Commit f735ec7a authored by vincentvigon's avatar vincentvigon
Browse files

passage a gitlab

parent 23e577d5
File deleted
.idea/
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No preview for this file type
%% Cell type:markdown id: tags:
# histogrammes et densités
%% Cell type:code id: tags:
``` python
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
```
%% Cell type:markdown id: tags:
## Graphiques en batons
bonjour toto \index{toto}
%% Cell type:code id: tags:
``` python
x= [1,1.5,2,2.5,3]
y= [1,4,3,4,1]
""" Par défaut width=0.8, ce qui ne ferait pas joli ici"""
plt.bar(x,y,edgecolor="k",width=0.5); # "k" c'est black
```
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:code id: tags:
``` python
""" Population totale par sexe et âge au 1er janvier 2018, France. source:
https://www.insee.fr/fr/statistiques/fichier/1892086/pop-totale-france.xls """
nb_hommes=[370264,380786,390091,398757,409096,419458,422341,433633,433293,435936,432382,439050,429300,427923,426042,428943,436599,442760,420335,411745,397771,394287,384071,365958,361467,378875,380348,386998,386152,391032,392666,397890,400708,396839,392953,417916,422120,429147,406565,399665,406674,394569,405691,428904,452528,462957,459928,451838,443890,438042,436591,445267,446241,448156,443106,425952,424946,421198,414831,404537,400466,395119,387700,385660,374649,378923,368702,381299,370877,367734,357172,334517,249885,240416,230819,211303,184231,186016,189134,179799,169183,162468,149256,142187,126683,119777,106150,94411,76897,65760,54174,43318,35138,26429,19424,14138,10396,7400,2994,1612,2929,]
nb_femmes=[354226,363749,373574,386477,389867,398597,405611,415679,412153,415631,413237,420649,411513,409866,407270,408649,416111,421402,398604,394150,380429,381718,374299,363324,360271,378034,385787,396563,401533,408101,410675,420196,419362,418329,413567,437539,440858,446720,423369,413791,414315,405569,414926,435625,461600,470512,467329,459041,453968,453315,449798,459334,459223,465447,461150,445813,446626,446154,444025,434265,433063,429825,425865,424456,416053,420938,410389,425522,417056,411404,404106,382498,289497,281703,273366,251816,224905,234778,243629,239419,233198,231156,222866,220997,205946,204422,188772,178310,153484,139112,120327,105685,90293,73890,60007,48181,36624,27518,11907,7042,13945,]
nb_femmes=-np.array(nb_femmes)
ages=range(0,len(nb_hommes))
plt.figure(figsize=(10,10))
plt.bar(ages,nb_hommes,width=1,label="hommes")
plt.bar(ages,nb_femmes,width=1,label="femmes")
xticks=np.arange(0,101,10)
plt.xticks(xticks)
for x in xticks: plt.axvline(x,color="0.9",linewidth=0.3)
plt.legend();
```
%%%% Output: display_data
