first_class_function/higher_order_function.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fig', 'apple', 'cherry', 'banana', 'raspberry', 'strawberry']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据单词的长度排序，只需把 len 函数传给 key 参数\n",
    "fruits = ['strawberry', 'fig', 'apple', 'cherry', 'raspberry', 'banana']\n",
    "sorted(fruits, key=len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['banana', 'apple', 'fig', 'raspberry', 'strawberry', 'cherry']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据反向拼写给一个单词列表排序\n",
    "def reverse(word):\n",
    "    return word[::-1]\n",
    "\n",
    "\n",
    "sorted(fruits, key=reverse)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# map和filter与列表推导比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 2, 6, 24, 120]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from math import factorial\n",
    "list(map(factorial, range(6)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 2, 6, 24, 120]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[factorial(n) for n in range(6)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 6, 120]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(map(factorial, filter(lambda n: n % 2, range(6))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 6, 120]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[factorial(n) for n in range(6) if n % 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## reduce和sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4950"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在 Python 2 中， reduce 是内置函数，但是在 Python 3 中放到 functools 模块里了。这个函数最常用于求和, 可以使用内置的sum函数替换\n",
    "from functools import reduce\n",
    "from operator import add\n",
    "reduce(add, range(100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4950"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(range(100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## all 和 any\n",
    "> all 和 any 也是内置的归约函数。\n",
    "> all(iterable)\n",
    "    - 如果 iterable 的每个元素都是真值，返回 True； all([]) 返回 True。\n",
    "> any(iterable)\n",
    "    - 只要 iterable 中有元素是真值，就返回 True； any([]) 返回 False。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## functools.partial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 这个高阶函数用于部分应用一个函数。部分应用是指，基于一个函数创\n",
    "建一个新的可调用对象，把原函数的某些参数固定。使用这个函数可以把接受一个或多个\n",
    "参数的函数改编成需要回调的 API，这样参数更少。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> partial 的第一个参数是一个可调用对象，后面跟着任意个要绑定的定位参数和关键字参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 6, 9, 12, 15, 18, 21, 24, 27]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用 partial 把一个两参数函数改编成需要单参数的可调用对象\n",
    "from operator import mul\n",
    "from functools import partial\n",
    "triple = partial(mul, 3)\n",
    "list(map(triple, range(1, 10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import unicodedata, functools\n",
    "nfc = functools.partial(unicodedata.normalize, 'NFC')\n",
    "s1 = 'café'\n",
    "s2 = 'cafe\\u0301'\n",
    "s1 == s2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfc(s1) == nfc(s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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