Assignment_Python_AliEjaz1749.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Assignment Python\n",
    "#Course instructor : Muhammad Sohaib \n",
    "#Ali Ejaz - 1749"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'bp', 'sg', 'al', 'su', 'rbc', 'pc', 'pcc', 'ba', 'bgr', 'bu',\n",
      "       'sc', 'sod', 'pot', 'hemo', 'pcv', 'wbcc', 'rbcc', 'htn', 'dm', 'cad',\n",
      "       'appet', 'pe', 'ane', 'Class'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "##Ques1 : Load the dataset to pandas dataframe and print names of columns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib import style\n",
    "style.use('ggplot')\n",
    "#import data set\n",
    "dataSt = pd.read_csv(r'D:\\chronic_kidney_disease_updated.csv')\n",
    "\n",
    "#create copy of dataframe\n",
    "dataStFrm = dataSt.copy()\n",
    "\n",
    "print(dataStFrm.columns)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>rbc</th>\n",
       "      <th>pc</th>\n",
       "      <th>pcc</th>\n",
       "      <th>ba</th>\n",
       "      <th>bgr</th>\n",
       "      <th>...</th>\n",
       "      <th>pcv</th>\n",
       "      <th>wbcc</th>\n",
       "      <th>rbcc</th>\n",
       "      <th>htn</th>\n",
       "      <th>dm</th>\n",
       "      <th>cad</th>\n",
       "      <th>appet</th>\n",
       "      <th>pe</th>\n",
       "      <th>ane</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>80</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>121</td>\n",
       "      <td>...</td>\n",
       "      <td>44</td>\n",
       "      <td>7800</td>\n",
       "      <td>5.2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>50</td>\n",
       "      <td>1.02</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>?</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>6000</td>\n",
       "      <td>?</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62</td>\n",
       "      <td>80</td>\n",
       "      <td>1.01</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>423</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>7500</td>\n",
       "      <td>?</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>poor</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48</td>\n",
       "      <td>70</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>normal</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>present</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>117</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>6700</td>\n",
       "      <td>3.9</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>poor</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   bp     sg   al   su     rbc        pc         pcc          ba  bgr  \\\n",
       "0  NaN  NaN    NaN  NaN  NaN     NaN       NaN         NaN         NaN  NaN   \n",
       "1   48   80   1.02    1    0       ?    normal  notpresent  notpresent  121   \n",
       "2    7   50   1.02    4    0       ?    normal  notpresent  notpresent    ?   \n",
       "3   62   80   1.01    2    3  normal    normal  notpresent  notpresent  423   \n",
       "4   48   70  1.005    4    0  normal  abnormal     present  notpresent  117   \n",
       "\n",
       "   ...   pcv  wbcc rbcc  htn   dm  cad appet   pe  ane Class  \n",
       "0  ...   NaN   NaN  NaN  NaN  NaN  NaN   NaN  NaN  NaN   NaN  \n",
       "1  ...    44  7800  5.2  yes  yes   no  good   no   no   ckd  \n",
       "2  ...    38  6000    ?   no   no   no  good   no   no   ckd  \n",
       "3  ...    31  7500    ?   no  yes   no  poor   no  yes   ckd  \n",
       "4  ...    32  6700  3.9  yes   no   no  poor  yes  yes   ckd  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Ques2. Display first 5 rows in the dataset.\n",
    "dataStFrm[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'yes', 'no', ' yes', '\\tno', '\\tyes'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Ques 3. Check if dataset is in cleaned form else clean it. Check Diabetes Mellitus(dm) variable and print what type of values it has.\n",
    "dataStFrm['dm'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     age   bp     sg   al   su       rbc        pc         pcc          ba  \\\n",
      "0    NaN  NaN    NaN  NaN  NaN       NaN       NaN         NaN         NaN   \n",
      "1     48   80   1.02    1    0       NaN    normal  notpresent  notpresent   \n",
      "2      7   50   1.02    4    0       NaN    normal  notpresent  notpresent   \n",
      "3     62   80   1.01    2    3    normal    normal  notpresent  notpresent   \n",
      "4     48   70  1.005    4    0    normal  abnormal     present  notpresent   \n",
      "5     51   80   1.01    2    0    normal    normal  notpresent  notpresent   \n",
      "6     60   90  1.015    3    0       NaN       NaN  notpresent  notpresent   \n",
