medical_data_visualizer.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://replit.com/@VishnuTR/boilerplate-medical-data-visualizer-1#main.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['MPLCONFIGDIR'] = os.getcwd() + \"/configs/\"\n",
    "import matplotlib\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "  <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",
       "    </tr>\n",
       "  </thead>\n",
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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",
       "2   2  18857    1     165    64.0    130     70            3     1      0   \n",
       "3   3  17623    2     169    82.0    150    100            1     1      0   \n",
       "4   4  17474    1     156    56.0    100     60            1     1      0   \n",
       "\n",
       "   alco  active  cardio  \n",
       "0     0       1       0  \n",
       "1     0       1       1  \n",
       "2     0       0       1  \n",
       "3     0       1       1  \n",
       "4     0       0       0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import data\n",
    "df = pd.read_csv('C:/Users/91740/Downloads/medical_examination.csv')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        21.967120\n",
       "1        34.927679\n",
       "2        23.507805\n",
       "3        28.710479\n",
       "4        23.011177\n",
       "           ...    \n",
       "69995    26.927438\n",
       "69996    50.472681\n",
       "69997    31.353579\n",
       "69998    27.099251\n",
       "69999    24.913495\n",
       "Name: overweight, Length: 70000, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculated bmi value\n",
    "df['overweight'] = (df['weight'] / (df['height']/100)**2)\n",
    "df['overweight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        1\n",
       "2        0\n",
       "3        1\n",
       "4        0\n",
       "        ..\n",
       "69995    1\n",
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       "Name: overweight, Length: 70000, dtype: int64"
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     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "#using lambda fucntion fix the values of df['overweight'] either 1 or 0 based on the condition\n",
    "df['overweight'] = (df['weight'] / (df['height']/100)**2).apply(lambda x: 1 if x > 25 else 0)\n",
    "df['overweight']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.geeksforgeeks.org/how-to-use-if-else-elif-in-python-lambda-functions/#:~:text=Using%20if%2Delse%20in%20lambda%20function&text=Here%2C%20if%20block%20will%20be,when%20the%20condition%20is%20false.&text=Here%2C%20the%20lambda%20function%20will,if%20the%20condition%20is%20false."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize data by making 0 always good and 1 always bad. \n",
    "#If the value of 'cholestorol' or 'gluc' is 1, make the value 0. \n",
    "#If the value is more than 1, make the value 1.\n",
    "df['cholesterol'] = df['cholesterol'].apply(lambda x: 0 if x == 1 else 1)\n",
    "df['gluc'] = df['gluc'].apply(lambda x: 0 if x == 1 else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
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       "      <th>ap_hi</th>\n",
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       "      <th>smoke</th>\n",
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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            0     0      0   \n",
       "1   1  20228    1     156    85.0    140     90            1     0      0   \n",
       "2   2  18857    1     165    64.0    130     70            1     0      0   \n",
       "3   3  17623    2     169    82.0    150    100            0     0      0   \n",
       "4   4  17474    1     156    56.0    100     60            0     0      0   \n",
       "\n",
       "   alco  active  cardio  \n",
       "0     0       1       0  \n",
       "1     0       1       1  \n",
       "2     0       0       1  \n",
       "3     0       1       1  \n",
       "4     0       0       0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reformated data\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Draw Categorical Plot\n",
    "#def used for replit purpose only\n",
    "#otherwise not required\n",
    "def draw_cat_plot():\n",
    "    # Create DataFrame for cat plot using `pd.melt` using just the values from 'cholesterol', 'gluc', 'smoke', 'alco', 'active', and 'overweight'.\n",
    "df_cat = pd.melt(df, id_vars = 'cardio', var_name = 'variable', value_vars = ['alco', 'active','cholesterol', 'gluc', 'weight','smoke'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cat = pd.melt(df, id_vars = 'cardio', var_name = 'variable', value_vars = ['alco', 'active','cholesterol', 'gluc', 'weight','smoke'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cardio</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
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       "    <tr>\n",
