20150410-dojo-max-price.ipynb

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   "source": [
    "from __future__ import print_function"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filename = '20150410-dojo-prices.txt'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show what is in the file."
   ]
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "IBM 3\n",
      "AAPL 3.1415926\n",
      "GOGL 10\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(open(filename).read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show how split method splits each line into a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['IBM', '3']\n",
      "['AAPL', '3.1415926']\n",
      "['GOGL', '10']\n"
     ]
    }
   ],
   "source": [
    "for line in open(filename):\n",
    "    print(line.split())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Straightforward loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\n"
     ]
    }
   ],
   "source": [
    "max_price = 0.0\n",
    "for line in open(filename):\n",
    "    price = float(line.split()[1])\n",
    "    max_price = max(max_price, price)\n",
    "print(max_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a generator expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   },
   "outputs": [
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     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max((float(line.split()[1]) for line in open(filename)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functional approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   "outputs": [
    {
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       "10.0"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(map(lambda line: float(line.split()[1]), open(filename)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(map(float, map(lambda line: line.split()[1], open(filename))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(map(float, map(lambda x: x[1] ,map(lambda line: line.split(), open(filename)))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "min, max, sum, any, and all are some built-in reduce style functions."
   ]
  }
 ],
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