tutorial/numpy.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "numpyはnpという名前でインポートされることがほとんどです"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "numpyではarrayという配列で計算を行います  \n",
    "ここでは[1, 2, 3, 4, 5]という要素をもったnumpyのarrayを用意しましょう"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-25T10:16:48.671603Z",
     "start_time": "2018-08-25T10:16:48.669527Z"
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   },
   "outputs": [],
   "source": [
    "arr = np.array([1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大きさはshapeでわかります"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-25T10:16:48.680356Z",
     "start_time": "2018-08-25T10:16:48.672775Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "arrayに対して足し算をしてみましょう"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-25T10:16:48.685235Z",
     "start_time": "2018-08-25T10:16:48.681590Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 5, 6, 7])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr + 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "すべての要素に+2されました  \n",
    "これをブロードキャストといいます"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "足し算だけでなく、他の演算子でも同じようなことが起きます  \n",
    "ルートをとってみましょう"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-25T10:16:48.688995Z",
     "start_time": "2018-08-25T10:16:48.686242Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  1.41421356,  1.73205081,  2.        ,  2.23606798])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "リストと同様にスライスで要素を抽出できます  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-25T10:16:48.693379Z",
     "start_time": "2018-08-25T10:16:48.689963Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-25T10:16:48.697185Z",
     "start_time": "2018-08-25T10:16:48.694541Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[3:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "numpyのarange関数はよく使います  \n",
    "arange関数を使って[0, 1, 2, 3, 4]という要素をもったarrayを簡単に作ることができます"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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     "end_time": "2018-08-25T10:16:48.701069Z",
     "start_time": "2018-08-25T10:16:48.698168Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "np.arange(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "arange関数にはstart, end, stepを指定できます  \n",
    "[2, 4, 6, 8]という2ずつ数字が増えていくarrayは次のように作ることができます"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-25T10:16:48.704800Z",
     "start_time": "2018-08-25T10:16:48.702121Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6, 8])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(2, 10, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "linspace関数もよく使います  \n",
    "linspace関数もstartからendまでのarrayを作る関数ですが、stepではなく要素の数を指定します  \n",
    "ここでは0から3までを11分割したarrayを作ってみます"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-25T10:16:48.709763Z",
     "start_time": "2018-08-25T10:16:48.705735Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.3,  0.6,  0.9,  1.2,  1.5,  1.8,  2.1,  2.4,  2.7,  3. ])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 3, 11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "二次元のarrayも簡単に作れます"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-25T10:16:48.713971Z",
     "start_time": "2018-08-25T10:16:48.710915Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3],\n",
       "       [5, 7]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.array([[1, 3],\n",
    "                 [5, 7]])\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-25T10:16:48.717982Z",
     "start_time": "2018-08-25T10:16:48.714930Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "save関数でarrayを保存できます  \n",
    "第一引数が保存する名前で、第二引数が保存するarrayです"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-25T10:16:48.722431Z",
     "start_time": "2018-08-25T10:16:48.719269Z"
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   },
   "outputs": [],
   "source": [
    "np.save('test.npy', arr2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "読み込みはload関数でできます"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-25T10:16:48.728431Z",
     "start_time": "2018-08-25T10:16:48.724097Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3],\n",
       "       [5, 7]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.load('test.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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