       "      <th>419996</th>\n",
       "      <td>1</td>\n",
       "      <td>smoke</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>419997</th>\n",
       "      <td>1</td>\n",
       "      <td>smoke</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419998</th>\n",
       "      <td>1</td>\n",
       "      <td>smoke</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419999</th>\n",
       "      <td>0</td>\n",
       "      <td>smoke</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>420000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        cardio variable  value\n",
       "0            0     alco    0.0\n",
       "1            1     alco    0.0\n",
       "2            1     alco    0.0\n",
       "3            1     alco    0.0\n",
       "4            0     alco    0.0\n",
       "...        ...      ...    ...\n",
       "419995       0    smoke    1.0\n",
       "419996       1    smoke    0.0\n",
       "419997       1    smoke    0.0\n",
       "419998       1    smoke    0.0\n",
       "419999       0    smoke    0.0\n",
       "\n",
       "[420000 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cat = pd.melt(df, id_vars = 'cardio', var_name = 'variable', value_vars = ['active','alco','cholesterol', 'gluc','smoke'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 762.375x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw the catplot with 'sns.catplot()'\n",
    "fig = sns.catplot(data=df_cat, kind=\"count\",  x=\"variable\",hue=\"value\", col=\"cardio\").set_axis_labels(\"variable\", \"total\")\n",
    "fig = fig.fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do not modify the next two lines\n",
    "fig.savefig('catplot.png')\n",
    "#return used because of def\n",
    "#otherwise not required\n",
    "return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Draw Heat Map\n",
    "def draw_heat_map():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean the data\n",
    "df_heat = df[(df['ap_lo']<=df['ap_hi']) &\n",
    "    (df['height'] >= df['height'].quantile(0.025))&\n",
    "    (df['height'] <= df['height'].quantile(0.975))&\n",
    "    (df['weight'] >= df['weight'].quantile(0.025))&\n",
    "    (df['weight'] <= df['weight'].quantile(0.975))\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "\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",
       "\n",
       "               cardio  \n",
       "id           0.003900  \n",
       "age          0.240241  \n",
       "sex          0.002320  \n",
       "height      -0.024102  \n",
       "weight       0.170042  \n",
       "ap_hi        0.050556  \n",
       "ap_lo        0.327181  \n",
       "cholesterol  0.203467  \n",
       "gluc         0.088445  \n",
       "smoke       -0.020345  \n",
       "alco        -0.011088  \n",
       "active      -0.037339  \n",
       "cardio       1.000000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the correlation matrix\n",
    "corr = df_heat.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00000000e+00,  2.50729691e-03,  3.42610273e-03,\n",
       "         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],\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],\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],\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],\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],\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],\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],\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",
       "         2.03466605e-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,  1.00000000e+00,\n",
       "        -4.15702174e-03,  1.42971522e-02, -1.01454104e-02,\n",
       "         8.84445796e-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],\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],\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],\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]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a mask for the upper triangle\n",
    "mask = np.triu(corr)\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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N7E1HqardmZ12fPSsTnn+mbJvP07y1iSpqjdkFrNTZ3XKMR1L8s75uxqvTvJUd//kdJ+wo6cZ26WwtmXivn08ySuSfHn+fpkfd/ehczb0eWDivrHBxH27K8lfVNVDSf4nyQe7+2fnbupzb+K+fSDJv1bV32V2muxd/rGeVNUXMztlvXv++8QPJ3lpknT3pzL7/eJ1SU4meTrJuxc+p30FYHSuAALA8MQMgOGJGQDDEzMAhidmAAxPzAAYnpgBMDwxA2B4/weikBsKGudPugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " # Set up the matplotlib figure\n",
    "fig, ax = plt.subplots(figsize=(7, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f347f4f788>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw the heatmap with 'sns.heatmap()'\n",
    "sns.heatmap(corr,mask=mask, fmt='.1f',vmax=.3, linewidths=.5,square=True, cbar_kws = {'shrink':0.5},annot=True, center=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}