      "7     68   70   1.01    0    0       NaN    normal  notpresent  notpresent   \n",
      "8     24  NaN  1.015    2    4    normal  abnormal  notpresent  notpresent   \n",
      "9     52  100  1.015    3    0    normal  abnormal     present  notpresent   \n",
      "10    53   90   1.02    2    0  abnormal  abnormal     present  notpresent   \n",
      "11    50   60   1.01    2    4       NaN  abnormal     present  notpresent   \n",
      "12    63   70   1.01    3    0  abnormal  abnormal     present  notpresent   \n",
      "13    68   70  1.015    3    1       NaN    normal     present  notpresent   \n",
      "14    68   70    NaN  NaN  NaN       NaN       NaN  notpresent  notpresent   \n",
      "15    68   80   1.01    3    2    normal  abnormal     present     present   \n",
      "16    40   80  1.015    3    0       NaN    normal  notpresent  notpresent   \n",
      "17    47   70  1.015    2    0       NaN    normal  notpresent  notpresent   \n",
      "18    47   80    NaN  NaN  NaN       NaN       NaN  notpresent  notpresent   \n",
      "19    60  100  1.025    0    3       NaN    normal  notpresent  notpresent   \n",
      "20    62   60  1.015    1    0       NaN  abnormal     present  notpresent   \n",
      "21    61   80  1.015    2    0  abnormal  abnormal  notpresent  notpresent   \n",
      "22    60   90    NaN  NaN  NaN       NaN       NaN  notpresent  notpresent   \n",
      "23    48   80  1.025    4    0    normal  abnormal  notpresent  notpresent   \n",
      "24    21   70   1.01    0    0       NaN    normal  notpresent  notpresent   \n",
      "25    42  100  1.015    4    0    normal  abnormal  notpresent     present   \n",
      "26    61   60  1.025    0    0       NaN    normal  notpresent  notpresent   \n",
      "27    75   80  1.015    0    0       NaN    normal  notpresent  notpresent   \n",
      "28    69   70   1.01    3    4    normal  abnormal  notpresent  notpresent   \n",
      "29    75   70    NaN    1    3       NaN       NaN  notpresent  notpresent   \n",
      "..   ...  ...    ...  ...  ...       ...       ...         ...         ...   \n",
      "371   69   70   1.02    0    0    normal    normal  notpresent  notpresent   \n",
      "372   28   60  1.025    0    0    normal    normal  notpresent  notpresent   \n",
      "373   72   60   1.02    0    0    normal    normal  notpresent  notpresent   \n",
      "374   61   70  1.025    0    0    normal    normal  notpresent  notpresent   \n",
      "375   79   80  1.025    0    0    normal    normal  notpresent  notpresent   \n",
      "376   70   80   1.02    0    0    normal    normal  notpresent  notpresent   \n",
      "377   58   70  1.025    0    0    normal    normal  notpresent  notpresent   \n",
      "378   64   70   1.02    0    0    normal    normal  notpresent  notpresent   \n",
      "379   71   60  1.025    0    0    normal    normal  notpresent  notpresent   \n",
      "380   62   80  1.025    0    0    normal    normal  notpresent  notpresent   \n",
      "381   59   60   1.02    0    0    normal    normal  notpresent  notpresent   \n",
      "382   71   70  1.025    0    0       NaN       NaN  notpresent  notpresent   \n",
      "383   48   80  1.025    0    0    normal    normal  notpresent  notpresent   \n",
      "384   80   80  1.025    0    0    normal    normal  notpresent  notpresent   \n",
      "385   57   60   1.02    0    0    normal    normal  notpresent  notpresent   \n",
      "386   63   70   1.02    0    0    normal    normal  notpresent  notpresent   \n",
      "387   46   70  1.025    0    0    normal    normal  notpresent  notpresent   \n",
      "388   15   80  1.025    0    0    normal    normal  notpresent  notpresent   \n",
      "389   51   80   1.02    0    0    normal    normal  notpresent  notpresent   \n",
      "390   41   80  1.025    0    0    normal    normal  notpresent  notpresent   \n",
      "391   52   80  1.025    0    0    normal    normal  notpresent  notpresent   \n",
      "392   36   80  1.025    0    0    normal    normal  notpresent  notpresent   \n",
      "393   57   80   1.02    0    0    normal    normal  notpresent  notpresent   \n",
      "394   43   60  1.025    0    0    normal    normal  notpresent  notpresent   \n",
      "395   50   80   1.02    0    0    normal    normal  notpresent  notpresent   \n",
      "396   55   80   1.02    0    0    normal    normal  notpresent  notpresent   \n",
      "397   42   70  1.025    0    0    normal    normal  notpresent  notpresent   \n",
      "398   12   80   1.02    0    0    normal    normal  notpresent  notpresent   \n",
      "399   17   60  1.025    0    0    normal    normal  notpresent  notpresent   \n",
      "400   58   80  1.025    0    0    normal    normal  notpresent  notpresent   \n",