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ROoAMA6JxABwDQOYEOAKBzAh0AQOcEOgCAzgl0AACdE+gAADon0AEAdE6gAwDonEAHANC5RfN9AAAw15adc8Oz2h6+4I3zcCQwGip0AACdE+gAADon0AEAdE6gAwDonEURABALJeibCh0AQOcEOgCAzgl0AACdE+gAADpnUQQAbIGFEvRChQ4AoHMCHQBA5wQ6AIDOCXQAAJ0T6AAAOifQAQB0TqADAOicQAcA0DmBDgCgc74pAgB2gG+PYByp0AEAdE6gAwDo3E4Huqrar6puq6qvVtV9VfWeof1FVXVLVT04/H7h0F5VdVFVra2qe6rqVdMe65Rh+wer6pRp7a+uqnuH+1xUVbW1fQAALCSjqNBtSvJbrbVDkhyT5MyqOiTJOUk+11o7MMnnhutJ8vokBw4/pyf5WDIVzpL8fpKjkxyV5PenBbSPJXnXtPudOLRvaR8AAAvGTge61to3W2t/N1z+1yT3J9k3ycokVw6bXZnkLcPllUk+0aZ8KcmSqtonyQlJbmmtPdpa+06SW5KcONz2E621L7XWWpJPPOOxZtoHAMCCMdI5dFW1LMmRSb6c5GWttW8ON/1TkpcNl/dNsm7a3dYPbVtrXz9De7ayj2ce1+lVtbqqVn/729/e8Y4BAIyxkQW6qvp3Sa5NclZr7bvTbxsqa21U+5rJ1vbRWrustbaitbbiJS95yWweBgDAnBtJoKuq3TIV5j7ZWvvs0PzPw3Bpht/fGtofSbLftLsvHdq21r50hvat7QMAYMEYxSrXSvLxJPe31v5o2k2rkmxeqXpKkuumtb9jWO16TJLHh2HTm5IcX1UvHBZDHJ/kpuG271bVMcO+3vGMx5ppHwAAC8YovinitUn+S5J7q+ruoe19SS5Ick1VnZbkH5P85+G2G5O8IcnaJN9LcmqStNYerar/nuSOYbv/1lp7dLj8X5NckeR5Sf56+MlW9gEAsGDsdKBrrX0xSW3h5uNm2L4lOXMLj3V5kstnaF+d5JUztG+YaR8AAAuJ73IFgDnie2CZLb76CwCgcyp0ALCTZqq8wVxSoQMA6JxABwDQOUOuADCPLJRgFFToAAA6J9ABAHROoAMA6JxABwDQOYsiAGDMbF4osd+SxUmSdY/9wEIJtkqFDgCgcwIdAEDnBDoAgM6ZQwcAHXACYrZGhQ4AoHMCHQBA5wQ6AIDOCXQAAJ0T6AAAOifQAQB0zmlLAKBTTmXCZip0AACdE+gAADon0AEAdM4cOgCYIObVLUwqdAAAnRPoAAA6J9ABAHROoAMA6JxABwDQOYEOAKBzAh0AQOechw4AJpxz000+FToAgM4JdAAAnRPoAAA6Zw4dACxA5tVNFhU6AIDOCXQAAJ0T6AAAOifQAQB0TqADAOicQAcA0DmnLQEAksx8KpPE6Ux6oEIHANA5gQ4AoHMCHQBA5wQ6AIDOCXQAAJ2zyhUA2KqZVr9a+TpeVOgAADon0AEAdE6gAwDonEAHANA5gQ4AoHMCHQBA5wQ6AIDOCXQAAJ1zYmEAYIc52fB4UaEDAOicQAcA0DmBDgCgcwIdAEDnBDoAgM5Z5QoAjISVr/NHhQ4AoHMCHQBA5wQ6AIDOCXQAAJ0T6AAAOmeVKwAwa5adc0P2W7I4SbLusR8ksfJ1NqjQAQB0TqADAOicQAcA0DmBDgCgcwIdAEDnrHIFAOaU73wdPRU6AIDOCXQAAJ0T6AAAOmcOHQAw78yr2zkqdAAAnRPoAAA6J9ABAHROoAMA6JxFEQDAWLJQYvup0AEAdE6gAwDonEAHANA5c+gAgG6YVzczFToAgM4JdAAAnTPkCgB0zTCsQAcATKCFFvIMuQIAdE6FDgBYECa5ajcRFbqqOrGqvlZVa6vqnPk+HgCAudR9ha6qdk1ySZKfT7I+yR1Vtaq19tX5PTIAYNxtb9Vu2Tk3ZL8li5Mk6x77wRa3my/dB7okRyVZ21p7KEmq6uokK5MIdADADpsp5I27SQh0+yZZN+36+iRHT9+gqk5PcnqS7L///nNyUA/v8avZtHhZkmTRHg/PyT5ny6T0I5mcvujHeNGP8TMpfdGP8fLsfjw+X4fyLBMxh25bWmuXtdZWtNZWvOQlL5nvwwEAGKlJCHSPJNlv2vWlQxsAwIIwCYHujiQHVtXyqto9yclJVs3zMQEAzJnu59C11jZV1a8nuSnJrkkub63dN8+HBQD06rwZ5sadt9fcH8cO6D7QJUlr7cYkN873cQAAzIeJCHQAAM/JTNW4Dgl0AADbct7jyaZNU5cXjV98moRFEQAAC5pABwDQOYEOAKBzAh0AQOcEOgCAzgl0AACdE+gAADo3fidSAQCYDRNyEuGZqNABAHROoAMA6JxABwDQOXPoAIDJM8Hz5WaiQgcA0DmBDgCgcwIdAEDnBDoAgM4JdAAAnRPoAAA657QlAEDfFtgpSmaiQgcA0DmBDgCgcwIdAEDnBDoAgM4JdAAAnbPKFQDohxWtM1KhAwDonEAHANA5gQ4AoHMCHQBA5wQ6AIDOCXQAAJ1z2hIAYDw5Rcl2U6EDAOicQAcA0DmBDgCgcwIdAEDnBDoAgM4JdAAAnRPoAAA6J9ABAHTOiYUBgPnnJMI7RYUOAKBzAh0AQOcEOgCAzgl0AACdE+gAADon0AEAdE6gAwDonPPQAQBzyznnRk6FDgCgcwIdAEDnBDoAgM4JdAAAnRPoAAA6J9ABAHROoAMA6Jzz0AEAs+e8x5NNm6YuLxI7ZosKHQBA5wQ6AIDOCXQAAJ0T6AAAOifQAQB0TqADAOicQAcA0DmBDgCgcwIdAEDnnLIZABiN8x6f7yNYsFToAAA6J9ABAHROoAMA6JxABwDQOYEOAKBzAh0AQOectgQA2HFOUTJWVOgAADon0AEAdE6gAwDonEAHANA5gQ4