight and the value 1 for overweight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dd2728f4",
   "metadata": {},
   "outputs": [
    {
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    "df['weight']/df['height']"
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  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c7824768",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['overweight'] = (df[\"weight\"]/((df[\"height\"]/100)**2)).apply(lambda x : 1 if x>25 else 0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8d6a36b9",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "          id    age  sex  height  weight  ap_hi  ap_lo  cholesterol  gluc  \\\n",
       "0          0  18393    2     168    62.0    110     80            1     1   \n",
       "1          1  20228    1     156    85.0    140     90            3     1   \n",
       "2          2  18857    1     165    64.0    130     70            3     1   \n",
       "3          3  17623    2     169    82.0    150    100            1     1   \n",
       "4          4  17474    1     156    56.0    100     60            1     1   \n",
       "...      ...    ...  ...     ...     ...    ...    ...          ...   ...   \n",
       "69995  99993  19240    2     168    76.0    120     80            1     1   \n",
       "69996  99995  22601    1     158   126.0    140     90            2     2   \n",
       "69997  99996  19066    2     183   105.0    180     90            3     1   \n",
       "69998  99998  22431    1     163    72.0    135     80            1     2   \n",
       "69999  99999  20540    1     170    72.0    120     80            2     1   \n",
       "\n",
       "       smoke  alco  active  cardio  overweight  \n",
       "0          0     0       1       0           0  \n",
       "1          0     0       1       1           1  \n",
       "2          0     0       0       1           0  \n",
       "3          0     0       1       1           1  \n",
       "4          0     0       0       0           0  \n",
       "...      ...   ...     ...     ...         ...  \n",
       "69995      1     0       1       0           1  \n",
       "69996      0     0       1       1           1  \n",
       "69997      0     1       0       1           1  \n",
       "69998      0     0       0       1           1  \n",
       "69999      0     0       1       0           0  \n",
       "\n",
       "[70000 rows x 14 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d017196c",
   "metadata": {},
   "source": [
    "# Normalize the data by making 0 always good and 1 always bad. If the value of cholesterol or gluc is 1, make the value 0. If the value is more than 1, make the value 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "362ca9bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['gluc'] = df[\"gluc\"].apply(lambda x: 0 if x==1 else 1)\n",
    "df['cholesterol'] = df[\"cholesterol\"].apply(lambda x: 0 if x==1 else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "acd31d5e",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "          id    age  sex  height  weight  ap_hi  ap_lo  cholesterol  gluc  \\\n",
       "0          0  18393    2     168    62.0    110     80            0     0   \n",
       "1          1  20228    1     156    85.0    140     90            1     0   \n",
       "2          2  18857    1     165    64.0    130     70            1     0   \n",
       "3          3  17623    2     169    82.0    150    100            0     0   \n",
       "4          4  17474    1     156    56.0    100     60            0     0   \n",
       "...      ...    ...  ...     ...     ...    ...    ...          ...   ...   \n",
       "69995  99993  19240    2     168    76.0    120     80            0     0   \n",
       "69996  99995  22601    1     158   126.0    140     90            1     1   \n",
       "69997  99996  19066    2     183   105.0    180     90            1     0   \n",
       "69998  99998  22431    1     163    72.0    135     80            0     1   \n",
       "69999  99999  20540    1     170    72.0    120     80            1     0   \n",
       "\n",
       "       smoke  alco  active  cardio  overweight  \n",
       "0          0     0       1       0           0  \n",
       "1          0     0       1       1           1  \n",
       "2          0     0       0       1           0  \n",
       "3          0     0       1       1           1  \n",
       "4          0     0       0       0           0  \n",
