scipy.interpolate.make_interp_spline 给出

Question

我正在尝试创建一个平滑的频率分布图。该代码适用于某个数据集，但为另一个数据集提供以下错误消息：spl1 = make_interp_spline(bins1, data1['Frequency'].values)File "/<path_to_anaconda3>/envs/mlpy37/lib/python3.7/site-packages/scipy/interpolate/_bsplines.py", line 805, in make_interp_spline    raise ValueError('x and y are incompatible.')ValueError: x and y are incompatible.以下是可以正常工作的数据集的代码：import mathimport numpy as npimport pandas as pdimport statisticsfrom scipy.stats import skewfrom matplotlib import pyplot as pltfrom scipy.interpolate import make_interp_splineraw_data1 = [212, 869, 220, 654, 11, 624, 420, 121, 428, 865, 799, 405, 230, 670, 870, 366, 99, 55, 489, 312, 493, 163, 221, 84, 144, 48, 375, 86, 168, 100]min_value1 = min(raw_data1)max_value1 = max(raw_data1)step1 = math.ceil((max_value1 - min_value1) / 10)bin_edges1 = [i for i in range(min_value1 - 1, max_value1 + 1, step1)]bins1 = [i for i in range(min_value1, max_value1 + 1, step1)]if max(bin_edges1) < max_value1:    bin_edges1.append(max(bin_edges1) + step1)    bins1.append(max(bins1) + step1)data1 = pd.DataFrame({'Frequency': pd.cut(raw_data1, bin_edges1).value_counts()})x1 = np.linspace(min(bins1), max(bins1), 250)spl1 = make_interp_spline(bins1, data1['Frequency'].values)smooth_curve1 = spl1(x1)print(data1)mean1 = statistics.mean(raw_data1)median1 = statistics.median(raw_data1)print('Mean: {:.2f}'.format(mean1))print('Median: {:.2f}'.format(median1))try:    print('Mode: {:.2f}'.format(statistics.mode(raw_data1)))except Exception as e:    print(e)skewness1 = skew(raw_data1)if mean1 > median1:    print('Positive Skewness: ' + str(skewness1))elif mean1 < median1:    print('Negative Skewness: ' + str(skewness1))else:    print('No skewness: ' + str(skewness1))plt.figure()plt.subplot(111)plt.plot(x1, smooth_curve1)plt.title('Numerical Variables Exercise Skewness')plt.xlabel('Data')plt.ylabel('Frequency')plt.show()

Answer 1

注释掉一行实际上解决了问题（或者至少它运行了，我无法验证输出）。错误消息很有用：x 和 y 应该是相同的长度。

if max(bin_edges1) < max_value1:

    bin_edges1.append(max(bin_edges1) + step1)

    # bins1.append(max(bins1) + step1) <-- this one

此外，您的代码很难遵循，因为您混淆了您的工具。您也将其定义raw_data1为 python 列表bins1，并使用列表理解。

raw_data1 = [212, 869, 220, 654, 11, 624, 420, 121, 428, 865, 799, 405, 230, 670, 870, 366, 99, 55, 489, 312, 493, 163, 221, 84, 144, 48, 375, 86, 168, 100]

..

bins1 = [i for i in range(min_value1, max_value1 + 1, step1)]

然后你使用 numpy.linspace 为x1.

x1 = np.linspace(min(bins1), max(bins1), 250)

还涉及熊猫：

data1 = pd.DataFrame({'Frequency': pd.cut(raw_data1, bin_edges1).value_counts()})

我建议主要使用一个，仅在必要时使用其他工具。