数据质量分析—绘制箱型图

2019独角兽企业重金招聘Python工程师标准>>>

箱型图如下所示：

计算过程：

（1）计算上四分位数（Q3），中位数，下四分位数（Q1） 
（2）计算上四分位数和下四分位数之间的差值，即四分位数差（IQR，interquartile range）Q3-Q1 
（3）绘制箱线图的上下范围，上限为上四分位数，下限为下四分位数。在箱子内部中位数的位置绘制横线。 
（4）大于上四分位数1.5倍四分位数差的值，或者小于下四分位数1.5倍四分位数差的值，划为异常值（outliers）。 
（5）异常值之外，最靠近上边缘和下边缘的两个值处，画横线，作为箱线图的触须。 
（6）极端异常值，即超出四分位数差3倍距离的异常值，用实心点表示；较为温和的异常值，即处于1.5倍-3倍四分位数差之间的异常值，用空心点表示。 
（7）为箱线图添加名称，数轴等

 

对于异常值处理可取消异常值，或当作缺失值来处理。

如下是代码，  其中 whis 参数对异常值进行限定。其含义是 大于 上四分位点的whis倍、小于下四分位点whis倍的数值则显示为异常点。

"""
数据质量分析
用箱型图进行异常值检测
"""
import pandas as pd
import matplotlib.pyplot as plt


def box_plot():
    """
    绘制箱型图
    :return:
    """
    feature = pd.read_csv('./dataset/feature')
    data = feature['licheng']

    # plt.boxplot(data, sym="o", whis=1.5)
    # plt.boxplot(data, sym="o", whis=0.01)
    plt.boxplot(data, sym="o", whis=1)
    plt.show()


if __name__ == '__main__':
    box_plot()

 

licheng,youxi,bingbang
40920,8.326976,0.953952
14488,7.153469,1.673904
26052,1.441871,0.805124
75136,13.147394,0.428964
38344,1.669788,0.134296
72993,10.14174,1.0329549999999998
35948,6.830792,1.213192
42666,13.276369,0.54388
67497,8.631577,0.749278
35483,12.273169,1.508053
50242,3.723498,0.831917
63275,8.385879,1.669485
5569,4.875435,0.728658
51052,4.680098,0.625224
77372,15.29957,0.331351
43673,1.889461,0.191283
61364,7.516754,1.269164
69673,14.239195,0.261333
15669,0.0,1.250185
28488,10.528555,1.304844
6487,3.540265,0.822483
37708,2.991551,0.8339200000000001
22620,5.297865,0.638306
28782,6.593803,0.187108
19739,2.81676,1.686209
36788,12.458258,0.649617
5741,0.0,1.656418
28567,9.968648,0.731232
6808,1.364838,0.640103
41611,0.230453,1.151996
36661,11.865402,0.88281
43605,0.12046,1.352013
15360,8.545204,1.340429
63796,5.856649,0.16000599999999998
10743,9.665617999999998,0.778626
70808,9.778763,1.084103
72011,4.932976,0.632026
5914,2.216246,0.587095
14851,14.305635999999998,0.632317
33553,12.591889,0.686581
44952,3.424649,1.004504
17934,0.0,0.147573
27738,8.533823,0.205324
29290,9.829528,0.23862
42330,11.492186,0.263499
36429,3.570968,0.832254
39623,1.7712279999999998,0.207612
32404,3.513921,0.991854
27268,4.398172,0.975024
5477,4.276823,1.174874
14254,5.946014,1.614244
68613,13.79897,0.724375
41539,10.393591,1.6637240000000002
7917,3.007577,0.297302
21331,1.031938,0.486174
8338,4.7512120000000015,0.064693
5176,3.692269,1.655113
18983,10.448091,0.267652
68837,10.585786,0.329557
13438,1.604501,0.069064
48849,3.679497,0.961466
12285,3.795146,0.6966939999999999
7826,2.531885,1.659173
5565,9.73334,0.977746
10346,6.093067,1.413798
1823,7.712960000000002,1.054927
9744,11.470364,0.7604609999999999
16857,2.886529,0.934416
39336,10.054373,1.138351
65230,9.97247,0.881876
2463,2.335785,1.3661450000000002
27353,11.375155,1.528626
16191,0.0,0.6056189999999999
12258,4.126787,0.357501
42377,6.319522,1.058602
25607,8.680527,0.08695499999999999
77450,14.856391,1.129823
58732,2.454285,0.22238
46426,7.292202,0.548607
32688,8.745137,0.857348
64890,8.579001,0.683048
8554,2.507302,0.8691770000000001
28861,11.415476,1.505466
42050,4.83854,1.6808919999999998
32193,10.339507,0.583646
64895,6.573742,1.151433
2355,6.539397,0.462065
0,2.209159,0.723567
70406,11.196378,0.836326
57399,4.229595,0.128253
41732,9.505944,0.005273
11429,8.652725,1.348934
75270,17.101108,0.490712
5459,7.871839,0.717662
73520,8.262131,1.361646
40279,9.015635,1.658555
21540,9.215351,0.806762
17694,6.375007,0.033678
22329,2.262014,1.022169
46570,5.67711,0.7094689999999999
42403,11.293017,0.207976
33654,6.590043,1.353117
9171,4.71196,0.194167
28122,8.768099000000001,1.108041
34095,11.502519,0.545097
1774,4.682812,0.578112
40131,12.446578,0.300754
13994,12.908384,1.657722
77064,12.601108,0.974527
11210,3.929456,0.025466
6122,9.751503,1.18205
15341,3.043767,0.8881680000000001
44373,4.391522,0.8071
28454,11.695276,0.6790149999999999
63771,7.879742,0.154263
9217,5.613163,0.933632
69076,9.140172,0.8513
24489,4.258644,0.206892
16871,6.799831,1.221171
39776,8.752758,0.484418
5901,1.123033,1.180352
40987,10.833248,1.585426
7479,3.051618,0.026781
38768,5.308408999999998,0.030683
4933,1.841792,0.028099
32311,2.261978,1.605603
26501,11.573696,1.061347
37433,8.038764,1.08391
23503,10.734007,0.10371500000000003
68607,9.661909,0.350772
27742,9.00585,0.548737
11303,0.0,0.539131
0,5.75714,1.062373
32729,9.164656,1.624565
24619,1.31834,1.436243
42414,14.075597,0.695934
20210,10.10755,1.308398
33225,7.960292999999999,1.21976
54483,6.317292,0.018209
18475,12.664194,0.595653
33926,2.906644,0.581657
43865,2.388241,0.913938
26547,6.024471,0.486215
44404,7.226764,1.255329
16674,4.183997,1.27529
8123,11.850211,1.096981
42747,11.661797,1.167935
56054,3.574967,0.494666
10933,0.0,0.107475
18121,7.9376570000000015,0.904799
11272,3.3650269999999995,1.014085
16297,0.0,0.367491
28168,13.860672,1.29327
40963,10.306714,1.211594
31685,7.228002,0.67067
55164,4.50874,1.036192
17595,0.366328,0.163652
1862,3.2994440000000003,0.575152
57087,0.573287,0.607915
63082,9.183738,0.01228
51213,7.842646,1.