{
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   "source": [
    "# Methods for Boolean arrays\n",
    "\n",
    "Boolean values have been converted to 1 (`True`) and 0 (`False`) in the previous methods. Therefore, `sum` is often used to count the `True` values in a Boolean array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "72b3a506",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d7908101",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.random.randn(7, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42925e49",
   "metadata": {},
   "source": [
    "Number of positive values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "afeb251f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(7)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data > 0).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c73241c0",
   "metadata": {},
   "source": [
    "Number of negative values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9f9dd3c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data < 0).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e417b114",
   "metadata": {},
   "source": [
    "There are two additional methods, `any` and `all`, which are particularly useful for Boolean arrays:\n",
    "\n",
    "* `any` checks whether one or more values in an array are true\n",
    "* `all` checks whether each value is true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e6052cdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True, False,  True],\n",
       "       [ True, False, False],\n",
       "       [False,  True, False],\n",
       "       [False, False,  True],\n",
       "       [False, False,  True],\n",
       "       [ True, False, False],\n",
       "       [ True,  True, False]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = np.random.randn(7, 3)\n",
    "\n",
    "bools = data > data2\n",
    "\n",
    "bools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cf41bfea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.True_"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bools.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "26dc76d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.False_"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bools.all()"
   ]
  }
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