      "\n",
      "     bgr   ...    pcv   wbcc rbcc  htn   dm  cad appet   pe  ane   Class  \n",
      "0    NaN   ...    NaN    NaN  NaN  NaN  NaN  NaN   NaN  NaN  NaN     NaN  \n",
      "1    121   ...     44   7800  5.2  yes  yes   no  good   no   no     ckd  \n",
      "2    NaN   ...     38   6000  NaN   no   no   no  good   no   no     ckd  \n",
      "3    423   ...     31   7500  NaN   no  yes   no  poor   no  yes     ckd  \n",
      "4    117   ...     32   6700  3.9  yes   no   no  poor  yes  yes     ckd  \n",
      "5    106   ...     35   7300  4.6   no   no   no  good   no   no     ckd  \n",
      "6     74   ...     39   7800  4.4  yes  yes   no  good  yes   no     ckd  \n",
      "7    100   ...     36    NaN  NaN   no   no   no  good   no   no     ckd  \n",
      "8    410   ...     44   6900    5   no  yes   no  good  yes   no     ckd  \n",
      "9    138   ...     33   9600    4  yes  yes   no  good   no  yes     ckd  \n",
      "10    70   ...     29  12100  3.7  yes  yes   no  poor   no  yes     ckd  \n",
      "11   490   ...     28    NaN  NaN  yes  yes   no  good   no  yes     ckd  \n",
      "12   380   ...     32   4500  3.8  yes  yes   no  poor  yes   no     ckd  \n",
      "13   208   ...     28  12200  3.4  yes  yes  yes  poor  yes   no     ckd  \n",
      "14    98   ...    NaN    NaN  NaN  yes  yes  yes  poor  yes   no     ckd  \n",
      "15   157   ...     16  11000  2.6  yes  yes  yes  poor  yes   no     ckd  \n",
      "16    76   ...     24   3800  2.8  yes   no   no  good   no  yes     ckd  \n",
      "17    99   ...    NaN    NaN  NaN   no   no   no  good   no   no     ckd  \n",
      "18   114   ...    NaN    NaN  NaN  yes   no   no  poor   no   no     ckd  \n",
      "19   263   ...     37  11400  4.3  yes  yes  yes  good   no   no     ckd  \n",
      "20   100   ...     30   5300  3.7  yes   no  yes  good   no   no     ckd  \n",
      "21   173   ...     24   9200  3.2  yes  yes  yes  poor  yes  yes     ckd  \n",
      "22   NaN   ...     32   6200  3.6  yes  yes  yes  good   no   no     ckd  \n",
      "23    95   ...     32   6900  3.4  yes   no   no  good   no  yes     ckd  \n",
      "24   NaN   ...    NaN    NaN  NaN   no   no   no  poor   no  yes     ckd  \n",
      "25   NaN   ...     39   8300  4.6  yes   no   no  poor   no   no     ckd  \n",
      "26   108   ...     29   8400  3.7  yes  yes   no  good   no  yes     ckd  \n",
      "27   156   ...     35  10300    4  yes  yes   no  poor   no   no     ckd  \n",
      "28   264   ...     37   9600  4.1  yes  yes  yes  good  yes   no     ckd  \n",
      "29   123   ...    NaN    NaN  NaN   no  yes   no  good   no   no     ckd  \n",
      "..   ...   ...    ...    ...  ...  ...  ...  ...   ...  ...  ...     ...  \n",
      "371   83   ...     50   9300  5.4   no   no   no  good   no   no  notckd  \n",
      "372   79   ...     51   6500    5   no   no   no  good   no   no  notckd  \n",
      "373  109   ...     52  10500  5.5   no   no   no  good   no   no  notckd  \n",
      "374  133   ...     47   9200  4.9   no   no   no  good   no   no  notckd  \n",
      "375  111   ...     40   8000  6.4   no   no   no  good   no   no  notckd  \n",
      "376   74   ...     48   9700  5.6   no   no   no  good   no   no  notckd  \n",
      "377   88   ...     53   9100  5.2   no   no   no  good   no   no  notckd  \n",
      "378   97   ...     49   6400  4.8   no   no   no  good   no   no  notckd  \n",
      "379  NaN   ...     42   7700  5.5   no   no   no  good   no   no  notckd  \n",
      "380   78   ...     50   5400  5.7   no   no   no  good   no   no  notckd  \n",
      "381  113   ...     54   6500  4.9   no   no   no  good   no   no  notckd  \n",
      "382   79   ...     40   5800  5.9   no   no   no  good   no   no  notckd  \n",
      "383   75   ...     51   6000  6.5   no   no   no  good   no   no  notckd  \n",
      "384  119   ...     49   5100    5   no   no   no  good   no   no  notckd  \n",
      "385  132   ...     42  11000  4.5   no   no   no  good   no   no  notckd  \n",
      "386  113   ...     52   8000  5.1   no   no   no  good   no   no  notckd  \n",
      "387  100   ...     43   5700  6.5   no   no   no  good   no   no  notckd  \n",
      "388   93   ...     50   6200  5.2   no   no   no  good   no   no  notckd  \n",
      "389   94   ...     46   9500  6.4   no   no   no  good   no   no  notckd  \n",
      "390  112   ...     52   7200  5.8   no   no   no  good   no   no  notckd  \n",
      "391   99   ...     52   6300  5.3   no   no   no  good   no   no  notckd  \n",
      "392   85   ...     44   5800  6.3   no   no   no  good   no   no  notckd  \n",
      "393  133   ...     46   6600  5.5   no   no   no  good   no   no  notckd  \n",
      "394  117   ...     54   7400  5.4   no   no   no  good   no   no  notckd  \n",
