如何在python中为我的数据正确获取累积分布函数? [英] How to get cumulative distribution function correctly for my data in python?

问题描述

大家好，我有一个值列表，我需要获取累积分布函数我已将此列表保存在变量名称 yvalues 中

Hello everyone i have a list of values for which i need to get cumulative distribution function i have saved this list in a variable name yvalues

[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 17.0, 17.0, 1.0, 1.0, 1.0.0，22.0，23.0，24.0，25.0，26.0，27.0，28.0，29.0，30.0，31.0，32.0，33.0，34.0，35.0，36.0，37.0，38.0，39.0，40.0，41.0，42.0，43.0，44.0，45.0，46.0，47.0，48.0，49.0，50.0，51.0，52.0，53.0，54.0，55.0，56.0，57.0，58.0，59.0，60.0，61.0，62.0，63.0，64.0，65.0，66.0，67.0，68.0，69.0，70.0，71.0，72.0，73.0，74.0，75.0，76.0，77.0，78.0，79.0，80.0，81.0，82.0，83.0，84.0，85.0，86.0，87.0，88.0，89.0，90.0，91.0，92.0，93.0，94.0，95.0，96.0，97.0，98.0，99.0，100.0，101.0，102.0，103.0，104.0，105.0，106.0，107.0，108.0，109.0，110.0，111.0，112.0，113.0，114.0，115.0，116.0，117.0，118.0，119.0，120.0，121.0，122.0，123.0，124.0，125.0，126.0，127.0，128.0，129.0，130.0，131.0，132.0，133.0，134.0，135.0，136.0，137.0，138.0，139.0，140.0，141.0，142.0，143.0，144.0，145.0，146.0, 147.0, 148.0, 149.0, 150.0, 151.0, 152.0, 153.0, 154.0, 155.0, 156.0, 157.0，158.0，159.0，160.0，161.0，162.0，163.0，164.0，165.0，166.0，167.0，168.0，169.0，170.0，171.0，172.0，173.0，174.0，175.0，176.0，177.0，178.0，179.0，180.0，181.0，182.0，183.0，184.0，185.0，186.0，187.0，188.0，189.0，190.0，191.0，192.0，193.0，194.0，195.0，196.0，197.0，198.0，199.0，200.0，201.0，202.0，203.0，204.0，205.0，206.0，207.0，208.0，209.0，210.0，211.0，212.0，213.0，214.0，215.0，216.0，217.0，218.0，219.0，220.0，221.0，222.0，223.0，224.0，225.0，226.0，227.0，228.0，229.0，230.0，231.0，232.0，233.0，234.0，235.0，236.0，237.0，238.0，239.0，240.0，241.0，242.0，243.0，244.0，245.0，246.0，247.0，248.0，249.0，250.0，251.0，252.0，253.0，254.0，255.0，256.0，257.0，259.0，260.0，261.0，262.0，263.0，264.0，265.0，266.0，267.0，268.0，269.0，270.0，271.0，272.0，273.0，274.0，275.0，276.0，277.0，278.0，279.0，280.0，281.0，282.0，283.0, 284.0, 285.0, 286.0, 287.0, 288.0, 289.0, 290.0, 291.0, 292.0, 293.0, 294.0, 295.0, ., 293.0, ., 093.0, 293.0, 293.0302.0，303.0，304.0，305.0，306.0，307.0，308.0，309.0，310.0，311.0，313.0，315.0，316.0，317.0，318.0，319.0，320.0，321.0，322.0，323.0，324.0，325.0，326.0，327.0，328.0，329.0，331.0，332.0，333.0，334.0，335.0，336.0，337.0，338.0，339.0，340.0，341.0，342.0，343.0，344.0，345.0，346.0，347.0，349.0，350.0，352.0，353.0，354.0，355.0，356.0，357.0，358.0，359.0，360.0，362.0，363.0，364.0，365.0，367.0，368.0，370.0，371.0，372.0，375.0，376.0，377.0，378.0，379.0，380.0，381.0，383.0，384.0，386.0，389.0，390.0，391.0，392.0，393.0，395.0，396.0，397.0，398.0，399.0，400.0，402.0，403.0，404.0，405.0，411.0，412.0，413.0，414.0，415.0，416.0，417.0，419.0，420.0，424.0，425.0，426.0，427.0，428.0，429.0，430.0，431.0，432.0，433.0，434.0，435.0，436.0，438.0，439.0，440.0，442.0，443.0，445.0，446.0，447.0，448.0，452.0，454.0，456.0，458.0，460.0，461.0，462.0, 