Day_017_HW.ipynb

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   "source": [
    "# 作業\n",
    "- 新增一個欄位 `customized_age_grp`，把 `age` 分為 (0, 10], (10, 20], (20, 30], (30, 50], (50, 100] 這五組，\n",
    "'(' 表示不包含, ']' 表示包含  \n",
    "- Hints: 執行 ??pd.cut()，了解提供其中 bins 這個參數的使用方式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業目標]\n",
    "- 請同學試著查詢 pandas.cut 這個函數還有哪些參數, 藉由改動參數以達成目標\n",
    "- 藉由查詢與改動參數的過程, 熟悉查詢函數的方法與理解參數性質, 並了解數值的離散化的調整工具"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 仿照 In[3], In[4] 的語法, 並設定 pd.cut 的參數以指定間距"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 載入套件\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 初始設定 Ages 的資料\n",
    "ages = pd.DataFrame({\"age\": [18,22,25,27,7,21,23,37,30,61,45,41,9,18,80,100]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 等寬劃分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 新增欄位 \"equal_width_age\", 對年齡做等寬劃分\n",
    "ages[\"equal_width_age\"] = pd.cut(ages[\"age\"], 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.907, 30.25]    10\n",
       "(30.25, 53.5]      3\n",
       "(76.75, 100.0]     2\n",
       "(53.5, 76.75]      1\n",
       "Name: equal_width_age, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 觀察等寬劃分下, 每個種組距各出現幾次\n",
    "ages[\"equal_width_age\"].value_counts() # 每個 bin 的值的範圍大小都是一樣的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 等頻劃分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 新增欄位 \"equal_freq_age\", 對年齡做等頻劃分\n",
    "ages[\"equal_freq_age\"] = pd.qcut(ages[\"age\"], 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42.0, 100.0]     4\n",
       "(26.0, 42.0]      4\n",
       "(20.25, 26.0]     4\n",
       "(6.999, 20.25]    4\n",
       "Name: equal_freq_age, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 觀察等頻劃分下, 每個種組距各出現幾次\n",
    "ages[\"equal_freq_age\"].value_counts() # 每個 bin 的資料筆數是一樣的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 30]     6\n",
       "(50, 100]    3\n",
       "(30, 50]     3\n",
       "(10, 20]     2\n",
       "(0, 10]      2\n",
       "Name: customized_age_grp, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [0, 10, 20, 30, 50,100]\n",
    "ages[\"customized_age_grp\"] = pd.cut(ages[\"age\"], bins)\n",
    "ages[\"customized_age_grp\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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