AoHMCHQBA5wQ6AIDOCXQAAJ0T6AAAOifQAQB0TqADAOicQAcA0LlF830AAMCYO+/x+T4CtkGFDgCgcwIdAEDnBDoAgM4JdAAAnRPoAAA6Z5UrADDFatZuqdABAHRupwJdVf1/VfVAVd1TVf+rqpZMu+3cqlpbVV+rqhOmtZ84tK2tqnOmtS+vqi8P7Z+uqt2H9sXD9bXD7cu2tQ8AgIVkZyt0tyR5ZWvt8CR/n+TcJKmqQ5KcnOTQJCcm+WhV7VpVuya5JMnrkxyS5FeGbZPkw0k+0lr7ySTfSXLa0H5aku8M7R8ZttviPnayPwAA3dmpQNdau7m1tmm4+qUkS4fLK5Nc3Vr7QWvtH5KsTXLU8LO2tfZQa+2HSa5OsrKqKsmxST4z3P/KJG+Z9lhXDpc/k+S4Yfst7QMAYEEZ5Ry6dyb56+HyvknWTbtt/dC2pfa9kzw2LRxubn/aYw23Pz5sv6XHAgBYULa5yrWqbk3y8hluen9r7bphm/cn2ZTkk6M9vNGoqtOTnJ4k+++//zwfDQDAaG0z0LXWfm5rt1fV/5PkTUmOa621ofmRJPtN22zp0JYttG9IsqSqFg1VuOnbb36s9VW1KMlew/Zb28cz+3BZksuSZMWKFW2mbQAAerVT56GrqhOT/E6Sn2mtfW/aTauSfKqq/ijJK5IcmOT2JJXkwKpanqnwdXKSX22ttaq6LckvZ2pe3SlJrpv2WKck+T/D7X8zbL+lfQAA2+KccxNlZ08s/CdJFie5ZWqdQr7UWjujtXZfVV2T5KuZGoo9s7X24ySpql9PclOSXZNc3lq7b3is301ydVV9KMldST4+tH88yVVVtTbJo5kKgdnaPgCAaYS3ibdTgW44lciWbjs/yfkztN+Y5MYZ2h/KDKtUW2tPJHnrjuwDAGAh8U0RAACdE+gAADon0AEAdE6gAwDonEAHANA5gQ4AoHM7ex46AGCcOOfcgiTQAUCvhDcGAh0A7KyZgtV5e839cbBgmUMHANA5gQ4AoHOGXAGgB+bLsRUCHQCMm83hbdOmqd+LfFyzdV4hADAbLJRgDplDBwDQOYEOAKBzhlwBYD5Z7MAIqNABAHROhQ4A5opqHLNEhQ4AoHMCHQBA5wQ6AIDOmUMHADvCPDjGkAodAEDnBDoAgM4JdAAAnRPoAAA6J9ABAHROoAMA6JxABwDQOYEOAKBzAh0AQOcEOgCAzgl0AACd812uALAlvreVTqjQAQB0TqADAOicQAcA0Dlz6AAgMV+OrqnQAQB0TqADAOicQAcA0Dlz6ABYeMyXY8Ko0AEAdE6gAwDonEAHANA5gQ4AoHMCHQBA5wQ6AIDOCXQAAJ0T6AAAOifQAQB0TqADAOicQAcA0DmBDgCgcwIdAEDnBDoAgM4JdAAAnRPoAAA6J9ABAHROoAMA6JxABwDQOYEOAKBzAh0AQOcEOgCAzgl0AACdE+gAADon0AEAdE6gAwDonEAHANA5gQ4AoHMCHQBA5wQ6AIDOCXQAAJ0T6AAAOifQAQB0TqADAOjcovk+gIl13uPJpk1TlxcN/8zn7TV/xwMATCwVOgCAzgl0AACdM+QKz8V5j8/QZkgdgPkh0MG2zBTeAGCMCHRzSVUHAJgF5tABAHROoAMA6JxABwDQOYEOAKBzFkXMNwslxosVrQB0SIUOAKBzKnQwKqqtAMwTgY6FYXPY2rRp6veiRcIWABNDoGPhMl8OFgbvdRYAc+gAADqnQgezybw6AOaAQDeOhAAAYAcIdL0Q8gCALTCHDgCgcyMJdFX1W1XVqurFw/Wqqouqam1V3VNVr5q27SlV9eDwc8q09ldX1b3DfS6qqhraX1RVtwzb31JVL9zWPgAAFpKdHnKtqv2SHJ/kG9OaX5/kwOHn6CQfS3J0Vb0oye8nWZGkJbmzqla11r4zbPOuJF9OcmOSE5P8dZJzknyutXZBVZ0zXP/dLe1jZ/vDBHCKAgAWmFHMoftIkt9Jct20tpVJPtFaa0m+VFVLqmqfJK9Lcktr7dEkqapbkpxYVZ9P8hOttS8N7Z9I8pZMBbqVw/2S5Mokn89UoJtxH621b46gTzB7zIcEYMR2KtBV1cokj7TWvjKMkG62b5J1066vH9q21r5+hvYkedm0kPZPSV62jX0snEAnGKjGAUC2I9BV1a1JXj7DTe9P8r5MDbfOidZaq6q2o/erqtOTnJ4k+++//8iPCwBgPm0z0LXWfm6m9qo6LMnyJJurc0uT/F1VHZXkkST7Tdt86dD2SP5t+HRz++eH9qUzbJ8k/7x5KHUYtv3W0L6lfczUh8uSXJYkK1as2OFACLNOtRWAnfCch1xba/cmeenm61X1cJIVrbV/qapVSX69qq7O1EKFx4dAdlOSP9i8UjVT1b1zW2uPVtV3q+qYTC2KeEeSi4dtViU5JckFw+/rprU/ax/PtT8ATIDNfxxt2jT1e5HTrbIwzNYr/cYkb0iyNsn3kpyaJENw++9J7hi2+2+bF0gk+a9JrkjyvEwthvjrof2CJNdU1WlJ/jHJf97aPhY8lR56taX5kF6/ANs0skDXWls27XJLcuYWtrs8yeUztK9O8soZ2jckOW6G9i3ugzG2vYsYfIgL5wBsN7VoZo8VqKM303CSkEfP/OECIyHQLVSj/k9UeAPmmv934CkCHVt33uPPnlzsr+fJMRfVka196Jq4DjAS/hddCLZ73toY/bVrGIbNvBb65HmDOSXQwUIxToEddpTXL2yVQAe980EHsOAJdOy4+QoQggv0zXsYZo1AB4yOD2yAeSHQAbD95iK