Question

我有一个数据框，该数据框在一段时间内具有988个不同的产品值，

注意：这仅适用于一种产品（ContextID是产品编号）

p1 = unique_df[unique_df['ContextID'] == 7289972]
ocsvm = OneClassSVM(nu = 0.07, kernel = 'rbf', gamma = 'scale')
p1['y_ocsvm1'] = ocsvm.fit_predict(p1.values[:,[1]])

这提供了以下数据框：

    ContextID   BacksGas_Flow_sccm  StepID  Time_Elapsed    y_ocsvm1
104083  7289972 1.953125    1   0.0 1
104084  7289972 1.953125    1   0.055   1
104085  7289972 2.05078125  2   0.156   1
104086  7289972 2.05078125  2   0.48700000000000004 1
104087  7289972 2.05078125  2   1.477   1
104088  7289972 1.953125    2   2.4770000000000003  1
104089  7289972 1.7578125   2   3.4770000000000003  1
104090  7289972 1.7578125   2   4.487   1
104091  7289972 1.85546875  2   5.993   1
104092  7289972 1.7578125   2   6.545000000000001   1
104093  7289972 9.08203125  5   7.9830000000000005  1
104094  7289972 46.19140625 5   8.993   1
104095  7289972 46.19140625 5   9.993   1
104096  7289972 46.19140625 5   11.393  1
104097  7289972 46.19140625 5   11.993  1
104098  7289972 46.6796875  5   13.093  1
104099  7289972 46.6796875  5   13.384  1
104100  7289972 46.6796875  5   14.388000000000002  1
104101  7289972 46.6796875  5   15.386000000000001  1
104102  7289972 46.6796875  5   16.386000000000003  1
104103  7289972 46.6796875  5   17.396  1
104104  7289972 46.6796875  5   18.406000000000002  1
104105  7289972 46.6796875  5   19.396  1
104106  7289972 46.6796875  5   20.396  1
104107  7289972 46.6796875  5   21.396  1
104108  7289972 46.6796875  7   22.386000000000003  1
104109  7289972 46.6796875  7   23.456000000000003  1
104110  7289972 46.6796875  7   24.404  1
104111  7289972 46.6796875  12  25.443  1
104112  7289972 46.6796875  12  26.443  1
104113  7289972 46.6796875  12  27.443  1
104114  7289972 46.6796875  12  28.453000000000003  1
104115  7289972 46.6796875  12  29.443  1
104116  7289972 46.6796875  12  30.443  1
104117  7289972 46.6796875  12  31.443  1
104118  7289972 46.6796875  15  32.472  1
104119  7289972 46.6796875  15  33.444  1
104120  7289972 46.6796875  16  34.443000000000005  1
104121  7289972 46.6796875  16  35.443000000000005  1
104122  7289972 46.6796875  17  36.443000000000005  1
104123  7289972 25.09765625 19  37.503  -1
104124  7289972 45.99609375 19  38.513000000000005  -1
104125  7289972 59.08203125 19  39.503  1
104126  7289972 61.81640625 19  40.503  1
104127  7289972 62.59765625 19  41.503  1
104128  7289972 63.671875   19  42.503  1
104129  7289972 65.625  19  43.503  1
104130  7289972 66.69921875 19  44.503  1
104131  7289972 67.3828125  19  45.532000000000004  1
104132  7289972 67.3828125  19  46.502  1
104133  7289972 67.67578125 19  47.501000000000005  1
104134  7289972 68.26171875 19  48.501000000000005  1
104135  7289972 69.04296875 19  49.501000000000005  1
104136  7289972 69.82421875 19  50.501000000000005  1
104137  7289972 69.82421875 19  51.501000000000005  1
104138  7289972 70.8984375  19  52.501000000000005  1
104139  7289972 70.8984375  19  53.502  1
104140  7289972 70.8984375  19  54.502  1
104141  7289972 70.8984375  19  55.502  1
104142  7289972 71.6796875  19  56.502  1
104143  7289972 71.6796875  19  57.50000000000001   1
104144  7289972 72.55859375 19  58.923  1
104145  7289972 72.55859375 19  59.541000000000004  1
104146  7289972 72.55859375 19  60.541000000000004  1
104147  7289972 72.55859375 19  61.540000000000006  1
104148  7289972 72.55859375 19  62.540000000000006  1
104149  7289972 72.55859375 19  63.540000000000006  1
104150  7289972 73.33984375 19  64.54   1
104151  7289972 73.33984375 19  65.539  1
104152  7289972 73.33984375 19  66.539  1
104153  7289972 74.12109375 19  67.539  1
104154  7289972 74.12109375 19  68.539  1
104155  7289972 74.12109375 19  69.54   1
104156  7289972 73.2421875  19  70.54   1
104157  7289972 73.2421875  19  71.54   1
104158  7289972 74.0234375  19  73.02300000000001   1
104159  7289972 74.0234375  19  73.55000000000001   1
104160  7289972 74.0234375  19  75.153  1