      "395  137   ...     45   9500  4.6   no   no   no  good   no   no  notckd  \n",
      "396  140   ...     47   6700  4.9   no   no   no  good   no   no  notckd  \n",
      "397   75   ...     54   7800  6.2   no   no   no  good   no   no  notckd  \n",
      "398  100   ...     49   6600  5.4   no   no   no  good   no   no  notckd  \n",
      "399  114   ...     51   7200  5.9   no   no   no  good   no   no  notckd  \n",
      "400  131   ...     53   6800  6.1   no   no   no  good   no   no  notckd  \n",
      "\n",
      "[401 rows x 25 columns]\n"
     ]
    }
   ],
   "source": [
    "##Ques 4. The dataset is not in clean form. Write a function that takes pandas dataframe as an input, remove these characters i-e ( \"\\t\", \" \") from the string value and replace with \"nan\" if the string value has only unicode characters i-e (\"?\"). The function should apply this transformation to all columns in dataframe and for cross check display unique values of Diabetes Mellitus(dm).\n",
    "def CleanData(arry, dataStFrm):\n",
    "    for i in arry:\n",
    "        dataStFrm.replace(to_replace=i, value=np.nan, inplace=True)\n",
    "\n",
    "CleanData(( \"\\t\", \" \", \"?\"), dataStFrm)\n",
    "\n",
    "print(dataStFrm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "('Unable to parse string \"\\t?\" at position 67', 'occurred at index pcv')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32mpandas\\src\\inference.pyx\u001b[0m in \u001b[0;36mpandas.lib.maybe_convert_numeric (pandas\\lib.c:55708)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Unable to parse string \"\t?\"",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-8b76c822c7fe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;31m##Ques 5. Change datatype to Numerical of following columns in Dataframe. columns = ['age', 'bp', 'bgr', 'bu', 'sc', 'sod', 'pot', 'hemo', 'pcv', 'wbcc', 'rbcc']\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataStFrm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'age'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'bp'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'bgr'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'bu'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'sc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'sod'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'pot'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'hemo'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'pcv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wbcc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rbcc'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'age'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'bp'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'bgr'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'bu'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'sc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'sod'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'pot'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'hemo'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'pcv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wbcc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rbcc'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'age'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'bp'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'bgr'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'bu'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'sc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'sod'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'pot'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'hemo'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'pcv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wbcc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rbcc'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, broadcast, raw, reduce, args, **kwds)\u001b[0m\n\u001b[1;32m   4150\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mreduce\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   4151\u001b[0m                         \u001b[0mreduce\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4152\u001b[0;31m                     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreduce\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4153\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   4154\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply_broadcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_apply_standard\u001b[0;34m(self, func, axis, ignore_failures, reduce)\u001b[0m\n\u001b[1;32m   4246\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   4247\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4248\u001b[0;31m                     \u001b[0mresults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4249\u001b[0m                     \u001b[0mkeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   4250\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\tools\\util.py\u001b[0m in \u001b[0;36mto_numeric\u001b[0;34m(arg, errors, downcast)\u001b[0m\n\u001b[1;32m    193\u001b[0m             \u001b[0mcoerce_numeric\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'ignore'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'raise'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m             values = lib.maybe_convert_numeric(values, set(),\n\u001b[0;32m--> 195\u001b[0;31m                                                coerce_numeric=coerce_numeric)\n\u001b[0m\u001b[1;32m    196\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas\\src\\inference.pyx\u001b[0m in \u001b[0;36mpandas.lib.maybe_convert_numeric (pandas\\lib.c:56097)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: ('Unable to parse string \"\\t?\" at position 67', 'occurred at index pcv')"
     ]
    }
   ],
   "source": [
    "##Ques 5. Change datatype to Numerical of following columns in Dataframe. columns = ['age', 'bp', 'bgr', 'bu', 'sc', 'sod', 'pot', 'hemo', 'pcv', 'wbcc', 'rbcc']\n",
    "df = pd.DataFrame(dataStFrm, columns=['age', 'bp', 'bgr', 'bu', 'sc', 'sod', 'pot', 'hemo', 'pcv', 'wbcc', 'rbcc'])\n",
    "df[['age', 'bp', 'bgr', 'bu', 'sc', 'sod', 'pot', 'hemo', 'pcv', 'wbcc', 'rbcc']] = df[['age', 'bp', 'bgr', 'bu', 'sc', 'sod', 'pot', 'hemo', 'pcv', 'wbcc', 'rbcc']].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>bgr</th>\n",
       "      <th>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "      <th>pcv</th>\n",
       "      <th>wbcc</th>\n",
       "      <th>rbcc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.4</td>\n",
       "      <td>44.0</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.3</td>\n",
       "      <td>38.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.6</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6700.0</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.6</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7300.0</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>142.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12.2</td>\n",
       "      <td>39.0</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>410.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.4</td>\n",
       "      <td>44.0</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>52.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.8</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9600.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>53.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>114.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>50.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.4</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>63.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>131.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>10.8</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>138.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>9.7</td>\n",
       "      <td>28.0</td>\n",
       "      <td>12200.0</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>135.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>9.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>68.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>130.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>40.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>141.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>7.6</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>47.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>12.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>47.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>139.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>12.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>60.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>135.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>12.7</td>\n",
       "      <td>37.0</td>\n",
       "      <td>11400.0</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>62.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>61.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>135.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>24.0</td>\n",
       "      <td>9200.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>60.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.9</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>48.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>136.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>9.8</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>21.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>42.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>129.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>8300.0</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>61.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>141.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>9.9</td>\n",