0+8MAdtgu830AAADsHIEOAKBzhlwBmJmhT+iGQAfAzN8KA3TDuxZgoVF5g4kj0AH98bVSAE8j0AFPJywBdEegA7ZNyAMYa05bAgDQORU6gElmAQQsCAId8NwICnPHvzWwDYZcAQA6p0IHsDNmOiGvBSPAHFOhAwDonAodwEzMWwM6okIHANA5gQ4AoHOGXAFGPby6vY9n8QQwIgIdMBkm5evJzN0DngNDrgAAnVOhA5gvqnHAiAh0wMIiRAETyJArAEDnVOiAyaUaBywQKnQAAJ0T6AAAOifQAQB0TqADAOicQAcA0DmBDgCgcwIdAEDnBDoAgM4JdAAAnRPoAAA6J9ABAHROoAMA6JxABwDQOYEOAKBzAh0AQOcEOgCAzgl0AACdE+gAADon0AEAdE6gAwDonEAHANA5gQ4AoHMCHQBA5wQ6AIDOVWttvo9hTlXVt5P84xzC97BMAAAGMUlEQVTu8sVJ/mUO9zdbJqUfyeT0RT/Gi36Mn0npi36Ml7nux//VWnvJtjZacIFurlXV6tbaivk+jp01Kf1IJqcv+jFe9GP8TEpf9GO8jGs/DLkCAHROoAMA6JxAN/sum+8DGJFJ6UcyOX3Rj/GiH+NnUvqiH+NlLPthDh0AQOdU6AAAOifQzaKqOrGqvlZVa6vqnPk+nu1VVZdX1beqas20thdV1S1V9eDw+4XzeYzbo6r2q6rbquqrVXVfVb1naO+qL1W1R1XdXlVfGfrxwaF9eVV9eXh9fbqqdp/vY90eVbVrVd1VVdcP13vtx8NVdW9V3V1Vq4e2rl5bSVJVS6rqM1X1QFXdX1Wv6a0fVXXQ8Dxs/vluVZ3VWz+SpKrOHt7na6rqL4f3f3fvkap6z9CH+6rqrKGti+djRz4Da8pFw3NzT1W9ar6OW6CbJVW1a5JLkrw+ySFJfqWqDpnfo9puVyQ58Rlt5yT5XGvtwCSfG66Pu01Jfqu1dkiSY5KcOTwHvfXlB0mOba39hyRHJDmxqo5J8uEkH2mt/WSS7yQ5bR6PcUe8J8n906732o8k+dnW2hHTTmHQ22srSf44yf9urf37JP8hU89NV/1orX1teB6OSPLqJN9L8r/SWT+qat8k706yorX2yiS7Jjk5nb1HquqVSd6V5KhMvabeVFU/mX6ejyuy/Z+Br09y4PBzepKPzdExPltrzc8s/CR5TZKbpl0/N8m5831cO3D8y5KsmXb9a0n2GS7vk+Rr832Mz6FP1yX5+Z77kuT5Sf4uydGZOrHloqH9aa+3cf1JsjRT/xkem+T6JNVjP4ZjfTjJi5/R1tVrK8leSf4hw3zqXvvxjGM/Psnf9tiPJPsmWZfkRUkWDe+RE3p7jyR5a5KPT7v+gSS/09Pzsb2fgUn+NMmvzLTdXP+o0M2ezW/MzdYPbb16WWvtm8Plf0rysvk8mB1VVcuSHJnky+mwL8Mw5d1JvpXkliRfT/JYa23TsEkvr6//kan/2J8cru+dPvuRJC3JzVV1Z1WdPrT19tpanuTbSf58GAb/s6p6Qfrrx3QnJ/nL4XJX/WitPZLkwiTfSPLNJI8nuTP9vUfWJPm/q2rvqnp+kjck2S+dPR/PsKVjH5vPeoGOHdam/gzpZnl0Vf27JNcmOau19t3pt/XSl9baj9vUcNLSTA1j/Pt5PqQdVlVvSvKt1tqd830sI/IfW2uvytSQy5lV9Z+m39jJa2tRklcl+Vhr7cgkG/OMYbBO+pEkGeaWnZTkfz7zth76MczLWpmpoP2KJC/Is4f+xl5r7f5MDRPfnOR/J7k7yY+fsc3YPx9bMq7HLtDNnkcy9RfJZkuHtl79c1XtkyTD72/N8/Fsl6raLVNh7pOttc8OzV32JUlaa48luS1Twy5LqmrRcFMPr6/XJjmpqh5OcnWmhl3/OP31I8lT1ZS01r6VqflaR6W/19b6JOtba18ern8mUwGvt35s9vokf9da++fhem/9+Lkk/9Ba+3Zr7UdJPpup901375HW2sdba69urf2nTM37+/v093xMt6VjH5vPeoFu9tyR5MBhddLumRoGWDXPx7QzViU5Zbh8Sqbmo421qqokH09yf2vtj6bd1FVfquolVbVkuPy8TM0DvD9Twe6Xh83Gvh+ttXNba0tba8sy9X74m9ba29NZP5Kkql5QVXtuvpypeVtr0tlrq7X2T0nWVdVBQ9NxSb6azvoxza/k34Zbk/768Y0kx1TV84f/vzY/Hz2+R146/N4/yS8m+VT6ez6m29Kxr0ryjmG16zFJHp82NDu35nvi4ST/ZGrewN9nar7T++f7eHbguP8yU/M3fpSpv+BPy9Rcp88leTDJrUleNN/HuR39+I+ZKovfk6mS/93Dc9JVX5IcnuSuoR9rkvy/Q/sBSW5PsjZTQ0yL5/tYd6BPr0tyfa/9GI75K8PPfZvf3729toZjPiLJ6uH19VdJXthpP16QZEOSvaa19diPDyZ5YHivX5VkcafvkS9kKox+JclxPT0fO/IZmKmFXZcMn/P3ZmqF8rwct2+KAADonCFXAIDOCXQAAJ0T6AAAOifQAQB0TqADAOicQAcA0DmBDgCgcwIdAEDn/n96ckL8SBapyQAAAABJRU5ErkJggg==)
%% Cell type:markdown id: tags:
Exo: changez les abscisses pour faire apparaitre les années de naissances. Essayez de justifier les trous et les bonnes dans la pyramide d'âge.
