13_numpy_pandas.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
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   "source": [
    "#creating series[1d] using numpy array\n",
    "data=np.array([1,2,3,4,5])\n",
    "s=pd.Series(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "4    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print (s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    vhr\n",
      "b    xyz\n",
      "c    lol\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#creating series using dictionary\n",
    "s=pd.Series({'a':'vhr','b':'xyz','c':'lol'})\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   -0.180027\n",
      "1    0.421755\n",
      "2   -0.921218\n",
      "3    0.276429\n",
      "4    0.713834\n",
      "5    0.378191\n",
      "6    1.031886\n",
      "7   -0.773363\n",
      "8   -0.083020\n",
      "9   -0.443917\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "s=pd.Series(np.random.randn(10))\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.18002693903371075\n",
      "0   -0.180027\n",
      "1    0.421755\n",
      "2   -0.921218\n",
      "3    0.276429\n",
      "dtype: float64\n",
      "9   -0.443917\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#accessing the series\n",
    "print(s[0])\n",
    "\n",
    "print(s[:4])\n",
    "\n",
    "print(s[-1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   breadth  length\n",
      "0        5       1\n",
      "1        6       2\n",
      "2       76       3\n",
      "3        8       4\n"
     ]
    }
   ],
   "source": [
    "#pandas dataframe\n",
    "\n",
    "df=pd.DataFrame([np.random.randn(5),np.random.randn(5)])\n",
    "\n",
    "\n",
    "#dataframe using dictionary\n",
    "dic={'length':[1,2,3,4],'breadth':[5,6,76,8]}\n",
    "df=pd.DataFrame(dic)\n",
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5  1]\n",
      " [ 6  2]\n",
      " [76  3]\n",
      " [ 8  4]]\n"
     ]
    }
   ],
   "source": [
    "print(df.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1     6\n",
       "2    76\n",
       "3     8\n",
       "Name: breadth, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['breadth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   breadth  length\n",
      "0        5       1\n",
      "1        6       2\n",
      "2       76       3\n",
      "3        8       4\n"
     ]
    }
   ],
   "source": [
    "print (df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1=pd.DataFrame({\"height\":[1,2,4,4]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[\"length\"]+df[\"breadth\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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    "df1"
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   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
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       "0     6\n",
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     "metadata": {},
     "output_type": "execute_result"
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    "df"
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