01-ws_compare_Python_data_structures.ipynb

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   "source": [
    "## Intro to Python for Data Analysis"
   ]
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   "source": [
    "### Comparison of Python Data Structures for data analysis"
   ]
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   "source": [
    "Python Lists\n",
    "* use looping or iteration to perform element-wise operations"
   ]
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   "execution_count": 1,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7, 8]"
      ]
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     "execution_count": 1,
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   "source": [
    "a = [1,2,3,4]\n",
    "b = [5,6,7,8]\n",
    "a + b   #attention!"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [
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     "data": {
      "text/plain": [
       "[6, 8, 10, 12]"
      ]
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     "execution_count": 2,
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     "output_type": "execute_result"
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   "source": [
    "#list comprehension\n",
    "[sum(x) for x in zip(a, b)]"
   ]
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   "execution_count": 3,
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     "text": [
      "The slowest run took 4.48 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "100000 loops, best of 3: 2.07 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit ([sum(x) for x in zip(a, b)])"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy arrays\n",
    "* can perform vectorized data operations and computation\n",
    "* many built-in computational functions\n",
    "* used widely in scientific applications"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6,  8, 10, 12])"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "import numpy as np\n",
    "a = np.array([1,2,3,4])\n",
    "b = np.array([5,6,7,8])\n",
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 30.98 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "1000000 loops, best of 3: 676 ns per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit (a+b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "a.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas Series\n",
    "* allows for mixed types\n",
    "* can think of each Series as a column in a spreadsheet\n",
    "* labeled indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     6\n",
       "1     8\n",
       "2    10\n",
       "3    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "a = pd.Series([1,2,3,4])\n",
    "b = pd.Series([5,6,7,8])\n",
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 193 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit a+b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence, Pandas data structures have the flexibility for data analysis and science operations as well as access interop with numpy and general Python functionality\n",
    "makes it an ideal tool for analyzing data."
   ]
  }
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