free-code-camp-3.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
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   "source": [
    "<h2> Visualizing the given dataset </h2>"
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       "          id    age  gender  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",
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       "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",
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       "69999  99999  20540       1     170    72.0    120     80            2     1   \n",
       "\n",
       "       smoke  alco  active  cardio  \n",
       "0          0     0       1       0  \n",
       "1          0     0       1       1  \n",
       "2          0     0       0       1  \n",
       "3          0     0       1       1  \n",
       "4          0     0       0       0  \n",
       "...      ...   ...     ...     ...  \n",
       "69995      1     0       1       0  \n",
       "69996      0     0       1       1  \n",
       "69997      0     1       0       1  \n",
       "69998      0     0       0       1  \n",
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       "\n",
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   "source": [
    "df = pd.read_csv('../input/medical-examination-data/medical_examination_data.csv')\n",
    "df"
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   "source": [
    "<h2> Adding a new column named 'overweight' </h2>"
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       "          id    age  gender  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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Body-Mass-Index(BMI) = weight/(height*height)\n",
    "#In our dataset we have been given weight in Kg and Height in cm. So we will divide the Height by 100 while calculating the BMI\n",
    "df['overweight'] = (df['weight']/(df['height']/100)**2).apply(lambda BMI : 1 if BMI > 25 else 0)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "<h2> Normalize 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.</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d22bab0",
   "metadata": {
    "execution": {
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    {
     "data": {
      "text/plain": [
       "(1    59479\n",
       " 3     5331\n",
       " 2     5190\n",
       " Name: gluc, dtype: int64,\n",
       " 1    52385\n",
       " 2     9549\n",
       " 3     8066\n",
       " Name: cholesterol, dtype: int64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gluc'].value_counts(), df['cholesterol'].value_counts()"
   ]
  },
  {
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   },
   "outputs": [
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      "text/plain": [
       "          id    age  gender  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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       "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gluc'] = df['gluc'].apply(lambda gluc_value : 0 if gluc_value == 1 else 1)\n",
    "df['cholesterol'] = df['cholesterol'].apply(lambda cholesterol_value : 0 if cholesterol_value == 1 else 1)\n",
    "df"
   ]
  },
  {
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   "source": [
    ".<h2> Draw Categorical Plot </h2>\n",
    "<h2> Create DataFrame for cat plot using `pd.melt` using just the values from 'cholesterol', 'gluc', 'smoke', 'alco', 'active', and 'overweight'. </h2>\n",
    "<h2> Group and reformat the data to split it by 'cardio'. Show the counts of each feature. You will have to rename one of the columns for the catplot to work correctly.</h2>\n",
    "<h2> Draw the catplot with 'sns.catplot()' </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c40e84f7",
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   "source": [
    "def draw_cat_plot():\n",
    "    df_cat = pd.melt(df,id_vars = ['cardio'],value_vars = ['cholesterol','gluc','smoke','alco','active','overweight'])\n",
    "    df_cat['total'] = 1\n",
    "    df_cat = df_cat.groupby(['cardio','variable','value'], as_index = False).count()\n",
    "    fig = sns.catplot(x = \"variable\", y = \"total\", data = df_cat, hue = \"value\", kind = \"bar\", col = \"cardio\").fig\n",
    "    fig.savefig('catplot.png')\n",
    "    return fig\n"
   ]
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    {
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\n",
      "text/plain": [
       "<Figure size 762.375x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cat = pd.melt(df,id_vars = ['cardio'],value_vars = ['cholesterol','gluc','smoke','alco','active','overweight'])\n",
    "df_cat['total'] = 1\n",
    "df_cat = df_cat.groupby(['cardio','variable','value'], as_index = False).count()\n",
    "fig = sns.catplot(x = \"variable\", y = \"total\", data = df_cat, hue = \"value\", kind = \"bar\", col = \"cardio\").fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0938dbb",
   "metadata": {
    "papermill": {
     "duration": 0.014662,
     "end_time": "2022-01-10T18:10:35.841481",
     "exception": false,
     "start_time": "2022-01-10T18:10:35.826819",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<h2> Draw Heat Map </h2>\n",
    "<h2> Clean the data </h2>\n",
    "<h2> Calculate the correlation matrix </h2>\n",
    "<h2> Generate a mask for the upper triangle </h2>\n",
    "<h2> Set up the matplotlib figure </h2>\n",
    "<h2> Draw the Heatmap </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "24f84234",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T18:10:35.879484Z",
     "iopub.status.busy": "2022-01-10T18:10:35.878777Z",
     "iopub.status.idle": "2022-01-10T18:10:35.882497Z",
     "shell.execute_reply": "2022-01-10T18:10:35.881978Z",
     "shell.execute_reply.started": "2022-01-10T18:09:53.322377Z"
    },
    "papermill": {
     "duration": 0.026566,
     "end_time": "2022-01-10T18:10:35.882639",
     "exception": false,
     "start_time": "2022-01-10T18:10:35.856073",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def draw_heat_map():\n",
    "    df_heat = df[\n",
    "       (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",
    "    corr = df_heat.corr(method = \"pearson\")\n",
    "    mask = np.triu(corr)\n",
    "    fig, ax = plt.subplots(figsize=(12,12))\n",
    "    sns.heatmap(corr, linewidths=1, annot = True, square = True, mask = mask, fmt = \".1f\", center=0.08, cbar_kws = {\"shrink\":0.5})\n",
    "    fig.savefig('heatmap.png')\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9baf665e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T18:10:35.921224Z",
     "iopub.status.busy": "2022-01-10T18:10:35.920468Z",
     "iopub.status.idle": "2022-01-10T18:10:36.833711Z",
     "shell.execute_reply": "2022-01-10T18:10:36.833192Z",
     "shell.execute_reply.started": "2022-01-10T18:09:42.702272Z"
    },
    "papermill": {
     "duration": 0.936529,
     "end_time": "2022-01-10T18:10:36.833858",
     "exception": false,
     "start_time": "2022-01-10T18:10:35.897329",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_heat = df[\n",
    "       (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",
    "corr = df_heat.corr(method = \"pearson\")\n",
    "mask = np.triu(corr)\n",
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "sns.heatmap(corr, linewidths=1, annot = True, square = True, mask = mask, fmt = \".1f\", center=0.08, cbar_kws = {\"shrink\":0.5})\n",
    "   "
   ]
  },
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   "source": []
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