Exo: Comment expliquez-vous le grand nombre de centenaire comparez aux personnes de 98 ou 99 ans ?
%% Cell type:markdown id: tags:
## Histogrammes
Considérons maintenant un échantillon c.à.d un ensemble de nombre réels. Dans la plupart des cas les échantillons sont construit:
* soit à partir de simulation successive de v.a (ex: v.a gaussienne)
* soit à partir d'observations (ex: tailles des gens dans la rue)
Dresser l'histogramme d'un échantillon consiste à découper les réel en sous-interavalles, puis d'afficher des batons qui ont pour base ces sous-intervallse, et comme hauteur le nombre d'élément de l'échantillon contenu dans chaque sous-intervalles.
%% Cell type:code id: tags:
``` python
"""l'échantillon à observer"""
X=np.random.normal(0,1,size=1000)
plt.hist(X,bins=15,color='blue',density=True,edgecolor="k");
```
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:markdown id: tags:
Explication des options de plt.hist :
* bins=10 : on découpe l'intervalle [min(X),max(X)] en dix sous-intervalles.
* normed=True: la hauteur des batons est normalisée pour que cela ressemble à une densité
* rwidth=0.9: la largeur de chaque baton occupe 90% de chaque sous-intervalle.
%% Cell type:markdown id: tags:
Mais parfois il est préférable de préciser nous même les sous-intervalles (=la base des batons)
%% Cell type:code id: tags:
``` python
X=np.random.normal(0,1,size=1000)
plt.hist(X, bins=[-2,0.5,1,5], density=True,edgecolor="k"); #un choix particulièrement idiot de bins
```
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:markdown id: tags:
## Histogramme de loi discrète
%% Cell type:markdown id: tags:
Attention, pour les lois discrètes il faut obligatoirement préciser le découpage.
Pour voir une catastrophe, remplacez bins par 11 dans plt.hist(). Expliquez le phénomène.
%% Cell type:code id: tags:
``` python
n=10
X=np.random.binomial(n,0.5,size=3000)
"""attention np.arange(0,n+2,1) donne l'intervalle discret [0,n+2[= [0,n+1].
on lui soustrait ensuite 0.5 pour avoir chaque entier de [0,n] dans un sous-intervalle"""
bins=np.arange(0,n+2,1)-0.5
""" rwidth=0.6 (=ratio_width) signifie que la base des batons occupe 60% des sous-intervalles. """
plt.hist(X,bins=bins, histtype='bar', color='blue', rwidth=0.6)
"""on précise les graduations en x"""
plt.xticks(np.arange(0,n+1,1));
```
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:markdown id: tags:
### Plusieurs histogrammes
comparons des lois béta
%% Cell type:code id: tags:
``` python
nbData=10000
X1=np.random.beta(3,1,size=nbData)
X2=np.random.beta(2,3,size=nbData)
X3=np.random.beta(1,0.5,size=nbData)
plt.hist([X1,X2,X3],bins=20,label=["a=3,b=1","a=2,b=3","a=1,b=0.5"]);
plt.legend();
```
%%%% Output: display_data
