Sample Code/Day_02_SampleCode_v2.ipynb

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    "# 範例\n",
    "***"
   ]
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   "cell_type": "markdown",
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   "source": [
    "# [教學目標]\n",
    "\n",
    "* 認識 NumPy 中提供的資料型態\n",
    "* 了解各種型態的差異與定義\n",
    "* 正確的比較型態判斷\n",
    "\n"
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      "<module 'numpy' from '/Users/wei/.virtualenvs/py3/lib/python3.6/site-packages/numpy/__init__.py'>\n",
      "1.16.1\n"
     ]
    }
   ],
   "source": [
    "# 載入 NumPy 套件\n",
    "import numpy as np\n",
    "\n",
    "# 檢查正確載入與版本\n",
    "print(np)\n",
    "print(np.__version__)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "[[ 0  1  2  3  4]\n",
      " [ 5  6  7  8  9]\n",
      " [10 11 12 13 14]]\n"
     ]
    },
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     "data": {
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       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
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   "source": [
    "# 從一個陣列開始\n",
    "\n",
    "a = np.arange(15).reshape(3, 5)\n",
    "print(a) # 利用 print(...) 印出陣列\n",
    "a # 利用 Jupyter 印出陣列"
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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      "int64\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "# 陣列的常用屬性\n",
    "\n",
    "print(a.dtype) # int64\n",
    "print(a.itemsize) # 8\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# numpy 中的型態判斷\n",
    "\n",
    "print(a.dtype == 'int64') \n",
    "print(a.dtype is 'int64') \n",
    "print(a.dtype is np.int64) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# 資料型態的正確用法\n",
    "\n",
    "print(a.dtype == 'int64') # True\n",
    "print(a.dtype == np.int64) # True\n",
    "print(a.dtype is np.dtype('int64')) # True\n"
   ]
  },
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