在Python中，我们通常使用pandas库来处理和分析数据。数据填充是数据预处理的一个重要步骤，用于处理数据中的缺失值。以下是使用pandas库进行数据填充的示例代码：
在数据分析中，处理缺失值（空值）是一个重要的预处理步骤。缺失值的存在可能导致数据分析结果不准确或产生误导。因此，需要采取适当的策略来填补这些缺失值。以下是几种常见的空值填补办法：
1. 常数填充
将缺失值替换为某个常数，例如0、中位数、均值或者一个特殊的标识值。这种方法的优点是简单快速，但缺点是可能引入偏差，因为填充的常数可能与实际数据分布不符。
python复制代码
|---|---------------------------------------------------|
| | df['column_name'].fillna(value=0, inplace=True) |
2. 均值/中位数/众数填充
根据列的数据类型（数值型或分类型），使用列的均值、中位数或众数来填充缺失值。这种方法适用于数值型数据，可以保持数据的分布特性。
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|---|--------------------------------------------------------------------------|
| | df['column_name'].fillna(value=df['column_name'].mean(), inplace=True) |
3. 插值填充
对于时间序列数据，可以使用插值方法（如线性插值、多项式插值等）来估计缺失值。这种方法能够考虑到数据随时间的变化趋势。
python复制代码
|---|----------------------------------------------------------------|
| | df['column_name'].interpolate(method='linear', inplace=True) |
4. 基于模型预测填充
使用机器学习模型（如回归模型、决策树、随机森林等）来预测缺失值。这种方法更加复杂，但可能更准确地估计缺失值，尤其是当缺失值与其他变量存在复杂关系时。
python复制代码
|---|------------------------------------------------------------|
| | from sklearn.impute import IterativeImputer |
| | imputer = IterativeImputer(max_iter=10, random_state=0) |
| | df_filled = imputer.fit_transform(df) |
5. 热卡填充（Hot Deck Imputation）
从数据集中随机选择一个非缺失值来替换缺失值。这种方法可以保持数据的分布特性，但可能引入随机性。
6. 多重插补（Multiple Imputation）
创建多个数据集，每个数据集对缺失值进行不同的填充，然后对这些数据集的分析结果进行合并，以考虑由填充缺失值引起的不确定性。
7. 基于相似性的填充
对于分类数据，可以根据其他相似样本的值来填充缺失值。例如，可以使用K近邻算法来找到与缺失值样本最相似的样本，并用这些样本的值来填充缺失值。
注意事项：
- 在选择填充方法时，应考虑数据的性质、缺失值的比例以及分析的目的。
- 在填充缺失值后，建议检查数据的分布和统计特性，以确保填充没有引入不合理的偏差。
- 有时，缺失值可能包含有用的信息（例如，某些缺失值可能表示"不适用"或"未知"），在这种情况下，应谨慎处理，避免不必要的填充。
首先，确保你已经安装了pandas库。如果没有，你可以使用pip来安装：
bash复制代码
pip install pandas
数据例样
|----|-------|--------|----|----|----|--------|----|----|-----|-----|-----|------|-------|---|
| x1 | x2 | x3 | x4 | x5 | x6 | x7 | x8 | x9 | x10 | x11 | x12 | x13 | x14 | y |
| 1 | 22.08 | 11.46 | 2 | 4 | 4 | 1.585 | 0 | 0 | 0 | 1 | 2 | 100 | 1213 | 0 |
| 0 | 22.67 | 7 | 2 | 8 | 4 | 0.165 | 0 | 0 | 0 | 0 | 2 | 160 | 1 | 0 |
| 0 | 29.58 | 1.75 | 1 | 4 | 4 | 1.25 | 0 | 0 | 0 | 1 | 2 | 280 | 1 | 0 |
| 0 | 21.67 | | 1 | 5 | 3 | 0 | 1 | 1 | 11 | 1 | 2 | 0 | 1 | 1 |
| 1 | 20.17 | 8.17 | 2 | 6 | 4 | 1.96 | 1 | 1 | 14 | 0 | 2 | 60 | 159 | 1 |
| 0 | | 0.585 | 2 | 8 | 8 | | 1 | 1 | 2 | 0 | 2 | | 1 | 1 |
| 1 | 17.42 | 6.5 | 2 | 3 | 4 | 0.125 | 0 | 0 | 0 | 0 | 2 | 60 | 101 | 0 |
| 0 | 58.67 | 4.46 | 2 | 11 | 8 | 3.04 | 1 | 1 | 6 | 0 | 2 | 43 | 561 | 1 |
| 1 | 27.83 | 1 | 1 | 2 | 8 | 3 | 0 | 0 | 0 | 0 | 2 | 176 | 538 | 0 |
| 0 | 55.75 | 7.08 | 2 | 4 | 8 | 6.75 | 1 | 1 | 3 | 1 | 2 | 100 | 51 | 0 |
| 1 | 33.5 | 1.75 | 2 | 14 | 8 | | 1 | 1 | 4 | 1 | 2 | 253 | 858 | 1 |
| 1 | 41.42 | 5 | 2 | 11 | 8 | 5 | 1 | 1 | 6 | 1 | 2 | 470 | 1 | 1 |
| 1 | 20.67 | 1.25 | 1 | 8 | 8 | 1.375 | 1 | 1 | 3 | 1 | 2 | 140 | | 0 |
| | 34.92 | 5 | 2 | 14 | 8 | 7.5 | 1 | 1 | 6 | 1 | 2 | 0 | 1001 | 1 |
| 1 | | 2.71 | 2 | 8 | 4 | 2.415 | 0 | 0 | | 1 | 2 | 320 | 1 | 0 |
| 1 | 48.08 | 6.04 | 2 | 4 | 4 | | 0 | 0 | 0 | 0 | 2 | 0 | 2691 | 1 |
| 1 | 29.58 | 4.5 | 2 | 9 | 4 | 7.5 | 1 | 1 | 2 | 1 | 2 | 330 | 1 | 1 |
| 0 | 18.92 | 9 | 2 | 6 | 4 | 0.75 | 1 | 1 | 2 | 0 | 2 | 88 | 592 | 1 |
| 1 | 20 | 1.25 | 1 | 4 | 4 | 0.125 | 0 | 0 | 0 | 0 | 2 | 140 | 5 | 0 |
| 0 | 22.42 | 5.665 | 2 | 11 | 4 | 2.585 | 1 | | 7 | 0 | 2 | 129 | 3258 | 1 |
| 0 | 28.17 | 0.585 | 2 | 6 | 4 | 0.04 | | 0 | 0 | 0 | 2 | | 1005 | 0 |
| 0 | 19.17 | 0.585 | 1 | 6 | 4 | 0.585 | 1 | 0 | 0 | 1 | 2 | 160 | 1 | 0 |
| 1 | 41.17 | 1.335 | 2 | 2 | 4 | 0.165 | 0 | 0 | 0 | 0 | 2 | 168 | 1 | 0 |
| 1 | 41.58 | 1.75 | 2 | 4 | 4 | 0.21 | 1 | 0 | | 0 | 2 | 160 | 1 | 0 |