       "      <td>29.0</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>75.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>11.6</td>\n",
       "      <td>35.0</td>\n",
       "      <td>10300.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>69.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>130.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>37.0</td>\n",
       "      <td>9600.0</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>75.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>69.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>139.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>16.2</td>\n",
       "      <td>50.0</td>\n",
       "      <td>9300.0</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>28.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>145.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>72.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>150.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>15.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>61.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>13.7</td>\n",
       "      <td>47.0</td>\n",
       "      <td>9200.0</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>79.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>146.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>16.3</td>\n",
       "      <td>40.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>70.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>143.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>9700.0</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>58.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>147.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>16.4</td>\n",
       "      <td>53.0</td>\n",
       "      <td>9100.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>64.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>145.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>13.8</td>\n",
       "      <td>49.0</td>\n",
       "      <td>6400.0</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>71.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>15.2</td>\n",
       "      <td>42.0</td>\n",
       "      <td>7700.0</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>62.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>138.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>16.1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>59.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>139.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>15.3</td>\n",
       "      <td>54.0</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>71.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>142.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>16.6</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>48.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>137.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.8</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>141.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>13.9</td>\n",
       "      <td>49.0</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>57.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>15.4</td>\n",
       "      <td>42.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>63.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>146.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>16.5</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>46.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>142.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>16.4</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5700.0</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>15.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>136.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>16.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>144.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>15.5</td>\n",
       "      <td>46.0</td>\n",
       "      <td>9500.0</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>41.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>140.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>52.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>135.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>15.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>36.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>142.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>15.6</td>\n",