104161  7289972 74.0234375  19  75.693  1
104162  7289972 74.0234375  19  76.953  1
104163  7289972 74.0234375  19  78.093  1
104164  7289972 74.0234375  19  78.693  1
104165  7289972 74.0234375  19  80.05300000000001   1
104166  7289972 74.0234375  19  80.703  1
104167  7289972 74.90234375 19  81.703  1
104168  7289972 74.90234375 19  82.953  1
104169  7289972 74.12109375 19  83.69300000000001   1
104170  7289972 74.12109375 19  84.69300000000001   1
104171  7289972 74.12109375 19  85.69300000000001   1
104172  7289972 74.12109375 19  86.69300000000001   1
104173  7289972 74.12109375 19  88.10300000000001   1
104174  7289972 75.0    19  88.69300000000001   -1
104175  7289972 75.0    19  89.953  -1
104176  7289972 75.0    19  90.953  -1
104177  7289972 74.21875    19  91.953  1
104178  7289972 74.21875    19  92.953  1
104179  7289972 74.21875    19  93.69300000000001   1
104180  7289972 75.0    19  94.69300000000001   -1
104181  7289972 75.0    19  95.953  -1
104182  7289972 75.0    19  96.69300000000001   -1
104183  7289972 75.0    19  97.69300000000001   -1
104184  7289972 74.12109375 19  98.953  1
104185  7289972 74.12109375 19  99.653  1
104186  7289972 74.12109375 19  100.543 1
104187  7289972 74.90234375 19  101.85300000000001  1
104188  7289972 6.4453125   24  102.545 1
104189  7289972 3.515625    24  104.13300000000001  1
104190  7289972 2.5390625   24  104.983 1
104191  7289972 2.05078125  24  105.873 1
104192  7289972 2.05078125  24  106.97300000000001  1
104193  7289972 2.05078125  24  107.665 1
104194  7289972 1.953125    24  108.70500000000001  1
104195  7289972 1.953125    24  108.786 1
104196  7289972 1.953125    24  109.253 1
104197  7289972 1.953125    24  110.17500000000001  1
104198  7289972 2.05078125  24  111.165 1
104199  7289972 1.85546875  24  112.16300000000001  1
104200  7289972 1.85546875  24  113.165 1
104201  7289972 1.85546875  24  114.165 1
104202  7289972 1.85546875  24  115.165 1
104203  7289972 1.85546875  24  116.165 1
104204  7289972 2.05078125  24  117.23500000000001  1
104205  7289972 1.953125    24  118.185 1
104206  7289972 1.953125    24  119.185 1
104207  7289972 1.7578125   24  120.185 1
104208  7289972 1.66015625  24  121.185 -1
104209  7289972 1.7578125   24  122.185 1
104210  7289972 1.7578125   24  123.185 1
104211  7289972 1.7578125   24  124.185 1
104212  7289972 1.85546875  24  125.185 1
104213  7289972 1.85546875  24  126.185 1
104214  7289972 1.953125    24  127.224 1
104215  7289972 1.953125    24  127.41000000000001  1
104216  7289972 1.953125    24  128.073 1
104217  7289972 1.953125    24  128.672 1
104218  7289972 1.953125    24  129.692 1
104219  7289972 1.7578125   24  130.74200000000002  1
104220  7289972 1.85546875  24  131.782 1
104221  7289972 1.85546875  24  132.83200000000002  1
104222  7289972 1.85546875  24  133.852 1
104223  7289972 1.7578125   24  134.882 1
104224  7289972 1.85546875  24  135.9   1
104225  7289972 1.85546875  24  136.92000000000002  1
104226  7289972 1.7578125   24  137.93200000000002  1
104227  7289972 1.7578125   25  138.45100000000002  1
104228  7289972 1.85546875  25  139.481 1
104229  7289972 1.85546875  25  140.501 1
104230  7289972 1.85546875  26  141.531 1
104231  7289972 1.7578125   26  142.55100000000002  1
104232  7289972 1.953125    26  143.833 1
104233  7289972 1.953125    26  144.681 1
104234  7289972 1.85546875  26  145.741 1
104235  7289972 1.85546875  27  146.77  1
104236  7289972 1.85546875  27  147.79000000000002  1
104237  7289972 1.85546875  27  148.82000000000002  1
104238  7289972 1.953125    27  149.84  1
104239  7289972 1.85546875  27  150.86  1
104240  7289972 1.953125    27  151.92000000000002  1
104241  7289972 1.85546875  27  152.958 1
104242  7289972 1.7578125   27  153.978 1
104243  7289972 1.85546875  27  155.008 1
104244  7289972 1.85546875  27  156.02800000000002  1
104245  7289972 1.7578125   27  157.048 1
104246  7289972 1.85546875  27  158.12800000000001  1