| | 19.5 | | 2 | 6 | 4 | 0.79 | 0 | 0 | 0 | 0 | 2 | 80 | 351 | 0 |
| 1 | 32.75 | 1.5 | 2 | 13 | 8 | 5.5 | 1 | 1 | 3 | 1 | 2 | 0 | 1 | 1 |
| 1 | 22.5 | 0.125 | 1 | 4 | 4 | 0.125 | 0 | 0 | 0 | 0 | 2 | 200 | 71 | 0 |
| 1 | 33.17 | 3.04 | 1 | 8 | 8 | 2.04 | 1 | 1 | 1 | 1 | 2 | 180 | 18028 | 1 |
| 0 | 30.67 | 12 | 2 | 8 | 4 | 2 | 1 | 1 | 1 | 0 | 2 | 220 | 20 | 1 |
| 1 | 23.08 | 2.5 | 2 | 8 | 4 | 1.085 | 1 | 1 | 11 | 1 | 2 | 60 | 2185 | 1 |
| 1 | 27 | 0.75 | 2 | 8 | 8 | | 1 | 1 | 3 | 1 | 2 | 312 | 151 | 1 |
| 0 | 20.42 | 10.5 | 1 | 14 | 8 | 0 | 0 | 0 | 0 | 1 | 2 | 154 | 33 | 0 |
| 1 | 52.33 | 1.375 | 1 | 8 | 8 | 9.46 | 1 | 0 | | 1 | 2 | 200 | 101 | 0 |
| 1 | 23.08 | 11.5 | 2 | 9 | 8 | 2.125 | 1 | 1 | 11 | 1 | 2 | 290 | 285 | 1 |
| 1 | 42.83 | 1.25 | 2 | 7 | 4 | 13.875 | 0 | 1 | 1 | 1 | 2 | 352 | 113 | 0 |
| 1 | 74.83 | 19 | 1 | 1 | 1 | 0.04 | 0 | 1 | 2 | 0 | 2 | 0 | 352 | 0 |
| 1 | 25 | | 2 | 6 | 4 | 3 | 1 | 0 | 0 | 1 | | 20 | 1 | 1 |
| 1 | 39.58 | 13.915 | 2 | 9 | 4 | 8.625 | 1 | 1 | 6 | 1 | 2 | 70 | 1 | 1 |
| 0 | 47.75 | 8 | 2 | 8 | 4 | 7.875 | 1 | 1 | 6 | 1 | 2 | 0 | 1261 | 1 |
| 0 | 47.42 | 3 | 2 | 14 | 4 | 13.875 | 1 | 1 | 2 | 1 | 2 | 519 | 1705 | 1 |
| 1 | 23.17 | 0 | 2 | 13 | 4 | 0.085 | 1 | 0 | | 0 | 2 | 0 | 1 | 1 |
| 1 | 22.58 | 1.5 | 1 | 6 | 4 | 0.54 | 0 | 0 | 0 | 1 | 2 | 120 | 68 | 0 |
| 1 | 26.75 | 1.125 | 2 | 14 | 8 | 1.25 | 1 | 0 | 0 | 0 | 2 | 0 | 5299 | 1 |
| 1 | 63.33 | 0.54 | 2 | 8 | 4 | 0.585 | 1 | 1 | 3 | 1 | 2 | 180 | 1 | 0 |
| 1 | 23.75 | 0.415 | 1 | 8 | 4 | 0.04 | 0 | 1 | 2 | 0 | 2 | 128 | 7 | 0 |
| 0 | 20.75 | | 2 | 11 | 4 | 0.71 | 1 | 1 | 2 | 1 | 2 | 49 | 1 | 1 |
| 0 | 24.5 | 1.75 | 1 | 8 | 4 | 0.165 | 0 | 0 | 0 | 0 | 2 | 132 | 1 | 0 |
| 1 | 16.17 | 0.04 | 2 | 8 | 4 | 0.04 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 1 |
| 0 | 29.5 | 2 | 1 | 10 | 8 | 2 | 0 | 0 | 0 | 0 | 2 | 256 | 18 | 0 |
| 0 | 52.83 | 15 | 2 | 8 | 4 | 5.5 | 1 | 1 | 14 | 0 | 2 | 0 | 2201 | 1 |
| 1 | 32.33 | 3.5 | 2 | 4 | 4 | 0.5 | 0 | 0 | 0 | 1 | 2 | 232 | 1 | 0 |
| 1 | 21.08 | 4.125 | 1 | 3 | 8 | 0.04 | 0 | 0 | 0 | | 2 | 140 | 101 | 0 |
| 1 | 28.17 | 0.125 | 1 | 4 | 4 | 0.085 | 0 | 0 | 0 | 0 | 2 | 216 | 2101 | 0 |
| 1 | 19 | 1.75 | 1 | 8 | 4 | 2.335 | 0 | 0 | 0 | 1 | 2 | 112 | 7 | 0 |
| 1 | 27.58 | 3.25 | | 11 | 8 | 5.085 | 0 | 1 | 2 | 1 | 2 | | 2 | 0 |
| 1 | 27.83 | 1.5 | 2 | 9 | 4 | 2 | 1 | 1 | 11 | 1 | 2 | 434 | 36 | 1 |
| 1 | | 6.5 | 2 | 6 | 5 | 3.5 | 1 | 1 | 1 | 0 | 2 | 0 | 501 | 1 |
| 0 | 37.33 | 2.5 | 2 | 3 | 8 | 0.21 | 0 | 0 | 0 | 0 | 2 | 260 | | 0 |
| 1 | 42.5 | 4.915 | 1 | 9 | 4 | 3.165 | 1 | | 0 | 1 | 2 | 52 | 1443 | 1 |
| 1 | 56.75 | 12.25 | 2 | 7 | 4 | 1.25 | 1 | 1 | 4 | 1 | 2 | 200 | 1 | 1 |
| 1 | 43.17 | 5 | 2 | 3 | 5 | 2.25 | 0 | 0 | 0 | 1 | 2 | 141 | 1 | 0 |
| 0 | 23.75 | 0.71 | 2 | 9 | 4 | 0.25 | 0 | 1 | 1 | 1 | 2 | 240 | 5 | 0 |
| 1 | 18.5 | 2 | 2 | 3 | 4 | 1.5 | 1 | 1 | 2 | 0 | 2 | 120 | 301 | 1 |
| 0 | 40.83 | 3.5 | 2 | 3 | 5 | 0.5 | 0 | 0 | 0 | 0 | 1 | 1160 | 1 | 0 |
| 0 | 24.5 | 0.5 | 2 | 11 | 8 | 1.5 | 1 | 0 | 0 | 0 | 2 | 280 | 825 | 1 |
| 1 | 42 | 9.79 | 2 | 14 | 8 | 7.96 | 1 | 1 | 8 | 0 | 2 | 0 | 1 | 1 |
| 0 | 19.5 | 0.165 | 2 | 11 | 4 | 0.04 | 0 | 0 | 0 | 1 | 2 | 380 | 1 | 0 |
| 1 | 21.5 | 11.5 | 2 | 3 | 4 | 0.5 | 1 | 0 | 0 | 1 | 2 | 100 | 69 | 0 |
| 1 | 31.25 | 2.835 | 2 | 1 | 1 | 0 | 0 | 1 | 5 | 0 | 2 | 176 | 147 | 0 |
| | 27.25 | 1.585 | 2 | 13 | 8 | 1.835 | 1 | 1 | 12 | 1 | 2 | 583 | 714 | 1 |
| 1 | 48.75 | 26.335 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 2 | 0 | 1 | 0 |
| 0 | 30.42 | 1.375 | 2 | 9 | 8 | 0.04 | 0 | 1 | 3 | 0 | 2 | 0 | 34 | 0 |
| 1 | 29.42 | 1.25 | 2 | 9 | 4 | 1.75 | 0 | 0 | | 0 | 2 | | 1 | 0 |
| 1 | | 5.04 | 1 | | 5 | 1.5 | 1 | 1 | 8 | 1 | 2 | 144 | 8 | 1 |
| 1 | 40.25 | 21.5 | 2 | 10 | 9 | 20 | 1 | 1 | 11 | 0 | 2 | 0 | 1201 | 1 |
| 1 | 36.5 | 4.25 | 2 | 11 | 4 | 3.5 | 0 | 0 | 0 | 0 | 2 | 454 | 51 | 0 |
| 1 | 25.58 | 0.335 | 2 | 4 | 8 | 3.5 | 0 | 0 | 0 | 1 | 2 | 340 | 1 | 0 |
| 1 | 29.83 | 3.5 | 2 | 8 | 4 | 0.165 | 0 | 0 | 0 | 0 | 2 | 216 | 1 | 0 |
| 1 | 23.08 | 0 | 2 | 4 | 4 | 1 | 0 | 1 | 11 | 0 | 1 | 0 | 1 | 0 |
| 0 | 32.17 | 1.46 | 2 | 9 | 4 | 1.085 | 1 | 1 | 16 | 0 | 2 | 120 | 2080 | 1 |
| 1 | 25.17 | 3.5 | 2 | 13 | 4 | 0.625 | 1 | 1 | 7 | 0 | 2 | 0 | 7060 | 1 |
| 0 | 35.17 | 3.75 | 2 | 1 | 1 | 0 | 0 | 1 | 6 | 0 | 2 | 0 | 201 | 0 |
| 0 | 18.58 | 10 | 2 | 2 | 4 | 0.415 | 0 | 0 | 0 | 0 | 2 | 80 | 43 | 0 |
| 1 | 39.92 | 5 | 2 | 3 | 5 | 0.21 | 0 | 0 | 0 | 0 | 2 | 550 | 1 | 0 |
| 1 | 23.42 | 1 | 2 | 8 | 4 | 0.5 | 0 | 0 | 0 | 1 | 1 | 280 | 1 | 0 |
| 1 | 37.58 | 0 | 2 | 8 | 4 | 0 | 0 | 0 | 0 | 0 | 3 | 184 | 1 | 1 |
| 0 | 24.75 | 13.665 | 2 | 11 | 8 | 1.5 | 0 | 0 | 0 | 0 | 2 | 280 | 2 | 0 |
| 0 | 47 | 13 | 2 | 3 | 5 | 5.165 | 1 | 1 | 9 | 1 | 2 | 0 | 1 | 1 |
| 1 | 34.17 | 5.25 | 2 | 9 | 4 | 0.085 | 0 | 0 | 0 | 1 | 2 | 290 | 7 | 1 |
| 1 | 22.17 | 0.585 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 100 | 1 | 0 |
| 1 | 27.75 | 1.29 | 2 | 4 | 8 | 0.25 | 0 | 0 | 0 | 1 | 1 | 140 | 1 | 0 |
| 1 | 42.75 | 4.085 | 2 | 6 | 4 | 0.04 | 0 | 0 | 0 | 0 | 2 | 108 | 101 | 0 |
| 1 | 28.67 | 14.5 | 2 | 2 | 4 | 0.125 | 0 | 0 | 0 | 0 | 2 | 0 | 287 | 0 |
| 1 | 36.25 | 5 | 2 | 8 | 5 | 2.5 | 1 | 1 | 6 | 0 | 2 | 0 | 368 | 1 |
| 0 | 18.17 | 10 | 1 | 11 | 8 | 0.165 | 0 | 0 | 0 | 0 | 2 | 340 | 1 | 0 |
| 1 | 21.25 | 1.5 | 2 | 9 | 4 | 1.5 | 0 | 0 | 0 | 0 | 2 | 150 | 9 | 1 |
| 0 | 38.92 | 1.665 | 2 | 6 | 4 | 0.25 | 0 | 0 | 0 | 0 | 2 | 0 | 391 | 0 |
| 1 | 31.83 | 0.04 | 1 | 7 | 4 | 0.04 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 |
| 0 | 17.33 | 9.5 | 2 | 6 | 4 | 1.75 | 0 | 1 | 10 | 1 | 2 | 0 | 11 | 0 |
| 0 | 20.42 | 0.835 | 2 | 11 | 4 | 1.585 | 1 | 1 | 1 | 0 | 2 | 0 | 1 | 1 |
| 0 | 39.08 | 4 | 2 | 8 | 4 | 3 | 0 | 0 | 0 | 0 | 2 | 480 | 1 | 0 |
| 1 | 38.67 | 0.21 | 2 | 4 | 4 | 0.085 | 1 | 0 | 0 | 1 | 2 | 280 | 1 | 1 |
| 1 | 27.67 | 13.75 | 2 | 9 | 4 | 5.75 | 1 | 0 | 0 | 1 | 2 | 487 | 501 | 1 |
| 1 | 27.75 | 0.585 | 1 | 13 | 4 | 0.25 | 1 | 1 | 2 | 0 | 2 | 260 | 501 | 1 |
| 1 | 19 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 4 | 0 | 2 | 45 | 2 | 0 |
| 0 | 25 | 0.875 | 2 | 14 | 8 | 1.04 | 1 | 0 | 0 | 1 | 2 | 160 | 5861 | 1 |
| 1 | 27.67 | 2 | 2 | 14 | 8 | 1 | 1 | 1 | 4 | 0 | 2 | 140 | 7545 | 1 |
| 1 | 22.25 | 9 | 2 | 6 | 4 | 0.085 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 |
| 0 | 49.83 | 13.585 | 2 | 4 | 8 | 8.5 | 1 | 0 | 0 | 1 | 2 | 0 | 1 | 1 |
| 1 | 32.33 | 2.5 | 2 | 8 | 4 | 1.25 | 0 | 0 | 0 | 1 | 2 | 280 | 1 | 0 |
| 1 | 38.25 | 10.125 | 1 | 4 | 4 | 0.125 | 0 | 0 | 0 | 0 | 2 | 160 | 1 | 0 |
| 1 | 47.33 | 6.5 | 2 | 8 | 4 | 1 | 0 | 0 | 0 | 1 | 2 | 0 | 229 | 0 |
| 1 | 27.83 | 4 | 1 | 3 | 8 | 5.75 | 1 | 1 | 2 | 1 | 2 | 75 | 1 | 0 |
| 0 | 35.75 | 0.915 | 2 | 6 | 4 | 0.75 | 1 | 1 | 4 | 0 | 2 | 0 | 1584 | 1 |
| 1 | 33.58 | 0.25 | 2 | 3 | 5 | 4 | 0 | 0 | 0 | 1 | 1 | 420 | 1 | 0 |
| 1 | 34.08 | 0.08 | 1 | 7 | 5 | 0.04 | 1 | 1 | 1 | 1 | 2 | 280 | 2001 | 1 |
| 0 | 20.75 | 10.335 | 2 | 13 | 8 | 0.335 | 1 | 1 | 1 | 1 | 2 | 80 | 51 | 1 |
| 1 | 33.17 | 1 | 2 | 14 | 4 | 0.75 | 1 | 1 | 7 | 1 | 2 | 340 | 4072 | 1 |
| 1 | 22.75 | 11 | 2 | 11 | 4 | 2.5 | 1 | 1 | 7 | 1 | 2 | 100 | 810 | 1 |
| 1 | 48.75 | 8.5 | 2 | 8 | 8 | 12.5 | 1 | 1 | 9 | 0 | 2 | 181 | 1656 | 1 |
| 1 | 40.58 | 5 | 2 | 8 | 4 | 5 | 1 | 1 | 7 | 0 | 2 | 0 | 3066 | 1 |
| 1 | 20.67 | 0.835 | 1 | 8 | 4 | 2 | 0 | 0 | 0 | 1 | 1 | 240 | 1 | 0 |
| 0 | 38.75 | 1.5 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 76 | 1 | 0 |
| 0 | 57.08 | 19.5 | 2 | 8 | 4 | 5.5 | 1 | 1 | 7 | 0 | 2 | 0 | 3001 | 1 |
| 0 | 31.25 | 3.75 | 2 | 13 | 8 | 0.625 | 1 | 1 | 9 | 1 | 2 | 181 | 1 | 1 |
| 1 | 22 | 0.79 | 2 | 9 | 4 | 0.29 | 0 | 1 | 1 | 0 | 2 | 420 | 284 | 0 |
| 0 | 58.33 | 10 | 2 | 11 | 4 | 4 | 1 | 1 | 14 | 0 | 2 | 0 | 1603 | 1 |
| 1 | 28.92 | 0.375 | 2 | 8 | 4 | 0.29 | 0 | 0 | 0 | 0 | 2 | 220 | 141 | 0 |
| 0 | 46 | 4 | 2 | 5 | 3 | 0 | 1 | 0 | 0 | 0 | 2 | 100 | 961 | 1 |
| 1 | 21 | 3 | 1 | 2 | 4 | 1.085 | 1 | 1 | 8 | 1 | 2 | 160 | 2 | 1 |
| 0 | 24.75 | 12.5 | 2 | 6 | 4 | 1.5 | 1 | 1 | 12 | 1 | 2 | 120 | 568 | 1 |
| 0 | 20.83 | 0.5 | 1 | 10 | 2 | 1 | 0 | 0 | 0 | 0 | 2 | 260 | 1 | 0 |
| 1 | 24.58 | 13.5 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 184 | 1 | 0 |
| 0 | 26.5 | 2.71 | 1 | 8 | 4 | 0.085 | 0 | 0 | 0 | 0 | 1 | 80 | 1 | 0 |
| 1 | 40.92 | 0.835 | 2 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 2 | 130 | 2 | 0 |
| 0 | 38.33 | 4.415 | 2 | 8 | 4 | 0.125 | 0 | 0 | 0 | 0 | 2 | 160 | 1 | 0 |
| 1 | 19.58 | 0.585 | 2 | 1 | 1 | 0 | 0 | 1 | 3 | 0 | 2 | 350 | 770 | 0 |
| 1 | 39.25 | 9.5 | 2 | 7 | 4 | 6.5 | 1 | 1 | 14 | 0 | 2 | 240 | 4608 | 1 |
| 1 | 25.75 | 0.5 | 2 | 8 | 4 | 1.46 | 1 | 1 | 5 | 1 | 2 | 312 | 1 | 1 |
| 0 | 46.08 | 3 | 2 | 8 | 4 | 2.375 | 1 | 1 | 8 | 1 | 2 | 396 | 4160 | 1 |
| 1 | 19.67 | 10 | 1 | 4 | 8 | 0.835 | 1 | 0 | 0 | 1 | 2 | 140 | 1 | 0 |
| 0 | 22.25 | 1.25 | 1 | 1 | 1 | 3.25 | 0 | 0 | 0 | 0 | 2 | 280 | 1 | 0 |
| 1 | 18.83 | 3.54 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 180 | 2 | 0 |
| 1 | 64.08 | 20 | 2 | 14 | 8 | 17.5 | 1 | 1 | 9 | 1 | 2 | 0 | 1001 | 1 |
| 0 | 16.5 | 1.25 | 2 | 11 | 4 | 0.25 | 0 | 1 | 1 | 0 | 2 | 108 | 99 | 0 |
| 0 | 68.67 | 15 | 2 | 10 | 9 | 0 | 1 | 1 | 14 | 0 | 2 | 0 | 3377 | 1 |
| 1 | 76.75 | 22.29 | 2 | 10 | 9 | 12.75 | 1 | 1 | 1 | 1 | 2 | 0 | 110 | 1 |
| 0 | 15.92 | 2.875 | 2 | 11 | 4 | 0.085 | 0 | 0 | 0 | 0 | 2 | 120 | 1 | 0 |
| 1 | 34.83 | 4 | 2 | 2 | 5 | 12.5 | 1 | 0 | 0 | 1 | 2 | 184 | 1 | 0 |
| 0 | 47.42 | 8 | 2 | 10 | 5 | 6.5 | 1 | 1 | 6 | 0 | 2 | 375 | 51101 | 1 |
| 1 | 23.17 | 0 | 2 | 8 | 4 | 0 | 0 | 0 | 0 | 0 | 3 | 184 | 1 | 1 |
| 1 | 45.17 | 1.5 | 2 | 8 | 4 | 2.5 | 1 | 0 | 0 | 1 | 2 | 140 | 1 | 0 |
| 1 | 15.17 | 7 | 2 | 10 | 4 | 1 | 0 | 0 | 0 | 0 | 2 | 600 | 1 | 0 |
| 1 | 18.83 | 0.415 | 1 | 8 | 4 | 0.165 | 0 | 1 | 1 | 0 | 2 | 200 | 2 | 0 |
| 1 | 52.5 | 6.5 | 2 | 4 | 4 | 6.29 | 1 | 1 | 15 | 0 | 2 | 0 | 11203 | 1 |
| 1 | 19.17 | 0 | 1 | 7 | 5 | 0 | 0 | 0 | 0 | 1 | 1 | 500 | 2 | 1 |
| 1 | 18 | 0.165 | 2 | 11 | 7 | 0.21 | 0 | 0 | 0 | 0 | 2 | 200 | 41 | 1 |
| 1 | 37.5 | 0.835 | 2 | 10 | 4 | 0.04 | 0 | 0 | 0 | 0 | 2 | 120 | 6 | 0 |
| 1 | 22.67 | 1.585 | 1 | 9 | 4 | 3.085 | 1 | 1 | 6 | 0 | 2 | 80 | 1 | 1 |
| 1 | 47.83 | 4.165 | 2 | 14 | 5 | 0.085 | 0 | 0 | 0 | 1 | 2 | 520 | 1 | 0 |
| 1 | 34.08 | 2.5 | 2 | 8 | 4 | 1 | 0 | 0 | 0 | 0 | 2 | 460 | 17 | 0 |
| 0 | 33.08 | 1.625 | 2 | 2 | 4 | 0.54 | 0 | 0 | 0 | 1 | 2 | 0 | 1 | 0 |
| 1 | 43.08 | 0.375 | 1 | 8 | 4 | 0.375 | 1 | 1 | 8 | 1 | 2 | 300 | 163 | 1 |
| 1 | 34.5 | 4.04 | 1 | 3 | 5 | 8.5 | 1 | 1 | 7 | 1 | 2 | 195 | 1 | 1 |
| 1 | 42.75 | 3 | 2 | 3 | 5 | 1 | 1 | 0 | 0 | 0 | 2 | 0 | 201 | 0 |
| 1 | 18.25 | 0.165 | 2 | 2 | 4 | 0.25 | 0 | 0 | 0 | 1 | 1 | 280 | 1 | 0 |
| 1 | 23.08 | 2.5 | 2 | 1 | 1 | 0.085 | 0 | 0 | 0 | 1 | 2 | 100 | 4209 | 0 |
| 0 | 22.5 | 8.46 | 1 | 14 | 4 | 2.46 | 0 | 0 | 0 | 0 | 2 | 164 | 1 | 1 |
| 1 | 17.92 | 0.205 | 2 | 6 | 4 | 0.04 | 0 | 0 | 0 | 0 | 2 | 280 | 751 | 0 |
| 1 | 18.42 | 10.415 | 1 | 6 | 4 | 0.125 | 1 | 0 | 0 | 0 | 2 | 120 | 376 | 0 |
| 0 | 27.67 | 1.5 | 2 | 7 | 4 | 2 | 1 | 0 | 0 | 0 | 1 | 368 | 1 | 0 |
| 0 | 18.92 | 9.25 | 1 | 8 | 4 | 1 | 1 | 1 | 4 | 1 | 2 | 80 | 501 | 1 |
| 1 | 22.67 | 0.75 | 2 | 3 | 4 | 1.585 | 0 | 1 | 1 | 1 | 2 | 400 | 10 | 0 |
| 1 | 62.5 | 12.75 | 1 | 8 | 8 | 5 | 1 | 0 | 0 | 0 | 2 | 112 | 1 | 0 |
| 0 | 23.5 | 9 | 2 | 11 | 4 | 8.5 | 1 | 1 | 5 | 1 | 2 | 120 | 1 | 1 |
| 1 | 35.25 | 3.165 | 2 | 14 | 8 | 3.75 | 1 | 0 | 0 | 1 | 2 | 680 | 1 | 0 |
| 0 | 56.83 | 4.25 | 1 | 1 | 1 | 5 | 0 | 0 | 0 | 1 | 2 | 0 | 5 | 0 |
| 0 | 53.33 | 0.165 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 62 | 28 | 0 |
| 1 | 41.17 | 4.04 | 2 | 13 | 8 | 7 | 1 | 1 | 8 | 0 | 2 | 320 | 1 | 1 |
| 1 | 42.17 | 5.04 | 2 | 11 | 8 | 12.75 | 1 | 0 | 0 | 1 | 2 | 92 | 1 | 1 |
| 1 | 41.17 | 1.25 | 1 | 9 | 4 | 0.25 | 0 | 0 | 0 | 0 | 2 | 0 | 196 | 0 |
| 0 | 33.75 | 0.75 | 2 | 4 | 5 | 1 | 1 | 1 | 3 | 1 | 2 | 212 | 1 | 0 |
| 1 | 25.67 | 12.5 | 2 | 13 | 4 | 1.21 | 1 | 1 | 67 | 1 | 2 | 140 | 259 | 1 |
| 1 | 24.33 | 6.625 | 1 | 2 | 4 | 5.5 | 1 | 0 | 0 | 1 | 1 | 100 | 1 | 1 |
| 1 | 23.33 | 11.625 | 1 | 9 | 4 | 0.835 | 1 | 0 | 0 | 1 | 2 | 160 | 301 | 1 |
| 1 | 30.67 | 2.5 | 2 | 13 | 8 | 2.25 | 0 | 0 | 0 | 1 | 1 | 340 | 1 | 0 |
| 1 | 37.17 | 4 | 2 | 8 | 5 | 5 | 1 | 0 | 0 | 1 | 1 | 280 | 1 | 0 |
| 1 | 26.25 | 1.54 | 2 | 9 | 4 | 0.125 | 0 | 0 | 0 | 0 | 2 | 100 | 1 | 0 |
| 0 | 29.75 | 0.665 | 2 | 9 | 4 | 0.25 | 0 | 0 | 0 | 1 | 2 | 300 | 1 | 0 |
| 0 | 23 | 1.835 | 2 | 5 | 3 | 0 | 0 | 1 | 1 | 0 | 2 | 200 | 54 | 0 |
| 1 | 17.25 | 3 | 2 | 4 | 4 | 0.04 | 0 | 0 | 0 | 1 | 2 | 160 | 41 | 0 |
| 1 | 29.25 | 14.79 | 2 | 6 | 4 | 5.04 | 1 | 1 | 5 | 1 | 2 | 168 | 1 | 1 |
| 0 | 28.58 | 3.54 | 2 | 3 | 5 | 0.5 | 1 | 0 | 0 | 1 | 2 | 171 | 1 | 0 |
| 1 | 34.58 | 0 | 2 | 8 | 4 | 0 | 0 | 0 | 0 | 0 | 3 | 184 | 1 | 0 |
| 1 | 23.42 | 0.585 | 2 | 8 | 8 | 0.085 | 1 | 0 | 0 | 0 | 2 | 180 | 1 | 0 |
| 1 | 25 | 11.25 | 2 | 8 | 4 | 2.5 | 1 | 1 | 17 | 0 | 2 | 200 | 1209 | 1 |
| 0 | 18.75 | 7.5 | 2 | 11 | 4 | 2.71 | 1 | 1 | 5 | 0 | 2 | 184 | 26727 | 1 |
| 1 | 17.08 | | 2 | 11 | 4 | 0.335 | 0 | 1 | 4 | 0 | 2 | 160 | 9 | 0 |
| 0 | 16.08 | 0.335 | 2 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 2 | 160 | 127 | 0 |
| 1 | 32.92 | 2.5 | 2 | 6 | 4 | 1.75 | 0 | 1 | 2 | 1 | 2 | 720 | 1 | 0 |
| 0 | 20.33 | 10 | 2 | 8 | 8 | 1 | 1 | 1 | 4 | 0 | 2 | 50 | 1466 | 1 |
| 0 | | 1.75 | 1 | 5 | 3 | 0 | 0 | 0 | 0 | 0 | 2 | 160 | 1 | 0 |
| 1 | 33.17 | 1.04 | 2 | 12 | 8 | 6.5 | 1 | 0 | 0 | 1 | 2 | 164 | 31286 | 1 |
| 0 | 25.33 | 2.085 | 2 | 8 | 8 | 2.75 | 1 | 0 | 0 | 1 | 2 | 360 | 2 | 0 |
| 0 | 24.75 | 3 | 2 | 11 | 8 | 1.835 | 1 | 1 | 19 | 0 | 2 | 0 | 501 | 1 |
| 1 | 30.83 | 0 | 2 | 9 | 4 | 1.25 | | 1 | 1 | 0 | | 202 | 1 | 1 |
| 1 | 20.75 | 5.085 | 1 | 5 | 4 | 0.29 | 0 | 0 | 0 | 0 | 2 | 140 | 185 | 0 |
| 0 | 40.33 | 8.125 | 1 | 4 | 4 | 0.165 | 0 | 1 | 2 | 0 | 2 | 184 | 19 | 0 |
| 1 | 29.42 | 1.25 | 2 | 8 | 8 | 0.25 | 0 | 1 | 2 | 1 | 2 | 400 | 109 | 0 |
| 1 | 40.92 | 0.5 | 1 | 7 | 4 | 0.5 | 0 | 0 | 0 | 1 | 2 | 130 | 1 | 0 |
| 0 | 29.5 | 0.46 | 2 | 4 | 4 | 0.54 | 1 | 1 | 4 | 0 | 2 | | 501 | 1 |
| 1 | 54.42 | 0.5 | 1 | 4 | 8 | 3.96 | 1 | 0 | 0 | 0 | 2 | 180 | 315 | 1 |
| 1 | 34 | 5.5 | 1 | 8 | 4 | 1.5 | 0 | 0 | 0 | 1 | 2 | 60 | 1 | 0 |
| 1 | 25 | 12 | 2 | 4 | 4 | 2.25 | 1 | 1 | 2 | 1 | 2 | 120 | 6 | 0 |
| 0 | 26.58 | 2.54 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 180 | 61 | 0 |
| 0 | 33.08 | 4.625 | 2 | 11 | 8 | | 1 | 1 | 2 | 0 | 2 | 0 | 1 | 1 |
| 1 | 33.67 | 2.165 | 2 | 8 | 4 | 1.5 | 0 | 0 | 0 | 0 | 3 | 120 | 1 | 0 |
| 1 | 36.17 | | 2 | 3 | 5 | 5 | 0 | 0 | 0 | 0 | 2 | 210 | 688 | 0 |
| 1 | 19.5 | 0.29 | 2 | 4 | 4 | 0.29 | 0 | 0 | 0 | 0 | 2 | 280 | 365 | 0 |
| 1 | 24.17 | 0.875 | 2 | 11 | 4 | 4.625 | 1 | 1 | 2 | 1 | 2 | 520 | 2001 | 1 |
| 0 | 30.5 | 6.5 | 2 | 8 | 5 | 4 | 1 | 1 | 7 | 1 | 2 | 0 | 3066 | 1 |
| 1 | 20 | 7 | 2 | 8 | 4 | 0.5 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 |
| 1 | 25.33 | 0.58 | 2 | 8 | 4 | 0.29 | 1 | 1 | 7 | 1 | 2 | 96 | 5125 | 1 |
| 1 | 29.58 | 4.75 | 2 | 7 | 4 | 2 | 0 | 1 | 1 | 1 | 2 | 460 | 69 | 0 |
| 1 | 43.17 | 2.25 | 2 | 3 | 5 | 0.75 | 1 | 0 | 0 | 0 | 2 | 560 | 1 | 0 |
| 1 | 28.75 | 1.165 | 2 | 4 | 4 | 0.5 | 1 | 0 | 0 | 0 | 1 | 280 | 1 | 0 |
| 1 | 31.57 | 3 | 1 | 3 | 5 | 7 | 0 | 0 | 0 | 0 | 2 | 0 | 2 | 0 |
| 1 | 20.25 | 9.96 | 2 | 10 | 2 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 1 | 1 |
| 1 | 45 | 8.5 | 2 | 13 | 8 | 14 | 1 | 1 | 1 | 1 | 2 | 88 | 2001 | 1 |
| 0 | 22.83 | 2.29 | 2 | 11 | 8 | 2.29 | 1 | 1 | 7 | 1 | 2 | 140 | 2385 | 1 |
| 0 | 20.67 | 3 | 2 | 11 | 4 | 0.165 | 1 | 1 | 3 | 0 | 2 | 100 | 7 | 1 |
| 1 | 27.83 | 1.5 | 2 | 9 | 4 | 2.25 | 0 | 1 | 1 | 1 | 2 | 100 | 4 | 0 |
| 1 | 40.92 | 2.25 | 1 | 14 | 8 | 10 | 1 | 0 | 0 | 1 | 2 | 176 | 1 | 0 |
| 1 | 56.42 | 28 | 1 | 8 | 4 | 28.5 | 1 | 1 | 40 | 0 | 2 | 0 | 16 | 1 |
| 0 | 64.08 | 0.165 | 2 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 2 | 232 | 101 | 1 |
| 0 | 22.42 | 11.25 | 1 | 14 | 8 | 0.75 | 1 | 1 | 4 | 0 | 2 | 0 | 322 | 1 |
| 0 | 24.33 | 2.5 | 1 | 3 | 5 | 4.5 | 0 | 0 | 0 | 0 | 2 | 200 | 457 | 0 |
| 0 | 69.5 | 6 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
| 1 | 35.58 | 0.75 | 2 | 4 | 4 | 1.5 | 0 | 0 | 0 | 1 | 2 | 231 | 1 | 0 |
| 1 | 48.33 | 12 | 2 | 7 | 4 | 16 | 1 | 0 | 0 | 0 | 1 | 110 | 1 | 1 |
| 0 | 28.08 | 15 | 1 | 10 | 9 | 0 | 1 | 0 | 0 | 0 | 2 | 0 | 13213 | 1 |
| 1 | 35.17 | 2.5 | 2 | 4 | 4 | 4.5 | 1 | 1 | 7 | 0 | 2 | 150 | 1271 | 1 |
| 1 | 49.5 | 7.585 | 2 | 3 | 5 | 7.585 | 1 | 1 | 15 | 1 | 2 | 0 | 5001 | 1 |
| 1 | 80.25 | 5.5 | 2 | 8 | 4 | 0.54 | 1 | 0 | 0 | 0 | 2 | 0 | 341 | 0 |
| 1 | 29.25 | 13 | 2 | 2 | 8 | 0.5 | 0 | 0 | 0 | 0 | 2 | 228 | 1 | 0 |
| 0 | 16.92 | 0.5 | 2 | 3 | 4 | 0.165 | 0 | 1 | 6 | 1 | 2 | 240 | 36 | 0 |
| 0 | 16 | 0.165 | 2 | 6 | 4 | 1 | 0 | 1 | 2 | 1 | 2 | 320 | 2 | 0 |
| 1 | 60.08 | 14.5 | 2 | 1 | 1 | 18 | 1 | 1 | 15 | 1 | 2 | 0 | 1001 | 1 |
| 1 | | 2 | 2 | 4 | 8 | | 1 | 1 | 2 | 1 | 2 | 181 | 1 | 1 |
| 1 | 41.5 | 1.54 | 2 | 3 | 5 | 3.5 | 0 | 0 | 0 | 0 | 2 | | 1 | 1 |
| 1 | 24.08 | 9 | 2 | 6 | 4 | 0.25 | 0 | 0 | 0 | 1 | 2 | 0 | 1 | 0 |
| 1 | 24.5 | 13.335 | 1 | 6 | 4 | | 0 | 0 | 0 | 1 | 2 | 120 | 476 | 0 |
| 1 | 34.75 | 15 | 2 | 12 | 7 | 5.375 | 1 | 1 | 9 | 1 | 2 | 0 | 135 | 1 |
| 1 | 33.67 | 1.25 | 2 | 9 | 4 | 1.165 | 0 | 0 | 0 | 0 | 2 | 120 | 1 | 0 |
| 0 | 40.83 | 10 | | 11 | 8 | 1.75 | 1 | 0 | 0 | 0 | 2 | 29 | 838 | 1 |
| 1 | 20.42 | 1.835 | 2 | 8 | 4 | 2.25 | 1 | 1 | 1 | 0 | 2 | 100 | 151 | 1 |
| 1 | 37.5 | 1.125 | 1 | 2 | 4 | 1.5 | 0 | 0 | 0 | 1 | 2 | 431 | 1 | 1 |
| 1 | 48.5 | 4.25 | 2 | 7 | 4 | 0.125 | 1 | 0 | 0 | 1 | 2 | 225 | 1 | 1 |
| 1 | 23 | 0.75 | 2 | 7 | 4 | 0.5 | 0 | 0 | 0 | 1 | 1 | 320 | 1 | 0 |
| 1 | | 3.375 | 2 | 8 | 8 | 8.29 | 0 | 0 | 0 | 1 | 2 | 0 | 1 | 0 |
| 1 | 16.33 | 2.75 | 2 | 6 | 4 | 0.665 | 0 | 1 | 1 | 0 | 2 | 80 | 22 | 0 |
| | 22.67 | | 2 | 11 | 8 | 1.335 | 1 | 0 | 0 | 0 | | 100 | 1 | 1 |
| 0 | 21.75 | 11.75 | 2 | 8 | 4 | 0.25 | 0 | 0 | 0 | 1 | 2 | 180 | 1 | 0 |
| 0 | 25.08 | 2.54 | 1 | 6 | 4 | 0.25 | 1 | 0 | 0 | 1 | 2 | 370 | 1 | 1 |
| 1 | 36.33 | 2.125 | 1 | 9 | 4 | 0.085 | 1 | 1 | 1 | 0 | 2 | 50 | 1188 | 1 |
| 0 | 28.58 | 3.75 | 2 | 8 | 4 | 0.25 | 0 | 1 | 1 | 1 | 2 | 40 | 155 | 0 |
| 1 | 22.17 | 12.125 | 2 | 8 | 4 | 3.335 | 0 | 1 | 2 | 1 | 2 | 180 | 174 | 0 |
| 1 | 34.17 | 1.54 | 2 | 13 | 4 | 1.54 | 1 | 1 | 1 | 1 | 2 | 520 | 50001 | 1 |
| 1 | 35 | 2.5 | 2 | 3 | 4 | 1 | 0 | 0 | 0 | 1 | 2 | 210 | 1 | 0 |
| 1 | 28.58 | 3.625 | 2 | 6 | 4 | 0.25 | 0 | 0 | 0 | 1 | 2 | 100 | 1 | 0 |
| 0 | 19.17 | 5.415 | 2 | 3 | 8 | 0.29 | 0 | 0 | 0 | 0 | 2 | 80 | 485 | 0 |
| 1 | 23.25 | 1 | 2 | 8 | 4 | 0.835 | 1 | 0 | 0 | 0 | 1 | 300 | 1 | 1 |
| 1 | 16.5 | 0.125 | 2 | 8 | 4 | 0.165 | 0 | 0 | 0 | 0 | 2 | 132 | 1 | 0 |
| 0 | 20.08 | 0.125 | 2 | 11 | 4 | 1 | 0 | 1 | 1 | 0 | 2 | 240 | 769 | 1 |
| 1 | 22.33 | 11 | 2 | 9 | 4 | 2 | 1 | 1 | 1 | 0 | 2 | 80 | 279 | 1 |
| 1 | 34 | 5.085 | 1 | 3 | 5 | 1.085 | | 0 | 0 | 1 | 2 | 480 | 1 | 0 |
| 0 | 57.08 | 0.335 | 2 | 3 | 5 | 1 | 1 | 0 | 0 | 1 | 2 | 252 | 2198 | 0 |
| 1 | 16.25 | 0.835 | 2 | 7 | 4 | 0.085 | 1 | 0 | 0 | 0 | 1 | 200 | 1 | 0 |
| 1 | 32.83 | | 2 | 13 | 8 | 2.75 | 1 | 1 | 6 | 0 | 2 | 160 | 2073 | 1 |
| 1 | 48.25 | 25.085 | 2 | 9 | 4 | 1.75 | 1 | 1 | 3 | 0 | 2 | | 15 | 1 |
| 1 | 34.17 | 2.75 | 2 | 3 | 5 | 2.5 | 0 | 0 | 0 | 1 | 2 | 232 | 201 | 0 |
| 1 | 18.33 | 1.21 | 1 | 10 | 2 | 0 | 0 | 0 | 0 | 0 | 2 | 100 | 1 | 0 |
| 1 | 44.25 | 0.5 | 2 | 7 | 4 | 10.75 | 1 | 0 | 0 | 0 | 1 | 400 | 1 | 0 |
| 1 | 38.92 | 1.75 | 2 | 4 | 4 | 0.5 | 0 | 0 | 0 | 1 | 2 | 300 | 3 | 0 |
| 1 | 31.58 | 0.75 | 1 | 6 | 4 | 3.5 | 0 | 0 | 0 | 1 | 2 | 320 | 1 | 0 |
| 1 | 25.08 | 1.71 | 2 | 14 | 4 | 1.665 | 1 | 1 | 1 | 1 | 2 | 395 | 21 | 1 |
| 1 | 65.42 | 11 | 2 | 10 | 9 | 20 | 1 | 1 | 7 | 1 | 2 | 22 | 1 | 1 |
| 1 | 41.33 | 0 | 2 | 8 | 5 | 15 | 1 | 0 | 0 | 0 | 2 | 0 | 1 | 1 |
| 1 | 31.25 | 1.125 | 2 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 2 | 96 | 20 | 0 |
| 0 | 36.75 | 4.71 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 160 | 1 | 0 |
| 1 | 32.33 | 7.5 | 2 | 10 | 5 | 1.585 | 1 | 0 | 0 | 1 | 1 | 420 | 1 | 0 |
| 0 | 31.92 | 3.125 | 2 | 1 | 1 | 3.04 | 0 | 1 | 2 | 1 | 2 | 200 | 5 | 0 |
| 1 | 24.83 | 2.75 | 2 | 8 | 4 | 2.25 | 1 | 1 | 6 | 0 | 2 | 184 | 601 | 1 |
| 1 | 21.92 | 0.54 | 1 | 14 | 4 | 0.04 | 1 | 1 | 1 | 1 | 2 | 840 | 60 | 1 |
| 1 | 34.25 | 3 | 2 | 13 | 8 | 7.415 | 1 | 0 | 0 | 1 | 2 | 0 | 1 | 1 |
| 1 | 51.83 | 3 | 1 | 1 | 1 | 1.5 | 0 | 0 | 0 | 0 | 2 | 180 | 5 | 0 |
| 0 | 31.57 | 1.5 | 2 | 1 | 1 | 0 | 0 | 1 | 2 | 1 | 2 | 200 | 106 | 0 |
| 1 | 38.42 | 0.705 | 2 | 8 | 4 | 0.375 | 0 | 1 | 2 | 0 | 2 | 225 | 501 | 0 |
| 1 | 39.17 | 1.71 | 2 | 14 | 4 | 0.125 | 1 | 1 | 5 | 1 | 2 | 480 | 1 | 1 |
| 1 | 18.17 | 2.46 | 2 | 8 | 7 | 0.96 | 0 | 1 | 2 | 1 | 2 | 160 | 588 | 0 |
| 1 | 39 | 5 | 2 | 13 | 4 | 3.5 | 1 | 1 | 10 | 1 | 2 | 0 | 1 | 1 |
| 1 | 20 | 11.045 | 2 | 8 | 4 | 2 | 0 | 0 | 0 | 1 | 2 | 136 | 1 | 0 |
| 1 | 32.75 | 2.335 | 2 | 2 | 8 | 5.75 | 0 | 0 | 0 | 1 | 2 | 292 | 1 | 0 |
| 1 | 18.58 | 5.71 | 2 | 2 | 4 | 0.54 | 0 | 0 | 0 | 0 | 2 | 120 | 1 | 0 |
| 0 | 15.83 | 7.625 | 2 | 11 | 4 | 0.125 | 0 | 1 | 1 | 1 | 2 | 0 | 161 | 0 |
| 1 | 39.33 | 5.875 | 2 | 13 | 8 | 10 | 1 | 1 | 14 | 1 | 2 | 399 | 1 | 1 |
| 0 | 19.58 | 0.665 | 2 | 9 | 4 | 1.665 | 0 | 0 | 0 | 0 | 2 | 220 | 6 | 0 |
| 1 | 21.33 | 10.5 | 2 | 8 | 4 | 3 | 1 | 0 | 0 | 1 | 2 | 0 | 1 | 1 |
| 0 | 22.67 | 0.75 | 2 | 8 | 4 | 2 | 0 | 1 | 2 | 1 | 2 | 200 | 395 | 0 |
| 1 | 25.67 | 3.25 | 2 | 8 | 8 | 2.29 | 0 | 1 | 1 | 1 | 2 | 416 | 22 | 0 |
| 1 | 21.08 | 10.085 | 1 | 10 | 8 | 1.25 | 0 | 0 | 0 | 0 | 2 | 260 | 1 | 0 |
| 0 | 49 | 1.5 | 2 | 5 | 3 | 0 | 1 | 0 | 0 | 1 | 2 | 100 | 28 | 0 |
| 1 | 20.67 | 5.29 | 2 | 11 | 4 | 0.375 | 1 | 1 | 1 | 0 | 2 | 160 | 1 | 0 |
| 0 | 19.67 | 0.21 | 2 | 11 | 8 | 0.29 | 1 | 1 | 11 | 0 | 2 | 80 | 100 | 1 |
| 1 | 21.83 | 0.25 | 2 | 2 | 8 | 0.665 | 1 | 0 | 0 | 1 | 2 | 0 | 1 | 1 |
| 1 | 26.83 | 0.54 | 2 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 100 | 1 | 0 |