       "...      ...   ...     ...     ...         ...  \n",
       "69995      1     0       1       0           1  \n",
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       "69998      0     0       0       1           1  \n",
       "69999      0     0       1       0           0  \n",
       "\n",
       "[70000 rows x 14 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63fb7b58",
   "metadata": {},
   "source": [
    "# Convert the data into long format and create a chart that shows the value counts of the categorical features using seaborn's catplot(). The dataset should be split by 'Cardio' so there is one chart for each cardio value. The chart should look like examples/Figure_1.png. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bc653f9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cat = pd.melt(df, id_vars=['cardio'],value_vars=['cholesterol','gluc','smoke','alco','active','overweight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "91b7eeea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>...</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>overweight</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>419999</th>\n",
       "      <td>0</td>\n",
       "      <td>overweight</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>420000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        cardio     variable  value\n",
       "0            0  cholesterol      0\n",
       "1            1  cholesterol      1\n",
       "2            1  cholesterol      1\n",
       "3            1  cholesterol      0\n",
       "4            0  cholesterol      0\n",
       "...        ...          ...    ...\n",
       "419995       0   overweight      1\n",
       "419996       1   overweight      1\n",
       "419997       1   overweight      1\n",
       "419998       1   overweight      1\n",
       "419999       0   overweight      0\n",
       "\n",
       "[420000 rows x 3 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "46aff824",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cat['total']=1\n",
    "df_cat = df_cat.groupby([\"cardio\",\"variable\",\"value\"], as_index=False).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a6dc00fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1058.88x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.catplot(x=\"variable\",y=\"total\",data = df_cat,hue='value',kind='bar',col='cardio').fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0601d97d",
   "metadata": {},
   "source": [
    "# Clean the data. Filter out the following patient segments that represent incorrect data:\n",
    "## diastolic pressure is higher than systolic (Keep the correct data with (df['ap_lo'] <= df['ap_hi']))\n",
    "## height is less than the 2.5th percentile (Keep the correct data with (df['height'] >= df['height'].quantile(0.025)))\n",
    "## height is more than the 97.5th percentile\n",
    "## weight is less than the 2.5th percentile\n",
    "## weight is more than the 97.5th percentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "08699842",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_heat = df[\n",
    "    (data['ap_lo'] <= data['ap_hi']) &\n",
    "    (data['height'] >= data['height'].quantile(0.025)) &\n",
    "    (data['height'] <= data['height'].quantile(0.975)) &\n",
    "    (data['weight'] >= data['weight'].quantile(0.025)) &\n",
    "    (data['weight'] <= data['weight'].quantile(0.975))\n",
    "]\n",
    "corr = df_heat.corr(method=\"pearson\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0501e1a1",
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "      <th>overweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002507</td>\n",
       "      <td>0.003426</td>\n",
       "      <td>0.000326</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.003492</td>\n",
       "      <td>0.000484</td>\n",
       "      <td>0.003719</td>\n",
       "      <td>0.002289</td>\n",
       "      <td>-0.002732</td>\n",
       "      <td>-0.000708</td>\n",
       "      <td>0.005891</td>\n",
       "      <td>0.003900</td>\n",
       "      <td>-0.000679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.002507</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018132</td>\n",
       "      <td>-0.076932</td>\n",
       "      <td>0.070168</td>\n",
       "      <td>0.018689</td>\n",
       "      <td>0.153508</td>\n",
       "      <td>0.129996</td>\n",
       "      <td>0.086990</td>\n",
       "      <td>-0.044371</td>\n",
