03. Python 資料分析主流環境 Jupyter.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1-1 魔術指令\n",
    "\n",
    "在 Jupyter 中有許多「魔術指令」, 讓我們更方便的用 Python! 每個魔術指令就是用百分比符號開始, 我們介紹第一個非常有用的\n",
    "\n",
    "    %cd 資料夾路徑\n",
    "    \n",
    "熟悉指令列的就知道這就會進入指定資料夾。\n",
    "\n",
    "接著我們把標準資料分析主要套件讀進來, 唯一要說明的大概是\n",
    "\n",
    "    %matplotlib inline\n",
    "\n",
    "這是說之後畫出的圖, 直接在我們 Jupyter Notebook 的網頁中呈現出來!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1-2 自由方便的試驗場"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "還可以取好高級的亂數, 從平均數 0, 標準差 1 的常態分佈中取出 n 個數字。\n",
    "\n",
    "    randn(n)\n",
    "    \n",
    "要平均值 m, 標準差 s 的時候呢?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "μ = 87\n",
    "σ = 2.5\n",
    "\n",
    "eggs = np.random.randn(100) * σ + μ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.960831113962"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eggs.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5872714248131534"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eggs.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【技巧】使用 `seaborn` 套件\n",
    "\n",
    "`seaborn` 套件可以簡單畫漂亮的圖!\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yenlung/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11de23978>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(eggs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【技巧】 `zip` 和 `upzip`\n",
    "\n",
    "有時我們這種\n",
    "\n",
    "$$x = [x_1, x_2, \\ldots, x_n]$$\n",
    "\n",
    "$$y = [y_1, y_2, \\ldots, y_n]$$\n",
    "\n",
    "表示的資料, 想換成\n",
    "\n",
    "$$(x_1, y_1), (x_2, y_2), \\ldots (x_n, y_n)$$\n",
    "\n",
    "這要怎麼做呢?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1, 2, 3, 4, 5]\n",
    "y = [6, 7, 8, 9, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "points = list(zip(x,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 6), (2, 7), (3, 8), (4, 9), (5, 0)]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "是不是很簡單? 再來如果我們本來是上面這種形式的資料, 怎麼把 x, y 座標分開呢? 非常神的居然還是 `zip`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 2, 3, 4, 5), (6, 7, 8, 9, 0)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(*points))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "所以..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = list(zip(*points))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3, 4, 5)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 7, 8, 9, 0)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【原理】\n",
    "\n",
    "這原理是什麼呢? 原來 `zip` 可以看成「把每群同一號的編成一組」。首先, 我們有\n",
    "\n",
    "$$x = [x_1, x_2, \\ldots, x_n]$$\n",
    "\n",
    "$$y = [y_1, y_2, \\ldots, y_n]$$\n",
    "\n",
    "兩群, `zip` 後就是, 雙方一號變成一組, 二號變成一組, ... 於是就成為我們要的:\n",
    "\n",
    "$$(x_1, y_1), (x_2, y_2), \\ldots (x_n, y_n)$$\n",
    "\n",
    "反過來為什麼還是 `zip` 呢? 原因是現在我們有 $n$ 群, 每群都只有兩個元素, 分別叫 1 號和 2 號好了。於是 `zip` 就是「來, 1 號集合」, 還有 「2 號集合」, 於是正是我們要的分別把 $x$ 和 $y$ 都放入各自的一群!\n",
    "\n",
    "最後是 `*points` 的 `*` 是什麼呢? 有傷痛回憶的同學, 千萬不要想起 C 的那段傷痛回憶。在 Python 只是說 `points` 中的所有元素, 我們也不知有多少個, 就打個 `*` 就可以!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1-3 親切的輔助系統\n",
    "\n",
    "* Tab 補完神鍵\n",
    "* 函數打完不知怎麼用, 可以按\n",
    "\n",
    "![Shift-Tab](images/shift_tab.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1-4 用 Markdown 做筆記\n",
    "\n",
    "Cell 可以選 `Markdown` 模式, 就可以用 `Markdown` 語法做筆記。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【重點】標題\n",
    "\n",
    "`# 最大標題`\n",
    "# 最大標題\n",
    "\n",
    "`## 副標題`\n",
    "## 副標題\n",
    "\n",
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 最大標題\n",
    "## 第二標題\n",
    "\n",
    "內文胡說八道, 還可修改"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【重點】分點說明\n",
    "\n",
    "我們要學以下幾個套件:\n",
    "\n",
    "* `matplotlib`\n",
    "* `numpy`\n",
    "* `pandas`\n",
    "* `scikit-learn`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "我們要學的有\n",
    "\n",
    "* Python\n",
    "* matplotlib\n",
    "* sklearn\n",
    "* pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【重點】程式碼\n",
    "\n",
    "顯示程式碼有幾種方式。\n",
    "\n",
    "#### [1] 前面有若干空白\n",
    "\n",
    "    for i in range(10):\n",
    "        print(i)\n",
    "        \n",
    "#### [2] 內文可以用斜引號\n",
    "\n",
    "    `matplotlib`\n",
    "    \n",
    "#### [3] 還可以用 3 個引號外加語言\n",
    "\n",
    "    ```python\n",
    "    for i in range(10):\n",
    "        print(i)\n",
    "    ```\n",
    "\n",
    "```python\n",
    "for i in range(10):\n",
    "    print(i)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "一段程式\n",
    "\n",
    "    for i in range(10):\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一段程式\n",
    "\n",
    "```python\n",
    "for i in range(10):\n",
    "    print(i)\n",
    "``` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【重點】插入網頁\n",
    "\n",
    "格式如下\n",
    "\n",
    "    [網頁名稱](URL)\n",
    "    \n",
    "例如\n",
    "\n",
    "    [炎龍老師的網頁](http://yenlung.km.nccu.edu.tw)\n",
    "    \n",
