diff --git a/module1/exo2/fichier-markdown.md b/module1/exo2/fichier-markdown.md index 433d70d1a417b86c138c9ad446d76e6caf7df628..eac8441693e8583f6a945af770620aa388a1122c 100644 --- a/module1/exo2/fichier-markdown.md +++ b/module1/exo2/fichier-markdown.md @@ -38,4 +38,7 @@ Une ligne de `code` ``` # Extrait de code -``` \ No newline at end of file +``` + + + diff --git a/module2/exo1/toy_notebook_en.ipynb b/module2/exo1/toy_notebook_en.ipynb deleted file mode 100644 index 0bbbe371b01e359e381e43239412d77bf53fb1fb..0000000000000000000000000000000000000000 --- a/module2/exo1/toy_notebook_en.ipynb +++ /dev/null @@ -1,25 +0,0 @@ -{ - "cells": [], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} - diff --git a/module2/exo1/toy_notebook_fr.ipynb b/module2/exo1/toy_notebook_fr.ipynb index 2b19dd6a167a05bbea9a9004bc43687633438854..01e695666655e3d40027b094d80753e0b27835a2 100644 --- a/module2/exo1/toy_notebook_fr.ipynb +++ b/module2/exo1/toy_notebook_fr.ipynb @@ -1,3 +1,4 @@ +<<<<<<< HEAD { "cells": [ { @@ -165,3 +166,10 @@ "nbformat": 4, "nbformat_minor": 2 } +======= +# A propos du calcul de $\pi$ + +## En demandant à la lib maths + +Mon ordinateur m'indique que $\pi$ vaut *approximativement* +>>>>>>> 5ad1b5b042f860c16a9c9d8ac1c75b735867c0e1 diff --git a/module2/exo2/exercice.ipynb b/module2/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..260f76529ca33f522d4fc8ea14aad798337f4458 100644 --- a/module2/exo2/exercice.ipynb +++ b/module2/exo2/exercice.ipynb @@ -1,5 +1,133 @@ { - "cells": [], + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Moyenne= 14.113000000000001\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "x= 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", + "\n", + "Moyenne = np.mean(x)\n", + "print('Moyenne=', Moyenne)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mediane= 14.5\n", + "le Minimum= 2.8\n", + "le Maximum= 23.4\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import statistics\n", + "\n", + "x= 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", + "\n", + "Mediane = statistics.median(x)\n", + "print('Mediane=', Mediane)\n", + "\n", + "Min = min(x)\n", + "Max = max(x)\n", + "\n", + "print('le Minimum=', Min)\n", + "print('le Maximum=', Max)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.312369534258399\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "x= 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", + "\n", + "Ecart_type = np.std(x)\n", + "print(Ecart_type)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.312369534258399\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "x= 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", + "\n", + "ddof= 1\n", + "ecart_type = np.std(x)\n", + "print(ecart_type)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.334094455301447\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "x= 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", + "\n", + "ecart_type = np.std(x, ddof=1)\n", + "print(ecart_type)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +144,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5b58a3ae26346460dd39e62a39c55243d7..268d1933fa647c67d63673ae796cbda2c9df0ac8 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -28,13 +28,11 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 3, + "metadata": {}, "outputs": [], "source": [ - "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-25.csv?v=dbw4m\"" ] }, { @@ -61,9 +59,976 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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205 rows × 10 columns

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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020240725237334219625255043356329383FRFrance
120240625266382251899280865399377421FRFrance
220240525301224286226316222452430474FRFrance
320240425312596297185328007469446492FRFrance
420240325260433246176274690390369411FRFrance
520240225223175209907236443335315355FRFrance
620240125207680192579222781311288334FRFrance
720235225204801189186220416308285331FRFrance
820235125281235264279298191423397449FRFrance
920235025281168265632296704423400446FRFrance
1020234925286802271137302467431407455FRFrance
1120234825247301232152262450372349395FRFrance
1220234725224971210414239528338316360FRFrance
1320234625170563157940183186257238276FRFrance
1420234525150125137230163020226207245FRFrance
152023442510860898016119200163147179FRFrance
162023432510515994706115612158142174FRFrance
1720234225123120111942134298185168202FRFrance
1820234125126477115697137257190174206FRFrance
1920234025145262132323158201219200238FRFrance
2020233925156932144286169578236217255FRFrance
2120233825140770128842152698212194230FRFrance
222023372510944098918119962165149181FRFrance
23202336259080281444100160137123151FRFrance
2420233525753536652184185113100126FRFrance
25202334256229853692709049481107FRFrance
2620233325513154186260768776391FRFrance
2720233225444113680352019675678FRFrance
2820233125450463708953003685680FRFrance
2920233025436453531951971665379FRFrance
.................................
17520204125819547392189987124112136FRFrance
1762020402567761603107521210392114FRFrance
1772020392568999613507664810593117FRFrance
17820203825886388012297154135122148FRFrance
17920203725564544977663132867696FRFrance
18020203625241491999128307373143FRFrance
18120203525198311552824134302337FRFrance
1822020342511690787615504181224FRFrance
1832020332510848714514551161022FRFrance
1842020322511409742215396171123FRFrance
1852020312513366941217320201426FRFrance
18620203025214281660926247332640FRFrance
18720202925222401757826902342741FRFrance
18820202825175811371521447272133FRFrance
1892020272510817795713677161220FRFrance
1902020262571264846940611814FRFrance
1912020252570534747935911715FRFrance
192202024255269349370458511FRFrance
1932020232567684615892110713FRFrance
19420202225874861921130413917FRFrance
19520202125146511135317949221727FRFrance
19620202025284162387532957433650FRFrance
19720201925273472288031814423549FRFrance
19820201825346182949939737534561FRFrance
19920201725459034003251774706179FRFrance
200202016256096954118678209383103FRFrance
201202015259536486665104063145132158FRFrance
20220201425213772201334226210325306344FRFrance
20320201325297819283636312002452430474FRFrance
20420201225273062259400286724415394436FRFrance
\n", + "

205 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202407 25 237334 219625 255043 356 329 \n", + "1 202406 25 266382 251899 280865 399 377 \n", + "2 202405 25 301224 286226 316222 452 430 \n", + "3 202404 25 312596 297185 328007 469 446 \n", + "4 202403 25 260433 246176 274690 390 369 \n", + "5 202402 25 223175 209907 236443 335 315 \n", + "6 202401 25 207680 192579 222781 311 288 \n", + "7 202352 25 204801 189186 220416 308 285 \n", + "8 202351 25 281235 264279 298191 423 397 \n", + "9 202350 25 281168 265632 296704 423 400 \n", + "10 202349 25 286802 271137 302467 431 407 \n", + "11 202348 25 247301 232152 262450 372 349 \n", + "12 202347 25 224971 210414 239528 338 316 \n", + "13 202346 25 170563 157940 183186 257 238 \n", + "14 202345 25 150125 137230 163020 226 207 \n", + "15 202344 25 108608 98016 119200 163 147 \n", + "16 202343 25 105159 94706 115612 158 142 \n", + "17 202342 25 123120 111942 134298 185 168 \n", + "18 202341 25 126477 115697 137257 190 174 \n", + "19 202340 25 145262 132323 158201 219 200 \n", + "20 202339 25 156932 144286 169578 236 217 \n", + "21 202338 25 140770 128842 152698 212 194 \n", + "22 202337 25 109440 98918 119962 165 149 \n", + "23 202336 25 90802 81444 100160 137 123 \n", + "24 202335 25 75353 66521 84185 113 100 \n", + "25 202334 25 62298 53692 70904 94 81 \n", + "26 202333 25 51315 41862 60768 77 63 \n", + "27 202332 25 44411 36803 52019 67 56 \n", + "28 202331 25 45046 37089 53003 68 56 \n", + "29 202330 25 43645 35319 51971 66 53 \n", + ".. ... ... ... ... ... ... ... \n", + "175 202041 25 81954 73921 89987 124 112 \n", + "176 202040 25 67761 60310 75212 103 92 \n", + "177 202039 25 68999 61350 76648 105 93 \n", + "178 202038 25 88638 80122 97154 135 122 \n", + "179 202037 25 56454 49776 63132 86 76 \n", + "180 202036 25 24149 19991 28307 37 31 \n", + "181 202035 25 19831 15528 24134 30 23 \n", + "182 202034 25 11690 7876 15504 18 12 \n", + "183 202033 25 10848 7145 14551 16 10 \n", + "184 202032 25 11409 7422 15396 17 11 \n", + "185 202031 25 13366 9412 17320 20 14 \n", + "186 202030 25 21428 16609 26247 33 26 \n", + "187 202029 25 22240 17578 26902 34 27 \n", + "188 202028 25 17581 13715 21447 27 21 \n", + "189 202027 25 10817 7957 13677 16 12 \n", + "190 202026 25 7126 4846 9406 11 8 \n", + "191 202025 25 7053 4747 9359 11 7 \n", + "192 202024 25 5269 3493 7045 8 5 \n", + "193 202023 25 6768 4615 8921 10 7 \n", + "194 202022 25 8748 6192 11304 13 9 \n", + "195 202021 25 14651 11353 17949 22 17 \n", + "196 202020 25 28416 23875 32957 43 36 \n", + "197 202019 25 27347 22880 31814 42 35 \n", + "198 202018 25 34618 29499 39737 53 45 \n", + "199 202017 25 45903 40032 51774 70 61 \n", + "200 202016 25 60969 54118 67820 93 83 \n", + "201 202015 25 95364 86665 104063 145 132 \n", + "202 202014 25 213772 201334 226210 325 306 \n", + "203 202013 25 297819 283636 312002 452 430 \n", + "204 202012 25 273062 259400 286724 415 394 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 383 FR France \n", + "1 421 FR France \n", + "2 474 FR France \n", + "3 492 FR France \n", + "4 411 FR France \n", + "5 355 FR France \n", + "6 334 FR France \n", + "7 331 FR France \n", + "8 449 FR France \n", + "9 446 FR France \n", + "10 455 FR France \n", + "11 395 FR France \n", + "12 360 FR France \n", + "13 276 FR France \n", + "14 245 FR France \n", + "15 179 FR France \n", + "16 174 FR France \n", + "17 202 FR France \n", + "18 206 FR France \n", + "19 238 FR France \n", + "20 255 FR France \n", + "21 230 FR France \n", + "22 181 FR France \n", + "23 151 FR France \n", + "24 126 FR France \n", + "25 107 FR France \n", + "26 91 FR France \n", + "27 78 FR France \n", + "28 80 FR France \n", + "29 79 FR France \n", + ".. ... ... ... \n", + "175 136 FR France \n", + "176 114 FR France \n", + "177 117 FR France \n", + "178 148 FR France \n", + "179 96 FR France \n", + "180 43 FR France \n", + "181 37 FR France \n", + "182 24 FR France \n", + "183 22 FR France \n", + "184 23 FR France \n", + "185 26 FR France \n", + "186 40 FR France \n", + "187 41 FR France \n", + "188 33 FR France \n", + "189 20 FR France \n", + "190 14 FR France \n", + "191 15 FR France \n", + "192 11 FR France \n", + "193 13 FR France \n", + "194 17 FR France \n", + "195 27 FR France \n", + "196 50 FR France \n", + "197 49 FR France \n", + "198 61 FR France \n", + "199 79 FR France \n", + "200 103 FR France \n", + "201 158 FR France \n", + "202 344 FR France \n", + "203 474 FR France \n", + "204 436 FR France \n", + "\n", + "[205 rows x 10 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -122,7 +2103,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -152,10 +2133,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -179,7 +2158,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -199,9 +2178,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sorted_data['inc'][-200:].plot()" ] @@ -252,14 +2277,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", - " for y in range(1985,\n", + " for y in range(2020,\n", " sorted_data.index[-1].year)]" ] }, @@ -274,7 +2297,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -298,9 +2321,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "yearly_incidence.plot(style='*')" ] @@ -314,9 +2360,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2021 2394230\n", + "2022 4728673\n", + "2023 5616796\n", + "dtype: int64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -331,9 +2391,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "yearly_incidence.hist(xrot=20)" ] @@ -364,7 +2447,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/toy_notebook_fr.ipynb b/toy_notebook_fr.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..39f398e5b441c7569e77e0723ad499712391dc02 --- /dev/null +++ b/toy_notebook_fr.ipynb @@ -0,0 +1,6 @@ +# A proopos du calcul de $\pi$ + +## En demandant à la lib maths + +Mon ordinateur m'indique que $\pi$ vaut *approximativement* +