NumPyWorks_5.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "### 0. Declarations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "49324edc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3ae4a88",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "83f10e32",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[1,2,3],[4,5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4d03ee19",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.array([2, 4, 6, 8, -1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c771d418",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "wt = np.array([0.25, 0.50, 0.25, 0.50, 0.75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "872d03fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = np.array([1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbdc1389",
   "metadata": {},
   "source": [
    "### 1. Basic Statistics\n",
    "We can do basic statistics on arrays such as taking minimum, maximum or sum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "93bb4c03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0a4bcd6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "adb14c4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18591a58",
   "metadata": {},
   "source": [
    "We can add an axis info to the operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fd9ec56b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(a, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c6aecd63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 6])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(a, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "73603e1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 7, 9])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(a, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "39dab893",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6, 15])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(a, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71c6bb64",
   "metadata": {},
   "source": [
    "### 2. Statistical Operations\n",
    "*np.mean(array_name)* > gives us the mean of an array or a specified axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "99032d0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "853fa6d5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 5.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(a, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afb8e934",
   "metadata": {},
   "source": [
    "*np.median(array_name)* > gives us the median value of an array or a specified axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d93b1122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ecf3b1d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(a[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "48d44fdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(a[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "474b4a71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 5.])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(a, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fbff25a",
   "metadata": {},
   "source": [
    "*np.var(array_name)* > gives us the variance value of an array or a specified axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4a84c150",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9166666666666665"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b08e4730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.66666667, 0.66666667])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(a, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7098a5ec",
   "metadata": {},
   "source": [
    "*np.std(array_name)* > gives us the standart deviation of an array or a specified axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d4aeb473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.707825127659933"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b667f48c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.5, 1.5, 1.5])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(a, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3ca17552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81649658, 0.81649658])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(a, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de9b9ba5",
   "metadata": {},
   "source": [
    "*np.average(array_name, weights)* > gives us the weighted average of an array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3f2b0020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.2222222222222223"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(b, weights = wt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "672754ed",
   "metadata": {},
   "source": [
    "*np.percentile(array_name, percentile)* > gives us a value of an array at the given percentile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b7f5f700",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(c, 50)"
   ]
  }
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