pandas_missing_data_management.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import Series,DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = Series(['one','two',np.nan,'four'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     one\n",
       "1     two\n",
       "2     NaN\n",
       "3    four\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we can use isnull() method to checkout if there is any missing value in DataSet\n",
    "data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     one\n",
       "1     two\n",
       "3    four\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we can also drop the value which is null using dropna() method\n",
    "data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "     0    1    2\n",
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "#similearly we can do this with DataFrame\n",
    "dframe1 =  DataFrame([[1,2,4],[np.nan,3,4],[5,6,np.nan],[np.nan,np.nan]])\n",
    "dframe1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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     "execution_count": 9,
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   "source": [
    "# SEE cArefullY , in third row there is no value we have added but it is still taking it and considering Nan,Let's\n",
    "# do this with more other things.\n",
    "dframe1 =  DataFrame([[1,2,4],[np.nan,3,4],[5,np.nan],[np.nan,np.nan]])\n",
    "dframe1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#now same here for [5,np.nan] it is taking it as Nan automatically\n",
    "dframe2 = DataFrame([[1,2,4],[np.nan,3,4],[5,6,np.nan],[np.nan,np.nan]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
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       "     0    1    2\n",
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     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "dframe2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
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       "     0    1    2\n",
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     "execution_count": 14,
     "metadata": {},
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   ],
   "source": [
    "# drop rows containing all null values \n",
    "dframe2.dropna(how='all')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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   "source": [
    "# drop all rows containing any null value \n",
    "dframe2.dropna(how='any')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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     "execution_count": 17,
     "metadata": {},
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   "source": [
    "dframe2.dropna(thresh=1)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
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   "outputs": [
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     "execution_count": 18,
     "metadata": {},
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   ],
   "source": [
    "dframe2.dropna(thresh=2)\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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    "dframe2.dropna(thresh=3)\n",
    "# thresh is used to find out row having atleast 3 contents atleast"
   ]
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   "cell_type": "code",
   "execution_count": 26,
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    "dframe2.dropna(inplace =False)"
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    "dframe2"
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  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "dframe3 = DataFrame([[1,2,4],[np.nan,3,4],[5,6,np.nan],[np.nan,np.nan]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
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   "outputs": [
    {
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    "dframe3.dropna(inplace =False)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
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   "outputs": [
    {
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       "1  NaN  3.0  4.0\n",
       "2  5.0  6.0  NaN\n",
       "3  NaN  NaN  NaN"
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     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "dframe3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <td>6.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
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       "     0    1    2\n",
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     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "dframe3.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <td>23.0</td>\n",
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       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0     1     2\n",
       "0   1.0   2.0   4.0\n",
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       "2   5.0   6.0  23.0\n",
       "3  23.0  23.0  23.0"
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     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "dframe3.fillna(23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>34.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>34.0</td>\n",
       "      <td>45.0</td>\n",
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       "3  34.0  45.0  99.0"
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     "execution_count": 35,
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    "# now fill specific values for specified columns\n",
    "dframe3.fillna({0:34,1:45,2:99})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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