       "      <td>44.0</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>57.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>147.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>14.8</td>\n",
       "      <td>46.0</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>43.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>141.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>13.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>7400.0</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>50.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>139.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9500.0</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>55.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>150.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>15.7</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6700.0</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>42.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>54.0</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>12.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>137.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>15.8</td>\n",
       "      <td>49.0</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>14.2</td>\n",
       "      <td>51.0</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>58.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>15.8</td>\n",
       "      <td>53.0</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>401 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     bp    bgr     bu    sc    sod  pot  hemo   pcv     wbcc  rbcc\n",
       "0     NaN    NaN    NaN    NaN   NaN    NaN  NaN   NaN   NaN      NaN   NaN\n",
       "1    48.0   80.0  121.0   36.0   1.2    NaN  NaN  15.4  44.0   7800.0   5.2\n",
       "2     7.0   50.0    NaN   18.0   0.8    NaN  NaN  11.3  38.0   6000.0   NaN\n",
       "3    62.0   80.0  423.0   53.0   1.8    NaN  NaN   9.6  31.0   7500.0   NaN\n",
       "4    48.0   70.0  117.0   56.0   3.8  111.0  2.5  11.2  32.0   6700.0   3.9\n",
       "5    51.0   80.0  106.0   26.0   1.4    NaN  NaN  11.6  35.0   7300.0   4.6\n",
       "6    60.0   90.0   74.0   25.0   1.1  142.0  3.2  12.2  39.0   7800.0   4.4\n",
       "7    68.0   70.0  100.0   54.0  24.0  104.0  4.0  12.4  36.0      NaN   NaN\n",
       "8    24.0    NaN  410.0   31.0   1.1    NaN  NaN  12.4  44.0   6900.0   5.0\n",
       "9    52.0  100.0  138.0   60.0   1.9    NaN  NaN  10.8  33.0   9600.0   4.0\n",
       "10   53.0   90.0   70.0  107.0   7.2  114.0  3.7   9.5  29.0  12100.0   3.7\n",
       "11   50.0   60.0  490.0   55.0   4.0    NaN  NaN   9.4  28.0      NaN   NaN\n",
       "12   63.0   70.0  380.0   60.0   2.7  131.0  4.2  10.8  32.0   4500.0   3.8\n",
       "13   68.0   70.0  208.0   72.0   2.1  138.0  5.8   9.7  28.0  12200.0   3.4\n",
       "14   68.0   70.0   98.0   86.0   4.6  135.0  3.4   9.8   NaN      NaN   NaN\n",
       "15   68.0   80.0  157.0   90.0   4.1  130.0  6.4   5.6  16.0  11000.0   2.6\n",
       "16   40.0   80.0   76.0  162.0   9.6  141.0  4.9   7.6  24.0   3800.0   2.8\n",
       "17   47.0   70.0   99.0   46.0   2.2  138.0  4.1  12.6   NaN      NaN   NaN\n",
       "18   47.0   80.0  114.0   87.0   5.2  139.0  3.7  12.1   NaN      NaN   NaN\n",
       "19   60.0  100.0  263.0   27.0   1.3  135.0  4.3  12.7  37.0  11400.0   4.3\n",
       "20   62.0   60.0  100.0   31.0   1.6    NaN  NaN  10.3  30.0   5300.0   3.7\n",
       "21   61.0   80.0  173.0  148.0   3.9  135.0  5.2   7.7  24.0   9200.0   3.2\n",
       "22   60.0   90.0    NaN  180.0  76.0    4.5  NaN  10.9  32.0   6200.0   3.6\n",
       "23   48.0   80.0   95.0  163.0   7.7  136.0  3.8   9.8  32.0   6900.0   3.4\n",
       "24   21.0   70.0    NaN    NaN   NaN    NaN  NaN   NaN   NaN      NaN   NaN\n",
       "25   42.0  100.0    NaN   50.0   1.4  129.0  4.0  11.1  39.0   8300.0   4.6\n",
       "26   61.0   60.0  108.0   75.0   1.9  141.0  5.2   9.9  29.0   8400.0   3.7\n",
       "27   75.0   80.0  156.0   45.0   2.4  140.0  3.4  11.6  35.0  10300.0   4.0\n",
       "28   69.0   70.0  264.0   87.0   2.7  130.0  4.0  12.5  37.0   9600.0   4.1\n",
       "29   75.0   70.0  123.0   31.0   1.4    NaN  NaN   NaN   NaN      NaN   NaN\n",
       "..    ...    ...    ...    ...   ...    ...  ...   ...   ...      ...   ...\n",
       "371  69.0   70.0   83.0   42.0   1.2  139.0  3.7  16.2  50.0   9300.0   5.4\n",
       "372  28.0   60.0   79.0   50.0   0.5  145.0  5.0  17.6  51.0   6500.0   5.0\n",
       "373  72.0   60.0  109.0   26.0   0.9  150.0  4.9  15.0  52.0  10500.0   5.5\n",
       "374  61.0   70.0  133.0   38.0   1.0  142.0  3.6  13.7  47.0   9200.0   4.9\n",
       "375  79.0   80.0  111.0   44.0   1.2  146.0  3.6  16.3  40.0   8000.0   6.4\n",