463.0, 464.0, 467.0, 468.0, 469.0, 470.0, 475.0, 477.0, 479.0, 480.0, 481.0, 482.0, ., 04, 84.0, 484.0, ., 04, 87.0492.0，493.0，495.0，500.0，502.0，505.0，508.0，509.0，511.0，514.0，515.0，516.0，517.0，518.0，519.0，520.0，524.0，526.0，527.0，528.0，530.0，531.0，532.0，533.0，534.0，535.0，536.0，537.0，540.0，541.0，545.0，546.0，547.0，548.0，551.0，552.0，553.0，555.0，558.0，562.0，563.0，565.0，566.0，567.0，569.0，570.0，572.0，573.0，574.0，575.0，577.0，579.0，583.0，585.0，587.0，588.0，591.0，593.0，594.0，597.0，599.0，601.0，602.0，607.0，610.0，613.0，614.0，622.0，624.0，627.0，629.0，630.0，631.0，632.0，633.0，636.0，637.0，638.0，640.0，645.0，649.0，654.0，655.0，656.0，658.0，662.0，668.0，676.0，677.0，679.0，682.0，685.0，689.0，691.0，696.0，697.0，699.0，700.0，702.0，703.0，706.0，707.0，721.0，722.0，725.0，727.0，731.0，733.0，735.0，740.0，744.0，747.0，751.0，754.0，760.0，770.0，778.0，779.0，781.0，782.0，791.0，798.0，805.0，807.0，825.0，835.0, 840.0, 846.0, 851.0, 877.0, 882.0, 887.0, 893.0, 900.0, 919.0, 926.0, 929.0, 944.0, ., 077.0, 959.0, 959.19.040，1017.0，1042.0，1043.0，1048.0，1055.0，1062.0，1077.0，1089.0，1111.0，1128.0，1162.0，1203.0，1204.0，1243.0，1300.0，1318.0，1325.0，1339.0，1362.0，1425.0，1483.0，1512.0，1657.0，1671.0，1709.0，1751.0，1812.0，1889.0，1955.0，2138.0，2147.0，2171.0，2205.0，2278.0，2558.0，2574.0，2781.0，2783.0，2790.0，2815.0，3019.0，3034.0，3278.0，3292.0，3415.0，3452.0，3579.0，3760.0，3857.0，3944.0，4111.0，4698.0，4994.0，5191.0，5586.0，5647.0，5874.0，6072.0，6440.0，6491.0，6772.0，7973.0，8341.0，13170.0，74473.0，76745.0，78061.0，78955.0，79225.0，79500.0，80509.0，80968.0，81203.0，81462.0，81506.0，81761.0，81989.0，82215.0，82426.0，83003.0，83011.0，83108.0，83129.0，83425.0，83457.0，83553.0，83609.0，83705.0，83844.0，83973.0，83996.0，84075.0，84283.0，84336.0，84524.0，84676.0，84787.0，84830.0，84943.0，84944.0，84960.0，85071.0，85088.0，85170.0，85194.0，85235.0，85353.0，85400.0，85557.0，85589.0，85599.0，85600.0，85716.0，85820.0，85824.0，85830.0，85846.0，85934.0，86022.0，86067.0，86177.0，86186.0，86195.0，86228.0，86279.0，86282.0，86289.0，86327.0，86336.0，86340.0，86359.0，86366.0，86370.0，86371.0，86376.0，86377.0，86385.0，86390.0，86391.0，86396.0，86397.0，86398.0，86399.0, 471967.0, 545161.0, 583973.0]

我已经试过了

a = yvalues
num_bins = len(a)
counts, bin_edges = np.histogram(a, bins=num_bins, normed=True)
cdf = np.cumsum(counts)

我的 cdf 输出是以下不正确的列表，因为 cdf 输出的最后一个值应该是 1.请帮助我不知道我做错了什么.提前致谢.