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFxBJREFUeJzt3XuQlNWdxvHvDyEggoqAFsUAg4agGFw0A2JpuVBYimCJlBNFYyBGxQtseUmM7G5ZWDFusbUEIyXqEp0ARgLuqsuoeAMvU24JMihyUdmZVZSZJXIR0YSAoL/9o1+wGWbonr7O2+f5VE3N26dPnz5nBvrpc963z5i7IyIi4WlX7A6IiEhxKABERAKlABARCZQCQEQkUAoAEZFAKQBERAKlABARCZQCQEQkUAoAEZFAtS92B46kR48eXl5eXuxuiIjEyurVq7e7e89U9dp0AJSXl1NbW1vsboiIxIqZfZJOPS0BiYgESgEgIhIoBYCISKDa9DmA5uzbt4+Ghgb27NlT7K6UtE6dOlFWVkaHDh2K3RURyZPYBUBDQwNdu3alvLwcMyt2d0qSu7Njxw4aGhro379/sbsjInkSuyWgPXv20L17d73455GZ0b17d82yREpc7AIA0It/AehnLFL6YhkAIiKSvdidA2iqfNrzOW1v04yxOW0v2SOPPMKcOXM46qij6NKlC3PnzmXQoEGH1Hn99deZOXMmzz33XKvbr6mp4bbbbmPt2rUsWrSIysrKXHVdREpQ7AMgTq6++mpuuukmAKqrq7njjjt48cUXc9Z+3759mTdvHjNnzsxZmyJSGIPnDz7k9rpJ6/L+nFoCytBll13Gj370I04//XTmzp2b1mOOPfbYg8d//etfW1xn//LLLxk7diwDBw7kpptu4ttvv02r/fLycs444wzatdOvVURS0wwgQ1VVVZxwwgn87W9/Y+jQoVx++eXccsstbNy48bC6d9xxBxMnTgRgzpw5zJo1i6+//ppXX3212bbffvtt3n//ffr168fo0aN5+umnqays5Morr0zZvohIuhQAGZo9ezbPPPMMAJs3b6auro7FixenfNyUKVOYMmUKCxcu5De/+Q3z588/rM6wYcM4+eSTAbjqqqt48803qaysTKt9EZF0KQAy8Prrr7Ns2TLeeustOnfuzIgRI9izZ0+r3qFPmDCBm2++udn2my4NHbitGYCI5JICIAO7du2iW7dudO7cmQ8//JAVK1YApHyHXldXx4ABAwB4/vnnDx43NjYyceJEli9fDiSWgD7++GP69evH4sWLmTx5clrti4i0RuwDIJ+XbbZk9OjRPPLII5x22mkMHDiQ4cOHp/W4Bx98kGXLltGhQwe6det2cPlny5YttG//3a9i6NChTJ06lfr6ekaOHMn48ePTan/VqlWMHz+enTt38uyzzzJ9+nQ2bNjQ+gGKSBBiHwDF0LFjR1544YVWP+6BBx5otnzFihVMmTIFgBEjRlBTU5NRv4YOHUpDQ0NGjxWR8CgA2oCpU6cWuwsiEiBdMC4iEigFgIhIoBQAIiKBUgCIiARKASAiEqj4XwV0z3E5bm9XbttLMmvWLB599FHat29Pz549qaqqol+/fofU2bRpE5dccgnr169vdftLlizh7rvvpl27drRv357f/e53nHfeebnqvoiUmJQzADPrY2avmdn7ZrbBzG6Nyk8ws1fMrC763i0qNzObbWb1ZrbWzM5KamtSVL/OzCblb1ht05lnnkltbS1r166lsrKSX/3qVzltf9SoUbz33nusWbOGqqoqrr/++py2LyKlJZ0loP3AL9x9EDAcmGJmg4BpwHJ3HwAsj24DXAwMiL4mAw9DIjCA6cDZwDBg+oHQiKNMtoMeOXIknTt3BmD48OEtfmhr//79/OQnP+G0006jsrKS3bt3p9V+ly5dDu4bdKTtpkVEII0AcPct7v5OdPwV8AHQGxgHHNjKcj5wWXQ8DljgCSuA482sF3AR8Iq7f+7uO4FXgNE5HU0BVVVVsXr1ampra5k9ezY7duzgyiuvZMiQIYd9LViw4LDHP/bYY1x88cXNtr1x40ZuueUWPvjgA4499lgeeughAG6//fZm258xY8bBxz7zzDOceuqpjB07lqqqqvwMXkRKQqvOAZhZOXAmsBI4yd23RHf9GTgpOu4NbE56WENU1lJ5LGW6HTTAH//4R2pra3njjTeavb9Pnz6ce+65AFxzzTXMnj2bX/7yl9x///0p2x4/fjzjx4+npqaGu+++m2XLlqU5IhEJTdoBYGZdgKeA29z9y+TlBXd3M/NcdMjMJpNYOqJv3765aDLnstkOetmyZdx333288cYbdOzYsdn2W9oO+vbbb+e11147rP6ECROYNm3aIWXnn38+H330Edu3b6dHjx4ZjVNESltaAWBmHUi8+D/h7k9HxZ+ZWS933xIt8WyNyhuBPkkPL4vKGoERTcpfb/pc7j4XmAtQUVGRk1DJtUy3g3733Xe58cYbefHFFznxxBMPue/UU0/lww8/BODTTz/lrbfe4pxzzmHhwoUHr+RJNQOor6/nlFNOwcx455132Lt3L927d890mCJS4lIGgCXefj4GfODus5LuqgYmATOi70uSyqea2SISJ3x3RSHxEvAvSSd+LwT+MesR5PGyzZZkuh30nXfeyV/+8hd+/OMfA4kZTnV1Ndu3b8f9u6wbOHAgc+bM4ec//zmDBg1q8Q/HNPXUU0+xYMECOnTowNFHH83ixYt1IlhEWpTODOBc4KfAOjNbE5X9E4kX/ifN7DrgE+CK6L6lwBigHtgNXAvg7p+b2b3Aqqjer93985yMosAy3Q66pfX45O2gy8vLD84EWuuuu+7irrvuyuixIhKelAHg7m8CLb2NHNVMfQemtNBWFaBLU5q45JJLit0FEQmQtoIQEQmUAkBEJFAKABGRQCkAREQCpQAQEQlU7LeDHjx/cE7bWzdpXU7bS1ZTU8Ntt93G2rVrWbRoEZWVlYfVyWY7aHfn1ltvZenSpXTu3Jl58+Zx1llnHVZvxIgRbNmyhaOPPhqAl19++bAPpolI6Yt9AMRJ3759mTdvHjNnzsxL+y+88AJ1dXXU1dWxcuVKbr75ZlauXNls3SeeeIKKioq89ENE4kFLQBnKZDvo8vJyzjjjDNq1O/KPPdPtoJcsWcLEiRMxM4YPH84XX3zBli1bUj9QRIKkAMhQtttBH0mm20E3NjbSp8932zCVlZXR2NjY7HNce+21DBkyhHvvvfeQbShEJBxaAspQNttBp5LNdtDpeOKJJ+jduzdfffUVl19+OY8//vjB3UpFJBwKgAxksx10OjLdDrp3795s3vzdn1xoaGigd+/D/+TCgbKuXbty9dVX8/bbbysARAKkAMhApttBH0kutoO+9NJLefDBB5kwYQIrV67kuOOOo1evXofU2b9/P1988QU9evRg3759PPfcc1xwwQUZ91tE4iv2AZDPyzZbkul20KtWrWL8+PHs3LmTZ599lunTp7Nhw4acbQc9ZswYli5dyve//306d+7MH/7wh4P3DRkyhDVr1rB3714uuugi9u3bxzfffMMFF1zADTfc0LofgIiUhNgHQDFkuh300KFDm/1D8LnaDtrMmDNnTrP3rVmT2Mn7mGOOYfXq1Rm1LyKlRQHQBmg7aBEpBl0GKiISqFgGgK5bzz/9jEVKX+wCoFOnTuzYsUMvUHnk7uzYsYNOnToVuysikkexOwdQVlZGQ0MD27ZtK3ZXSlqnTp0oKysrdjdEJI9iFwAdOnSgf//+xe6GiEjsxW4JSEREckMBICISKAWAiEigFAAiIoFSAIiIBEoBICISKAWAiEigFAAiIoFSAIiIBEoBICISKAWAiEigFAAiIoFSAIiIBEoBICISKAWAiEigFAAiIoFSAIiIBEoBICISqJQBYGZVZrbVzNYnld1jZo1mtib6GpN03z+aWb2ZbTSzi5LKR0dl9WY2LfdDERGR1khnBjAPGN1M+f3uPiT6WgpgZoOACcDp0WMeMrOjzOwoYA5wMTAIuCqqKyIiRZLyj8K7e42ZlafZ3jhgkbvvBT42s3pgWHRfvbt/BGBmi6K677e6xyIikhPZnAOYamZroyWiblFZb2BzUp2GqKylchERKZJMA+Bh4BRgCLAF+G2uOmRmk82s1sxqt23blqtmRUSkiYwCwN0/c/dv3P1b4Pd8t8zTCPRJqloWlbVU3lzbc929wt0revbsmUn3REQkDRkFgJn1Sro5HjhwhVA1MMHMOppZf2AA8DawChhgZv3N7HskThRXZ95tERHJVsqTwGb2J2AE0MPMGoDpwAgzGwI4sAm4EcDdN5jZkyRO7u4Hprj7N1E7U4GXgKOAKnffkPPRiIhI2tK5CuiqZoofO0L9+4D7milfCixtVe9ERCRv9ElgEZFApZwBiIhI9sqnPX/I7U0zxhapJ9/RDEBEJFAKABGRQCkAREQCpQAQEQmUAkBEJFAKABGRQCkAREQCpQAQEQmUAkBEJFAKABGRQCkAREQCpb2ARETS0Bb38smWZgAiIoFSAIiIBEoBICISKAWAiEigFAAiIoFSAIiIBEoBICISKAWAiEigFAAiIoFSAIiIBEoBICISKAWAiEigFAAiIoFSAIiIBErbQYtIMEpxS+dsaAYgIhIoBYCISKAUACIigVIAiIgESgEgIhIoBYCISKAUACIigVIAiIgESh8EE5FY0Ye5ciflDMDMqsxsq5mtTyo7wcxeMbO66Hu3qNzMbLaZ1ZvZWjM7K+kxk6L6dWY2KT/DERGRdKWzBDQPGN2kbBqw3N0HAMuj2wAXAwOir8nAw5AIDGA6cDYwDJh+IDRERKQ4UgaAu9cAnzcpHgfMj47nA5cllS/whBXA8WbWC7gIeMXdP3f3ncArHB4qIiJSQJmeBD7J3bdEx38GToqOewObk+o1RGUtlYuISJFkfRWQuzvgOegLAGY22cxqzax227ZtuWpWRESayDQAPouWdoi+b43KG4E+SfXKorKWyg/j7nPdvcLdK3r27Jlh90REJJVMA6AaOHAlzyRgSVL5xOhqoOHArmip6CXgQjPrFp38vTAqExGRIkn5OQAz+xMwAuhhZg0kruaZATxpZtcBnwBXRNWXAmOAemA3cC2Au39uZvcCq6J6v3b3pieWRUSkgFIGgLtf1cJdo5qp68CUFtqpAqpa1TsRkRgZPH/wIbfXTVpXpJ6kR1tBiIgESgEgIhIoBYCISKAUACIigVIAiIgESgEgIhIo/T0AyZ97jmtye1dx+iEizdIMQEQkUJoByJHpXbxIyVIASNuk4ClZ+pOObYcCoNSF+kIa6rhFWkHnAEREAqUAEBEJlAJARCRQCgARkUDpJLBIUzqBLIHQDEBEJFAKABGRQGkJSERaTR/mKg0KAJFcanr+AHQOQdosLQGJiARKASAiEigtAYkESuv4ogCIA12XLiJ5oCUgEZFAaQYgIhIZPH/wIbfXTVpXpJ4UhmYAIiKBUgCIiARKASAiEigFgIhIoBQAIiKBUgCIiARKASAiEih9DqBQ9GleSYf+nUgBKQBEpKSE9mGubCgARGLqsM3cOl19aAXNHiQFnQMQEQmUAkBEJFBZLQGZ2SbgK+AbYL+7V5jZCcBioBzYBFzh7jvNzIAHgDHAbuBn7v5ONs8vEneHL+MUqSNtSNM1fNA6fr7kYgYw0t2HuHtFdHsasNzdBwDLo9sAFwMDoq/JwMM5eG4REclQPpaAxgHzo+P5wGVJ5Qs8YQVwvJn1ysPzi4hIGrINAAdeNrPVZjY5KjvJ3bdEx38GToqOewObkx7bEJWJiEgRZHsZ6Hnu3mhmJwKvmNmHyXe6u5uZt6bBKEgmA/Tt2zfL7olIMeha/HjIagbg7o3R963AM8Aw4LMDSzvR961R9UagT9LDy6Kypm3OdfcKd6/o2bNnNt0TEZEjyDgAzOwYM+t64Bi4EFgPVAOTomqTgCXRcTUw0RKGA7uSlopERKTAslkCOgl4JnF1J+2Bhe7+opmtAp40s+uAT4ArovpLSVwCWk/iMtBrs3huERHJUsYB4O4fAX/XTPkOYFQz5Q5MyfT5REQkt7QXkIgkaCfS4GgrCBGRQGkGIJKFUt7KQZdylj7NAEREAqUZgEiJ0jt4SUUBINKG6UVc8kkBkC5dISEiJUbnAEREAqUZgEieaRlH2irNAEREAqUZgARB78JFDqcAkOCV8oe5RI5EASAFE9d34XHtt0gqCgBplWK9GDZ93kI+t0ipUgAERu9mJS+afk6mv/6caxwEHQBxfTGMa79FpG0JOgCkdOhErkjrKQCKRO/iRaTY9EEwEZFAaQaQIb2DF5G4UwBIm6A1/IDpCqKi0RKQiEigFAAiIoFSAIiIBEoBICISKAWAiEigFAAiIoFSAIiIBEqfA5Cc0bX8UhT6HEHGwgoA/UMRETlIS0AiIoEKawYgKWkZRyQcmgGIiARKASAiEigFgIhIoHQOoMRoDV+kFQK/MlAzABGRQCkAREQCVfAlIDMbDTwAHAU86u4zCt2Htk7LOCIx0HT5CGK3hFTQGYCZHQXMAS4GBgFXmdmgQvZBREQSCj0DGAbUu/tHAGa2CBgHvF/gfuRV03fwoHfxItL2FDoAegObk243AGcXuA9p0TKMiORVG7gCydy9cE9mVgmMdvfro9s/Bc5296lJdSYDk6ObA4GNGTxVD2B7lt2NoxDHHeKYIcxxhzhmyGzc/dy9Z6pKhZ4BNAJ9km6XRWUHuftcYG42T2Jmte5ekU0bcRTiuEMcM4Q57hDHDPkdd6EvA10FDDCz/mb2PWACUF3gPoiICAWeAbj7fjObCrxE4jLQKnffUMg+iIhIQsE/B+DuS4GleX6arJaQYizEcYc4Zghz3CGOGfI47oKeBBYRkbZDW0GIiAQq1gFgZqPNbKOZ1ZvZtGbu72hmi6P7V5pZeeF7mVtpjPkOM3vfzNaa2XIz61eMfuZaqnEn1bvczNzMYn+1SDpjNrMrot/3BjNbWOg+5kMa/8b7mtlrZvZu9O98TDH6mUtmVmVmW81sfQv3m5nNjn4ma83srJw8sbvH8ovESeT/BU4Gvge8BwxqUucW4JHoeAKwuNj9LsCYRwKdo+Ob4z7mdMcd1esK1AArgIpi97sAv+sBwLtAt+j2icXud4HGPRe4OToeBGwqdr9zMO7zgbOA9S3cPwZ4ATBgOLAyF88b5xnAwW0l3P1r4MC2EsnGAfOj4/8ERpmZFbCPuZZyzO7+mrvvjm6uIPFZi7hL53cNcC/wr8CeQnYuT9IZ8w3AHHffCeDuWwvcx3xIZ9wOHBsdHwf8XwH7lxfuXgN8foQq44AFnrACON7MemX7vHEOgOa2lejdUh133w/sAroXpHf5kc6Yk11H4l1D3KUcdzQl7uPuh2/EFE/p/K5/APzAzP7bzFZEO+3GXTrjvge4xswaSFxR+A+F6VpRtfb/flr0F8FKlJldA1QAf1/svuSbmbUDZgE/K3JXCq09iWWgESRmejVmNtjdvyhqr/LvKmCeu//WzM4BHjezH7r7t8XuWNzEeQaQcluJ5Dpm1p7EdHFHQXqXH+mMGTO7APhn4FJ331ugvuVTqnF3BX4IvG5mm0iskVbH/ERwOr/rBqDa3fe5+8fA/5AIhDhLZ9zXAU8CuPtbQCcS++WUsrT+77dWnAMgnW0lqoFJ0XEl8KpHZ1RiKuWYzexM4N9JvPiXwpowpBi3u+9y9x7uXu7u5STOfVzq7rXF6W5OpPPv+79IvPvHzHqQWBL6qJCdzIN0xv0pMArAzE4jEQDbCtrLwqsGJkZXAw0Hdrn7lmwbje0SkLewrYSZ/Rqodfdq4DES08N6EidYJhSvx9lLc8z/BnQB/iM63/2pu19atE7nQJrjLilpjvkl4EIzex/4BrjT3eM8w0133L8Afm9mt5M4IfyzmL+xw8z+RCLMe0TnNqYDHQDc/RES5zrGAPXAbuDanDxvzH9uIiKSoTgvAYmISBYUACIigVIAiIgESgEgIhIoBYCISKAUACIigVIAiIgESgEgIhKo/wegVWR2L4dDKgAAAABJRU5ErkJggg==)
%% Cell type:markdown id: tags:
La variété des formes possible d'une loi la rend très pratique en modélisation.
Choisissez des lois bêta bien choisies (dilatée par une constante), pour modéliser les variables X suivantes:
* X : quantité chocolat consommée par les français (sachant que plus on en mange, et plus on a envie d'en manger)
* X : durée de vie des français
* X : durée de vie des grenouilles (forte mortalité infantile)
Dressez les histogrammes
Connaissez-vous d'autre loi pour des durées de vie ?
%% Cell type:markdown id: tags:
## Superposons histogramme et densité