python/four_numpy_methods.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting of Array\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T21:31:22.244242Z",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.arange(16).reshape((4, 4))\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T21:31:34.452753Z",
     "start_time": "2020-08-02T21:31:34.442462Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1],\n",
       "       [ 4,  5],\n",
       "       [ 8,  9],\n",
       "       [12, 13]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Horizontal split\n",
    "upper, lower = np.hsplit(A, 2)\n",
    "upper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T21:32:12.681989Z",
     "start_time": "2020-08-02T21:32:12.677320Z"
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   "outputs": [],
   "source": [
    "# Vertical split\n",
    "left, right = np.vsplit(A, 2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Min and Max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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     "end_time": "2020-08-02T21:32:24.436924Z",
     "start_time": "2020-08-02T21:32:24.426248Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 9, 3, 9],\n",
       "       [9, 5, 4, 8],\n",
       "       [5, 4, 9, 0]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "B = np.random.randint(10, size = (3,4))\n",
    "B "
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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     "end_time": "2020-08-02T21:32:28.931219Z",
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   "outputs": [
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       "0"
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     "execution_count": 7,
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   "source": [
    "B.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T21:32:54.870487Z",
     "start_time": "2020-08-02T21:32:54.860497Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 3, 0])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Minimum of columns\n",
    "B.min(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T21:32:58.795951Z",
     "start_time": "2020-08-02T21:32:58.790480Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 0])"
      ]
     },
     "execution_count": 9,
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   ],
   "source": [
    "# Minimum of rows\n",
    "B.min(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T21:33:15.501827Z",
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     "data": {
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       "69"
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     "execution_count": 10,
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   ],
   "source": [
    "# Sum of the whole array\n",
    "B.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T21:33:27.751650Z",
     "start_time": "2020-08-02T21:33:27.747969Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.75"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Mean of the whole array\n",
    "B.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boolean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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     "end_time": "2020-08-02T21:34:10.885016Z",
     "start_time": "2020-08-02T21:34:10.878790Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# how many families whose number of members above 5\n",
    "np.count_nonzero(B<5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T21:33:57.787195Z",
     "start_time": "2020-08-02T21:33:57.781249Z"
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    {
     "data": {
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       "5"
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     "execution_count": 13,
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   ],
   "source": [
    "# Or this\n",
    "np.sum(B<5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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     "end_time": "2020-08-02T21:34:38.304392Z",
     "start_time": "2020-08-02T21:34:38.301089Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
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     },
     "execution_count": 15,
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   ],
   "source": [
    "# find if any of the family has the number of members above 8\n",
    "np.any(B>8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T21:34:58.563717Z",
     "start_time": "2020-08-02T21:34:58.558046Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find if all families have the number of members above 2\n",
    "np.all(B>2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T21:35:32.158431Z",
     "start_time": "2020-08-02T21:35:32.152146Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#find how many families in the range 3 and 5\n",
    "np.sum((B >= 3) & (B <= 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T21:36:28.443892Z",
     "start_time": "2020-08-02T21:36:28.437348Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# find the number of families that are not less than 3 or above 5\n",
    "np.sum(~(B < 3) | (B > 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T21:36:41.577225Z",
     "start_time": "2020-08-02T21:36:41.567953Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 9, 9, 8, 9])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Peek at what numbers are above 5 in our data\n",
    "B[B > 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sorting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sort indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T21:37:01.923811Z",
     "start_time": "2020-08-02T21:37:01.919363Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 6])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([3, 1, 2, 4, 6])\n",
    "np.sort(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T21:37:22.719715Z",
     "start_time": "2020-08-02T21:37:22.716397Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0, 3, 4])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort indices\n",
    "indices = np.argsort(x)\n",
    "indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sort Columns or Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T21:37:54.387012Z",
     "start_time": "2020-08-02T21:37:54.384456Z"
    }
   },
   "outputs": [],
   "source": [
    "A = np.random.randint(0, 10, (4, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T21:38:02.946875Z",
     "start_time": "2020-08-02T21:38:02.943432Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 0, 5, 3, 2, 2],\n",
       "       [4, 1, 6, 5, 3, 3],\n",
       "       [6, 2, 8, 7, 6, 9],\n",
       "       [7, 6, 9, 8, 7, 9]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By columns\n",
    "np.sort(A, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T21:38:11.695002Z",
     "start_time": "2020-08-02T21:38:11.684975Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3, 5, 6, 7, 9],\n",
       "       [2, 3, 6, 7, 8, 9],\n",
       "       [3, 4, 5, 6, 7, 9],\n",
       "       [0, 2, 2, 3, 6, 8]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By rows\n",
    "np.sort(A, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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