       "376  70.0   80.0   74.0   41.0   0.5  143.0  4.5  15.1  48.0   9700.0   5.6\n",
       "377  58.0   70.0   88.0   16.0   1.1  147.0  3.5  16.4  53.0   9100.0   5.2\n",
       "378  64.0   70.0   97.0   27.0   0.7  145.0  4.8  13.8  49.0   6400.0   4.8\n",
       "379  71.0   60.0    NaN    NaN   0.9  140.0  4.8  15.2  42.0   7700.0   5.5\n",
       "380  62.0   80.0   78.0   45.0   0.6  138.0  3.5  16.1  50.0   5400.0   5.7\n",
       "381  59.0   60.0  113.0   23.0   1.1  139.0  3.5  15.3  54.0   6500.0   4.9\n",
       "382  71.0   70.0   79.0   47.0   0.5  142.0  4.8  16.6  40.0   5800.0   5.9\n",
       "383  48.0   80.0   75.0   22.0   0.8  137.0  5.0  16.8  51.0   6000.0   6.5\n",
       "384  80.0   80.0  119.0   46.0   0.7  141.0  4.9  13.9  49.0   5100.0   5.0\n",
       "385  57.0   60.0  132.0   18.0   1.1  150.0  4.7  15.4  42.0  11000.0   4.5\n",
       "386  63.0   70.0  113.0   25.0   0.6  146.0  4.9  16.5  52.0   8000.0   5.1\n",
       "387  46.0   70.0  100.0   47.0   0.5  142.0  3.5  16.4  43.0   5700.0   6.5\n",
       "388  15.0   80.0   93.0   17.0   0.9  136.0  3.9  16.7  50.0   6200.0   5.2\n",
       "389  51.0   80.0   94.0   15.0   1.2  144.0  3.7  15.5  46.0   9500.0   6.4\n",
       "390  41.0   80.0  112.0   48.0   0.7  140.0  5.0  17.0  52.0   7200.0   5.8\n",
       "391  52.0   80.0   99.0   25.0   0.8  135.0  3.7  15.0  52.0   6300.0   5.3\n",
       "392  36.0   80.0   85.0   16.0   1.1  142.0  4.1  15.6  44.0   5800.0   6.3\n",
       "393  57.0   80.0  133.0   48.0   1.2  147.0  4.3  14.8  46.0   6600.0   5.5\n",
       "394  43.0   60.0  117.0   45.0   0.7  141.0  4.4  13.0  54.0   7400.0   5.4\n",
       "395  50.0   80.0  137.0   46.0   0.8  139.0  5.0  14.1  45.0   9500.0   4.6\n",
       "396  55.0   80.0  140.0   49.0   0.5  150.0  4.9  15.7  47.0   6700.0   4.9\n",
       "397  42.0   70.0   75.0   31.0   1.2  141.0  3.5  16.5  54.0   7800.0   6.2\n",
       "398  12.0   80.0  100.0   26.0   0.6  137.0  4.4  15.8  49.0   6600.0   5.4\n",
       "399  17.0   60.0  114.0   50.0   1.0  135.0  4.9  14.2  51.0   7200.0   5.9\n",
       "400  58.0   80.0  131.0   18.0   1.1  141.0  3.5  15.8  53.0   6800.0   6.1\n",
       "\n",
       "[401 rows x 11 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda l: pd.to_numeric(l, errors='coerce'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEhCAYAAABvIFsXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x99025f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Ques 6. Count the number of normal or abnormal red blood cells people having Chronic Kidney Disease(CKD).\n",
    "ckd=dataStFrm[dataStFrm.Class=='ckd']\n",
    "ckd['rbc'].value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1       80\n",
       "2       50\n",
       "3       80\n",
       "4       70\n",
       "5       80\n",
       "6       90\n",
       "7       70\n",
       "8        0\n",
       "9      100\n",
       "10      90\n",
       "11      60\n",
       "12      70\n",
       "13      70\n",
       "14      70\n",
       "15      80\n",
       "16      80\n",
       "17      70\n",
       "18      80\n",
       "19     100\n",
       "20      60\n",
       "21      80\n",
       "22      90\n",
       "23      80\n",
       "24      70\n",
       "25     100\n",
       "26      60\n",
       "27      80\n",
       "28      70\n",
       "29      70\n",
       "      ... \n",
       "371     70\n",
       "372     60\n",
       "373     60\n",
       "374     70\n",
       "375     80\n",
       "376     80\n",
       "377     70\n",
       "378     70\n",
       "379     60\n",
       "380     80\n",
       "381     60\n",
       "382     70\n",
       "383     80\n",
       "384     80\n",
       "385     60\n",
       "386     70\n",
       "387     70\n",
       "388     80\n",
       "389     80\n",
       "390     80\n",
       "391     80\n",
       "392     80\n",
       "393     80\n",
       "394     60\n",
       "395     80\n",
       "396     80\n",
       "397     70\n",
       "398     80\n",
       "399     60\n",
       "400     80\n",
       "Name: bp, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Ques 7. Display maximum blood pressure having Chronic Kidney Disease(CKD).\n",
    "type(dataStFrm['bp'])\n",
    "dataStFrm['bp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90\n"
     ]
    }
   ],
   "source": [
    "dataStFrm['bp'] = dataStFrm['bp'].astype(int).astype('str')\n",
    "dataStFrm.fillna(0,inplace=True)\n",
    "print(dataStFrm['bp'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##Ques 8. Create a csv file of cleaned data in dataframe with name \"clean_chronic_kidney_disease.csv\"\n",
    "dataStFrm.to_csv(path_or_buf=\"D:/clean_chronic_kidney_disease.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}