       "      <td>-0.026970</td>\n",
       "      <td>-0.011445</td>\n",
       "      <td>0.240241</td>\n",
       "      <td>0.089573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>0.003426</td>\n",
       "      <td>-0.018132</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.505785</td>\n",
       "      <td>0.129979</td>\n",
       "      <td>0.005063</td>\n",
       "      <td>0.059947</td>\n",
       "      <td>-0.037605</td>\n",
       "      <td>-0.021357</td>\n",
       "      <td>0.337436</td>\n",
       "      <td>0.168637</td>\n",
       "      <td>0.007864</td>\n",
       "      <td>0.002320</td>\n",
       "      <td>-0.052930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>0.000326</td>\n",
       "      <td>-0.076932</td>\n",
       "      <td>0.505785</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.251503</td>\n",
       "      <td>0.004586</td>\n",
       "      <td>0.017922</td>\n",
       "      <td>-0.063522</td>\n",
       "      <td>-0.030469</td>\n",
       "      <td>0.187830</td>\n",
       "      <td>0.089785</td>\n",
       "      <td>-0.005661</td>\n",
       "      <td>-0.024102</td>\n",
       "      <td>-0.149570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.070168</td>\n",
       "      <td>0.129979</td>\n",
       "      <td>0.251503</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.027030</td>\n",
       "      <td>0.227922</td>\n",
       "      <td>0.135981</td>\n",
       "      <td>0.107072</td>\n",
       "      <td>0.056533</td>\n",
       "      <td>0.059917</td>\n",
       "      <td>-0.014430</td>\n",
       "      <td>0.170042</td>\n",
       "      <td>0.655926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ap_hi</th>\n",
       "      <td>0.003492</td>\n",
       "      <td>0.018689</td>\n",
       "      <td>0.005063</td>\n",
       "      <td>0.004586</td>\n",
       "      <td>0.027030</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.072803</td>\n",
       "      <td>0.022807</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>-0.001928</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>-0.000222</td>\n",
       "      <td>0.050556</td>\n",
       "      <td>0.017327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ap_lo</th>\n",
       "      <td>0.000484</td>\n",
       "      <td>0.153508</td>\n",
       "      <td>0.059947</td>\n",
       "      <td>0.017922</td>\n",
       "      <td>0.227922</td>\n",
       "      <td>0.072803</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.150362</td>\n",
       "      <td>0.074840</td>\n",
       "      <td>0.023275</td>\n",
       "      <td>0.033042</td>\n",
       "      <td>0.001569</td>\n",
       "      <td>0.327181</td>\n",
       "      <td>0.172212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cholesterol</th>\n",
       "      <td>0.003719</td>\n",
       "      <td>0.129996</td>\n",
       "      <td>-0.037605</td>\n",
       "      <td>-0.063522</td>\n",
       "      <td>0.135981</td>\n",
       "      <td>0.022807</td>\n",
       "      <td>0.150362</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.383743</td>\n",
       "      <td>0.012680</td>\n",
       "      <td>0.038446</td>\n",
       "      <td>0.002494</td>\n",
       "      <td>0.203467</td>\n",
       "      <td>0.127986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gluc</th>\n",
       "      <td>0.002289</td>\n",
       "      <td>0.086990</td>\n",
       "      <td>-0.021357</td>\n",
       "      <td>-0.030469</td>\n",
       "      <td>0.107072</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>0.074840</td>\n",
       "      <td>0.383743</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004157</td>\n",
       "      <td>0.014297</td>\n",
       "      <td>-0.010145</td>\n",
       "      <td>0.088445</td>\n",
       "      <td>0.088109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoke</th>\n",
       "      <td>-0.002732</td>\n",
       "      <td>-0.044371</td>\n",
       "      <td>0.337436</td>\n",
       "      <td>0.187830</td>\n",
       "      <td>0.056533</td>\n",
       "      <td>-0.001928</td>\n",
       "      <td>0.023275</td>\n",
       "      <td>0.012680</td>\n",
       "      <td>-0.004157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.341200</td>\n",
       "      <td>0.026940</td>\n",
       "      <td>-0.020345</td>\n",
       "      <td>-0.003274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alco</th>\n",
       "      <td>-0.000708</td>\n",
