From 0ee28b31202f7ec6d34445f062864cbf547f332f Mon Sep 17 00:00:00 2001 From: c04925e8fd2cce9653c6609b8fc11028 Date: Tue, 4 Aug 2020 14:18:10 +0000 Subject: [PATCH] Replace exercice_fr.ipynb --- module2/exo2/exercice_fr.ipynb | 513 ++++++++++++++++++++++++++++++++- 1 file changed, 509 insertions(+), 4 deletions(-) diff --git a/module2/exo2/exercice_fr.ipynb b/module2/exo2/exercice_fr.ipynb index 0bbbe37..f1d88e1 100644 --- a/module2/exo2/exercice_fr.ipynb +++ b/module2/exo2/exercice_fr.ipynb @@ -1,5 +1,511 @@ { - "cells": [], + "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": [ + "(14.0,\n", + " 7.6,\n", + " 11.2,\n", + " 12.8,\n", + " 12.5,\n", + " 9.9,\n", + " 14.9,\n", + " 9.4,\n", + " 16.9,\n", + " 10.2,\n", + " 14.9,\n", + " 18.1,\n", + " 7.3,\n", + " 9.8,\n", + " 10.9,\n", + 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\u001b[0;36m\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": { + "text/plain": [ + "[14.0,\n", + " 7.6,\n", + " 11.2,\n", + " 12.8,\n", + " 12.5,\n", + " 9.9,\n", + " 14.9,\n", + " 9.4,\n", + " 16.9,\n", + " 10.2,\n", + " 14.9,\n", + " 18.1,\n", + " 7.3,\n", + " 9.8,\n", + " 10.9,\n", + " 12.2,\n", + " 9.9,\n", + " 2.9,\n", + " 2.8,\n", + " 15.4,\n", + " 15.7,\n", + " 9.7,\n", + " 13.1,\n", + " 13.2,\n", + " 12.3,\n", + " 11.7,\n", + " 16.0,\n", + " 12.4,\n", + " 17.9,\n", + " 12.2,\n", + " 16.2,\n", + " 18.7,\n", + " 8.9,\n", + " 11.9,\n", + " 12.1,\n", + " 14.6,\n", + " 12.1,\n", + " 4.7,\n", + " 3.9,\n", + " 16.9,\n", + " 16.8,\n", + " 11.3,\n", + " 14.4,\n", + " 15.7,\n", + " 14.0,\n", + 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"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": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +522,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.8.3" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } - -- 2.18.1