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Replace exercice_fr.ipynb

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module2/exo2/exercice_fr.ipynb
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{
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"cell_type": "code",
"execution_count": 1,
"metadata": {},
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"source": [
"import numpy as np"
]
},
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"cell_type": "code",
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"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",
"]"
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"cell_type": "code",
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"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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"DataS"
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{
"cell_type": "code",
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"data": {
"text/plain": [
"14.113000000000001"
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},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(DataS)"
]
},
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"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"
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"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
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"source": [
"np.max(DataS)"
]
},
{
"cell_type": "code",
"execution_count": 10,
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"outputs": [
{
"data": {
"text/plain": [
"14.5"
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},
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
......@@ -16,10 +522,9 @@
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