    "結果: [炎龍老師的網頁](http://yenlung.km.nccu.edu.tw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "[炎龍老師 GitHub 程式碼](http://bit.ly/yenlung)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【重點】插入圖片\n",
    "\n",
    "    ![圖片名稱](圖片路徑)\n",
    "    \n",
    "例如\n",
    "\n",
    "    ![Happy Python](images/take_photo.png)\n",
    "    \n",
    "![Happy Python](images/take_photo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【重點】輸入數學符號\n",
    "\n",
    "支援 LaTeX 指令。\n",
    "\n",
    "#### 隨文模式\n",
    "\n",
    "例如\n",
    "\n",
    "    設我們有函數 $f(x) = x^3 - 2x + 5$。\n",
    "    \n",
    "[結果] 設我們有函數 $f(x) = x^3 - 2x + 5$。\n",
    "\n",
    "#### 展式模式\n",
    "\n",
    "例如\n",
    "\n",
    "    $$\\int_a^b f(x) \\, dx$$\n",
    "\n",
    "$$\\int_a^b f(x) \\, dx$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "令一個可愛的函數 $f(x)=x^3 - 2x + 5$\n",
    "\n",
    "$$\\int_a^b f(x) \\, dx$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1-5 超炫的互動\n",
    "\n",
    "Jupyter Notebook 的互動模式, 提供非常好的科學計算 GUI 實驗場!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定義一個函數, 就能互動!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import interact"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【範例】數值滑桿"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca185a95559240e6a1ec570319e23c57",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=3, description='x', max=9, min=-3), Output()), _dom_classes=('widget-int…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.f(x)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(f, x=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也可以是浮點數。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b5e28333f0c1441b9c4a4be52eda4af2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=3.0, description='x', max=9.0, min=-3.0), Output()), _dom_classes=('wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.f(x)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(f, x=3.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "還可以設範圍。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cf55059c6d244241b135d752bc59422a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=5, description='x', max=10, min=1), Output()), _dom_classes=('widget-int…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(f, x=(1, 10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【範例】文字框"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "856ea6f39694429388f6b235377ba6c0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Text(value='你好', description='x'), Output()), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(f, x=\"你好\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【範例】下拉式選單之一"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "65af68a2d5e14b6da074feccdea0a389",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='x', options=('台北', '台中', '高雄'), value='台北'), Output()), _dom_class…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.f(x)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(f, x=['台北', '台中', '高雄'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【範例】下拉式選單之二"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "317a096d9fc9458bb7efdac578696a7a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='x', options={'台北': 1, '台中': 2, '高雄': 3}, value=1), Output()), _dom…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.f(x)>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(f, x={'台北':1, '台中':2, '高雄':3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import interact_manual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e0ff7dfc21f54b76a70b70a44ea2853d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Text(value='請輸入大名', description='x'), Button(description='Run Interact', style=ButtonSty…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.f(x)>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact_manual(f, x=\"請輸入大名\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【例子】會跑的蟲\n",
    "\n",
    "順便示範怎麼樣設預設值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def move(n=1):\n",
    "    print(' '*n + 'ooooo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ooooo\n"
     ]
    }
   ],
   "source": [
    "move()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               ooooo\n"
     ]
    }
   ],
   "source": [
    "move(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "96d6d44f355d4ed597c70c69af9bdd66",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='n', max=50, min=1), Output()), _dom_classes=('widget-int…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(move, n=(1, 50));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【例子】視覺化的互動"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(-10, 10, 1000)\n",
    "\n",
    "def myplot(n=1):\n",
    "    plt.plot(x, np.sin(n*x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1bef240facde474cab40d014a31ee972",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=1, description='n', max=50, min=1), Output()), _dom_classes=('widget-int…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(myplot, n=(1, 50));"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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