       "      <td>-0.026970</td>\n",
       "      <td>0.168637</td>\n",
       "      <td>0.089785</td>\n",
       "      <td>0.059917</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>0.033042</td>\n",
       "      <td>0.038446</td>\n",
       "      <td>0.014297</td>\n",
       "      <td>0.341200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025847</td>\n",
       "      <td>-0.011088</td>\n",
       "      <td>0.025056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>active</th>\n",
       "      <td>0.005891</td>\n",
       "      <td>-0.011445</td>\n",
       "      <td>0.007864</td>\n",
       "      <td>-0.005661</td>\n",
       "      <td>-0.014430</td>\n",
       "      <td>-0.000222</td>\n",
       "      <td>0.001569</td>\n",
       "      <td>0.002494</td>\n",
       "      <td>-0.010145</td>\n",
       "      <td>0.026940</td>\n",
       "      <td>0.025847</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.037339</td>\n",
       "      <td>-0.003387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardio</th>\n",
       "      <td>0.003900</td>\n",
       "      <td>0.240241</td>\n",
       "      <td>0.002320</td>\n",
       "      <td>-0.024102</td>\n",
       "      <td>0.170042</td>\n",
       "      <td>0.050556</td>\n",
       "      <td>0.327181</td>\n",
       "      <td>0.203467</td>\n",
       "      <td>0.088445</td>\n",
       "      <td>-0.020345</td>\n",
       "      <td>-0.011088</td>\n",
       "      <td>-0.037339</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.143168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>overweight</th>\n",
       "      <td>-0.000679</td>\n",
       "      <td>0.089573</td>\n",
       "      <td>-0.052930</td>\n",
       "      <td>-0.149570</td>\n",
       "      <td>0.655926</td>\n",
       "      <td>0.017327</td>\n",
       "      <td>0.172212</td>\n",
       "      <td>0.127986</td>\n",
       "      <td>0.088109</td>\n",
       "      <td>-0.003274</td>\n",
       "      <td>0.025056</td>\n",
       "      <td>-0.003387</td>\n",
       "      <td>0.143168</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id       age       sex    height    weight     ap_hi  \\\n",
       "id           1.000000  0.002507  0.003426  0.000326  0.000149  0.003492   \n",
       "age          0.002507  1.000000 -0.018132 -0.076932  0.070168  0.018689   \n",
       "sex          0.003426 -0.018132  1.000000  0.505785  0.129979  0.005063   \n",
       "height       0.000326 -0.076932  0.505785  1.000000  0.251503  0.004586   \n",
       "weight       0.000149  0.070168  0.129979  0.251503  1.000000  0.027030   \n",
       "ap_hi        0.003492  0.018689  0.005063  0.004586  0.027030  1.000000   \n",
       "ap_lo        0.000484  0.153508  0.059947  0.017922  0.227922  0.072803   \n",
       "cholesterol  0.003719  0.129996 -0.037605 -0.063522  0.135981  0.022807   \n",
       "gluc         0.002289  0.086990 -0.021357 -0.030469  0.107072  0.011111   \n",
       "smoke       -0.002732 -0.044371  0.337436  0.187830  0.056533 -0.001928   \n",
       "alco        -0.000708 -0.026970  0.168637  0.089785  0.059917  0.000769   \n",
       "active       0.005891 -0.011445  0.007864 -0.005661 -0.014430 -0.000222   \n",
       "cardio       0.003900  0.240241  0.002320 -0.024102  0.170042  0.050556   \n",
       "overweight  -0.000679  0.089573 -0.052930 -0.149570  0.655926  0.017327   \n",
       "\n",
       "                ap_lo  cholesterol      gluc     smoke      alco    active  \\\n",
       "id           0.000484     0.003719  0.002289 -0.002732 -0.000708  0.005891   \n",
       "age          0.153508     0.129996  0.086990 -0.044371 -0.026970 -0.011445   \n",
       "sex          0.059947    -0.037605 -0.021357  0.337436  0.168637  0.007864   \n",
       "height       0.017922    -0.063522 -0.030469  0.187830  0.089785 -0.005661   \n",
       "weight       0.227922     0.135981  0.107072  0.056533  0.059917 -0.014430   \n",
       "ap_hi        0.072803     0.022807  0.011111 -0.001928  0.000769 -0.000222   \n",
       "ap_lo        1.000000     0.150362  0.074840  0.023275  0.033042  0.001569   \n",
       "cholesterol  0.150362     1.000000  0.383743  0.012680  0.038446  0.002494   \n",
       "gluc         0.074840     0.383743  1.000000 -0.004157  0.014297 -0.010145   \n",
       "smoke        0.023275     0.012680 -0.004157  1.000000  0.341200  0.026940   \n",
       "alco         0.033042     0.038446  0.014297  0.341200  1.000000  0.025847   \n",
