{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 8.9,\n",
       " 21.0)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataS=[14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-daf0c2421aa7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mData\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'Data' is not defined"
     ]
    }
   ],
   "source": [
    "Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 21.0]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.113000000000001"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(DataS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.8"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(DataS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(DataS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(DataS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mddof\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m<\u001b[0m\u001b[0mno\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m>\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mDocstring:\u001b[0m\n",
       "Compute the standard deviation along the specified axis.\n",
       "\n",
       "Returns the standard deviation, a measure of the spread of a distribution,\n",
       "of the array elements. The standard deviation is computed for the\n",
       "flattened array by default, otherwise over the specified axis.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "a : array_like\n",
       "    Calculate the standard deviation of these values.\n",
       "axis : None or int or tuple of ints, optional\n",
       "    Axis or axes along which the standard deviation is computed. The\n",
       "    default is to compute the standard deviation of the flattened array.\n",
       "\n",
       "    .. versionadded:: 1.7.0\n",
       "\n",
       "    If this is a tuple of ints, a standard deviation is performed over\n",
       "    multiple axes, instead of a single axis or all the axes as before.\n",
       "dtype : dtype, optional\n",
       "    Type to use in computing the standard deviation. For arrays of\n",
       "    integer type the default is float64, for arrays of float types it is\n",
       "    the same as the array type.\n",
       "out : ndarray, optional\n",
       "    Alternative output array in which to place the result. It must have\n",
       "    the same shape as the expected output but the type (of the calculated\n",
       "    values) will be cast if necessary.\n",
       "ddof : int, optional\n",
       "    Means Delta Degrees of Freedom.  The divisor used in calculations\n",
       "    is ``N - ddof``, where ``N`` represents the number of elements.\n",
       "    By default `ddof` is zero.\n",
       "keepdims : bool, optional\n",
       "    If this is set to True, the axes which are reduced are left\n",
       "    in the result as dimensions with size one. With this option,\n",
       "    the result will broadcast correctly against the input array.\n",
       "\n",
       "    If the default value is passed, then `keepdims` will not be\n",
       "    passed through to the `std` method of sub-classes of\n",
       "    `ndarray`, however any non-default value will be.  If the\n",
       "    sub-class' method does not implement `keepdims` any\n",
       "    exceptions will be raised.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "standard_deviation : ndarray, see dtype parameter above.\n",
       "    If `out` is None, return a new array containing the standard deviation,\n",
       "    otherwise return a reference to the output array.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "var, mean, nanmean, nanstd, nanvar\n",
       "ufuncs-output-type\n",
       "\n",
       "Notes\n",
       "-----\n",
       "The standard deviation is the square root of the average of the squared\n",
       "deviations from the mean, i.e., ``std = sqrt(mean(abs(x - x.mean())**2))``.\n",
       "\n",
       "The average squared deviation is normally calculated as\n",
       "``x.sum() / N``, where ``N = len(x)``.  If, however, `ddof` is specified,\n",
       "the divisor ``N - ddof`` is used instead. In standard statistical\n",
       "practice, ``ddof=1`` provides an unbiased estimator of the variance\n",
       "of the infinite population. ``ddof=0`` provides a maximum likelihood\n",
       "estimate of the variance for normally distributed variables. The\n",
       "standard deviation computed in this function is the square root of\n",
       "the estimated variance, so even with ``ddof=1``, it will not be an\n",
       "unbiased estimate of the standard deviation per se.\n",
       "\n",
       "Note that, for complex numbers, `std` takes the absolute\n",
       "value before squaring, so that the result is always real and nonnegative.\n",
       "\n",
       "For floating-point input, the *std* is computed using the same\n",
       "precision the input has. Depending on the input data, this can cause\n",
       "the results to be inaccurate, especially for float32 (see example below).\n",
       "Specifying a higher-accuracy accumulator using the `dtype` keyword can\n",
       "alleviate this issue.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> a = np.array([[1, 2], [3, 4]])\n",
       ">>> np.std(a)\n",
       "1.1180339887498949 # may vary\n",
       ">>> np.std(a, axis=0)\n",
       "array([1.,  1.])\n",
       ">>> np.std(a, axis=1)\n",
       "array([0.5,  0.5])\n",
       "\n",
       "In single precision, std() can be inaccurate:\n",
       "\n",
       ">>> a = np.zeros((2, 512*512), dtype=np.float32)\n",
       ">>> a[0, :] = 1.0\n",
       ">>> a[1, :] = 0.1\n",
       ">>> np.std(a)\n",
       "0.45000005\n",
       "\n",
       "Computing the standard deviation in float64 is more accurate:\n",
       "\n",
       ">>> np.std(a, dtype=np.float64)\n",
       "0.44999999925494177 # may vary\n",
       "\u001b[1;31mFile:\u001b[0m      d:\\wpy64-3830\\python-3.8.3.amd64\\lib\\site-packages\\numpy\\core\\fromnumeric.py\n",
       "\u001b[1;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.std?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.334094455301447"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(DataS,ddof=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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