My cdf output is the following list which is not correct because cdf output last value should be 1. Please help i don't know what i am doing wrong. Thanks in advance.

<代码>>>>发展基金数组([ 0.0009658, 0.00104114, 0.00106169, 0.00107539, 0.00108909,0.00109252, 0.00109594, 0.00110279, 0.00110793, 0.00110964,0.00111135, 0.00111135, 0.00111135, 0.00111135, 0.00111135,0.00111135, 0.00111307, 0.00111307, 0.00111307, 0.00111307,0.00111307, 0.00111307, 0.00111307, 0.00111307, 0.00111307,0.00111307, 0.00111307, 0.00111307, 0.00111307, 0.00111307,0.00111307, 0.00111307, 0.00111307, 0.00111307, 0.00111307,0.00111307, 0.00111307, 0.00111307, 0.00111307, 0.00111307,0.00111307, 0.00111307, 0.00111307, 0.00111307, 0.00111307,0.00111307, 0.00111307, 0.00111307, 0.00111307, 0.00111307,0.00111307, 0.00111307, 0.00111307, 0.00111307, 0.00111307,0.00111307, 0.00111307, 0.00111307, 0.00111307, 0.00111307,0.00111307, 0.00111307, 0.00111307, 0.00111307, 0.00111307,0.00111307, 0.00111307, 0.00111307, 0.00111307, 0.00111307,0.00111307, 0.00111307, 0.00111307, 0.00111307, 0.00111307,0.00111307, 0.00111307, 0.00111307, 0.00111307, 0.00111307,0.00111307, 0.00111307, 0.00111307, 0.00111307, 0.00111307,0.00111307, 0.00111307, 0.00111307, 0.00111307, 0.00111307,0.00111307, 0.00111307, 0.00111307, 0.00111478, 0.00111478,0.00111478, 0.00111649, 0.0011182, 0.00111991, 0.00112334,0.00112505, 0.00112848, 0.00113533, 0.00113875, 0.00115416,0.00116615, 0.0011867, 0.00120896, 0.00124835, 0.00124835,0.00124835, 0.00124835, 0.00124835, 0.00124835, 0.00124835,0.00124835, 0.00124835, 0.00124835, 0.00124835, 0.00124835,0.00124835, 0.00124835, 0.00124835, 0.00124835, 0.00124835,0.00124835, 0.00124835, 0.00124835, 0.00124835, 0.00124835,0.00124835, 0.00124835, 0.00124835, 0.00124835, 0.00124835,0.00124835, 0.00124835, 0.00124835, 0.00124835, 0.00124835,0.00124835, 0.00124835, 0.00124835, 0.00124835, 0.00124835,0.00124835, 0.00124835, 0.00124835, 0.00124835, 0.00124835,0.00124835, 0.00124835, 0.00124835, 0.00124835, 0.00124835,0.00124835, 0.00124835, 0.00124835, 0.00124835, 0.00124835,0.00124835, 0.00124835, 0.00124835, 0.00124835, 0.00124835,0.00124835, 0.00124835, 0.00124835, 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推荐答案

当 normed=True 时，counts 可以解释为 pdf 值:

When normed=True, the counts can be interpreted as pdf values:

counts, bin_edges = np.histogram(a, bins=num_bins, normed=True)

cdf 由

dx = bin_edges[1]-bin_edges[0]
cdf = np.cumsum(counts*dx)

bin 边缘之间的距离是均匀的，所以 dx 是恒定的.counts*dx 给出每个 bin 的概率质量.现在np.cumsum的概率质量给出了累积分布函数.

The distance between the bin edges is uniform, so dx is constant. counts*dx gives the probability mass for each bin. Now np.cumsum of the probability masses gives the cumulative distribution function.

assert np.allclose(cdf[-1], 1)

这篇关于如何在python中为我的数据正确获取累积分布函数?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！