       "active       0.001569     0.002494 -0.010145  0.026940  0.025847  1.000000   \n",
       "cardio       0.327181     0.203467  0.088445 -0.020345 -0.011088 -0.037339   \n",
       "overweight   0.172212     0.127986  0.088109 -0.003274  0.025056 -0.003387   \n",
       "\n",
       "               cardio  overweight  \n",
       "id           0.003900   -0.000679  \n",
       "age          0.240241    0.089573  \n",
       "sex          0.002320   -0.052930  \n",
       "height      -0.024102   -0.149570  \n",
       "weight       0.170042    0.655926  \n",
       "ap_hi        0.050556    0.017327  \n",
       "ap_lo        0.327181    0.172212  \n",
       "cholesterol  0.203467    0.127986  \n",
       "gluc         0.088445    0.088109  \n",
       "smoke       -0.020345   -0.003274  \n",
       "alco        -0.011088    0.025056  \n",
       "active      -0.037339   -0.003387  \n",
       "cardio       1.000000    0.143168  \n",
       "overweight   0.143168    1.000000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "79d7d578",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = np.triu(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f25a37ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         3.25916709e-04,  1.49333224e-04,  3.49238023e-03,\n",
       "         4.83670793e-04,  3.71939143e-03,  2.28909034e-03,\n",
       "        -2.73227725e-03, -7.08059804e-04,  5.89114223e-03,\n",
       "         3.90011059e-03, -6.79420314e-04],\n",
       "       [ 0.00000000e+00,  1.00000000e+00, -1.81320052e-02,\n",
       "        -7.69318347e-02,  7.01683896e-02,  1.86894095e-02,\n",
       "         1.53507973e-01,  1.29996176e-01,  8.69901449e-02,\n",
       "        -4.43706471e-02, -2.69695382e-02, -1.14451266e-02,\n",
       "         2.40241089e-01,  8.95726547e-02],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  1.00000000e+00,\n",
       "         5.05784851e-01,  1.29979320e-01,  5.06250408e-03,\n",
       "         5.99466484e-02, -3.76050443e-02, -2.13573847e-02,\n",
       "         3.37435881e-01,  1.68637124e-01,  7.86370242e-03,\n",
       "         2.31999158e-03, -5.29296848e-02],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         1.00000000e+00,  2.51502865e-01,  4.58573625e-03,\n",
       "         1.79224331e-02, -6.35217890e-02, -3.04690159e-02,\n",
       "         1.87829575e-01,  8.97850523e-02, -5.66147972e-03,\n",
       "        -2.41019167e-02, -1.49569820e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  1.00000000e+00,  2.70301864e-02,\n",
       "         2.27921644e-01,  1.35980961e-01,  1.07071633e-01,\n",
       "         5.65328321e-02,  5.99166585e-02, -1.44302415e-02,\n",
       "         1.70041786e-01,  6.55925735e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  1.00000000e+00,\n",
       "         7.28027646e-02,  2.28068897e-02,  1.11111745e-02,\n",
       "        -1.92773034e-03,  7.69214083e-04, -2.22293525e-04,\n",
       "         5.05559101e-02,  1.73269545e-02],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         1.00000000e+00,  1.50361776e-01,  7.48402275e-02,\n",
       "         2.32745773e-02,  3.30418087e-02,  1.56945685e-03,\n",
       "         3.27180905e-01,  1.72212135e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  1.00000000e+00,  3.83743056e-01,\n",
       "         1.26800958e-02,  3.84462597e-02,  2.49367396e-03,\n",
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       "         8.84445796e-02,  8.81094078e-02],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         1.00000000e+00,  3.41200425e-01,  2.69395335e-02,\n",
       "        -2.03448215e-02, -3.27424154e-03],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  1.00000000e+00,  2.58472805e-02,\n",
       "        -1.10879844e-02,  2.50558772e-02],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  1.00000000e+00,\n",
       "        -3.73393835e-02, -3.38671169e-03],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         1.00000000e+00,  1.43167747e-01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  1.00000000e+00]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6bd8ea18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax = plt.subplots(figsize=(12,12))\n",
    "\n",
    "sns.heatmap(corr,linewidths=1,annot=True,square = True,mask=mask,fmt=\".1f\",center=0.08,cbar_kws={\"shrink\":0.5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0be42e8e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}