之后，我绘制了Time_Elapsed与BacksGas_Flow_sccm曲线，如下所示：

x_axis = p1.values[:,3]
y_axis = p1.values[:,1]

plt.figure(3)
plt.plot(x_axis, y_axis)
plt.scatter(p1.values[p1['y_ocsvm1'] == 1, 3], p1.values[p1['y_ocsvm1'] == 1, 1], c = 'green', label = 'Normal')
plt.scatter(p1.values[p1['y_ocsvm1'] == -1, 3], p1.values[p1['y_ocsvm1'] == -1, 1], c = 'red', label = 'Outlier')

这给了我如下图：

我需要以下任务的帮助：

我的数据框如上所述具有988个不同的ContextID（产品）。我想知道如何绘制图，例如将前200个产品放在一起，以便所有图彼此重叠并显示在一个窗口中，然后接下来的200个产品彼此重叠并显示在第二个窗口中，依此类推。因此，最后，我将有5个不同的窗口，其中4个将具有200种不同产品的重叠图，第5个窗口将具有188种不同产品的重叠图。
如果可能的话，可以使用plotly来确保交互性来完成上述任务，这意味着当我将鼠标悬停在一个图上时，它会分别突出显示吗？如果没有，那么matplotlib或seaborn对我来说绝对没问题

Answer 1

使用itertools spread recipe和grouper

pandas.DataFrame.groupby

我们将使用它来对每200个数据进行分组

然后，我们需要一种将一组数据添加到轴def grouper(iterable, n, fillvalue=None): "Collect data into fixed-length chunks or blocks" # grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx" args = [iter(iterable)] * n return zip_longest(*args, fillvalue=fillvalue)

的方法

ax

以及将数据分组的方法，为每个子组创建一个新图，然后将不同的def add_to_axes(ax, data, context_id=None): data.plot( x="Time_Elapsed", y="BacksGas_Flow_sccm", label=context_id, # color="blue", ax=ax, ) outlier = data["y_ocsvm1"] == -1 data[~outlier].plot.scatter( x="Time_Elapsed", y="BacksGas_Flow_sccm", color="green", label="Normal", ax=ax, ) data[outlier].plot.scatter( x="Time_Elapsed", y="BacksGas_Flow_sccm", color="red", label="outlier", ax=ax, )添加到绘图中。

context_id

可以这样称呼以生成图：

def group_plots(df, group_size=200):
    for group in grouper(df.groupby("ContextID"), n=group_size):
        fig, ax = plt.subplots()
        # group = list(filter(None, group))
        # print(group)
        for context_id, data in filter(None, group):
            # print(context_id, data.head())
            add_to_axes(ax, data, context_id)
        yield fig

我不知道if __name__ == "__main__": filename = Path("data/test2.csv") data = pd.read_csv(filename, delimiter="\s+", decimal=",") # my dummy data for i, fig in enumerate(group_plots(data)): fig.savefig(f"data/output{i}.png") # or do whathever you need with the fig，所以也可以这样做。主要组件保持不变：

一种用于对数据进行分组的方法，一种用于为每个数据子组创建新图形的方法以及一种将1系列添加到现有图的方法

如何同时绘制不同产品的时间序列图？

1 个答案: