Lecture 37 - Replace.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [],
   "source": [
    "ser1 = Series([1,2,3,4,1,2,3,4])"
   ]
  },
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   "execution_count": 3,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    1\n",
       "5    2\n",
       "6    3\n",
       "7    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   NaN\n",
       "1     2\n",
       "2     3\n",
       "3     4\n",
       "4   NaN\n",
       "5     2\n",
       "6     3\n",
       "7     4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1.replace(1,np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    100\n",
       "1      2\n",
       "2      3\n",
       "3    400\n",
       "4    100\n",
       "5      2\n",
       "6      3\n",
       "7    400\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1.replace([1,4],[100,400])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     2\n",
       "2     3\n",
       "3   NaN\n",
       "4     1\n",
       "5     2\n",
       "6     3\n",
       "7   NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1.replace({4:np.nan})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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