| 0 | 38.58 | 5 | 2 | 13 | 4 | 13.5 | 1 | 0 | 0 | 1 | 2 | 980 | 1 | 0 |
| 0 | 50.08 | 12.54 | 2 | 6 | 4 | 2.29 | 1 | 1 | 3 | 1 | 2 | 156 | 1 | 1 |
| 0 | 15.75 | 0.375 | 2 | 8 | 4 | 1 | 0 | 0 | 0 | 0 | 2 | 120 | 19 | 0 |
| 0 | 24.5 | 12.75 | 2 | 8 | 5 | 4.75 | 1 | 1 | 2 | 0 | 2 | 73 | 445 | 1 |
| 1 | 21 | 4.79 | 1 | 9 | 4 | 2.25 | 1 | 1 | 1 | 1 | 2 | 80 | 301 | 1 |
| 1 | 22.08 | 11 | 2 | 13 | 4 | 0.665 | 1 | 0 | 0 | 0 | 2 | 100 | 1 | 1 |
| 1 | 54.58 | 9.415 | 2 | 1 | 1 | 14.415 | 1 | 1 | 11 | 1 | 2 | 30 | 301 | 1 |
| 1 | 18.08 | 5.5 | 2 | 4 | 4 | 0.5 | 1 | 0 | 0 | 0 | 2 | 80 | 1 | 1 |
| 1 | 36.67 | 4.415 | 1 | 4 | 4 | 0.25 | 1 | 1 | 10 | 1 | 2 | 320 | 1 | 1 |
| 1 | 38.17 | 10.125 | 2 | 14 | 4 | 2.5 | 1 | 1 | 6 | 0 | 2 | 520 | 197 | 1 |
| 1 | 21.83 | 1.54 | 2 | 4 | 4 | 0.085 | 0 | 0 | 0 | 1 | 2 | 356 | 1 | 0 |
| 0 | 51.92 | 6.5 | 2 | 3 | 5 | 3.085 | 0 | 0 | 0 | 1 | 2 | 73 | 1 | 0 |
| 1 | 22.17 | 2.25 | 2 | 3 | 4 | 0.125 | 0 | 0 | 0 | 0 | 2 | 160 | 11 | 0 |
| 1 | 52.42 | 1.5 | 2 | 2 | 4 | 3.75 | 0 | 0 | 0 | 1 | 2 | 0 | 351 | 0 |
| 0 | 60.92 | 5 | 2 | 6 | 4 | 4 | 1 | 1 | 4 | 0 | 2 | 0 | 100 | 1 |
| 1 | 20.17 | 5.625 | 2 | 9 | 4 | 1.71 | 1 | 0 | 0 | 0 | 1 | 120 | 1 | 1 |
| 1 | 23.25 | 4 | 2 | 8 | 5 | 0.25 | 1 | 0 | 0 | 1 | 2 | 160 | 1 | 1 |
| 1 | 36.17 | 0.42 | 1 | 9 | 4 | 0.29 | 0 | 0 | 0 | 1 | 2 | 309 | 3 | 0 |
以下是对应上述缺失值处理方法的Python代码示例：
1. 常数填充
在这个修改后的代码中，constant_for_continuous是填充连续变量缺失值的常数（这里用0作为示例），而constant_for_discrete是填充离散变量缺失值的常数（这里用字符串'未知'作为示例）。您可以根据需要更改这些常数的值。
import pandas as pd
# 读取Excel文件
df = pd.read_excel("银行贷款审批数据.xlsx")
# 定义连续变量和离散变量列表
continuous_vars = ['x2', 'x3', 'x5', 'x6', 'x7', 'x10', 'x13', 'x14']
discrete_vars = ['x1', 'x4', 'x8', 'x9', 'x11', 'x12']
# 使用常数填充连续变量的缺失值（例如，使用常数0）
constant_for_continuous = 0
for var in continuous_vars:
df[var].fillna(constant_for_continuous, inplace=True)
# 使用常数填充离散变量的缺失值（例如，使用字符串'未知'）
constant_for_discrete = '未知'
for var in discrete_vars:
df[var].fillna(constant_for_discrete, inplace=True)
# 检查是否还有缺失值
missing_values = df.isnull().sum().sum()
if missing_values == 0:
print("所有缺失值已填充。")
else:
print("仍有缺失值未填充。")
# 输出填充后的数据框的前几行
print(df.head())
# 保存填充后的数据框到Excel文件
df.to_excel("填充后的银行贷款审批数据_常数填充.xlsx", index=False)
2. 均值/中位数/众数填充
import pandas as pd
from scipy import stats
# 读取Excel文件
df = pd.read_excel("银行贷款审批数据.xlsx")
# 定义连续变量和离散变量列表
continuous_vars = ['x2', 'x3', 'x5', 'x6', 'x7', 'x10', 'x13', 'x14']
discrete_vars = ['x1', 'x4', 'x8', 'x9', 'x11', 'x12']
# 使用均值填充连续变量的缺失值
for var in continuous_vars:
df[var].fillna(df[var].mean(), inplace=True)
# 或者使用中位数填充连续变量的缺失值
# for var in continuous_vars:
# df[var].fillna(df[var].median(), inplace=True)
# 使用众数填充离散变量的缺失值
for var in discrete_vars:
mode_value = stats.mode(df[var].dropna())[0][0]
df[var].fillna(mode_value, inplace=True)
# 检查是否还有缺失值
missing_values = df.isnull().sum().sum()
if missing_values == 0:
print("所有缺失值已填充。")
else:
print("仍有缺失值未填充。")
# 输出填充后的数据框的前几行
print(df.head())
# 保存填充后的数据框到Excel文件
df.to_excel("填充后的银行贷款审批数据_均值众数填充.xlsx", index=False)
3. 插值填充
import pandas as pd
# 读取Excel文件
df = pd.read_excel("银行贷款审批数据.xlsx")
# 定义连续变量和离散变量列表
continuous_vars = ['x2', 'x3', 'x5', 'x6', 'x7', 'x10', 'x13', 'x14']
discrete_vars = ['x1', 'x4', 'x8', 'x9', 'x11', 'x12']
# 使用线性插值填充连续变量的缺失值
for var in continuous_vars:
df[var].interpolate(method='linear', inplace=True)
# 对于离散变量，插值通常不是合适的方法，因为它们通常是分类数据。
# 因此，我们仍然使用众数来填充离散变量的缺失值。
for var in discrete_vars:
most_frequent_value = df[var].mode()[0]
df[var].fillna(most_frequent_value, inplace=True)
# 检查是否还有缺失值
missing_values = df.isnull().sum().sum()
if missing_values == 0:
print("所有缺失值已填充。")
else:
print("仍有缺失值未填充。")
# 输出填充后的数据框的前几行
print(df.head())
# 保存填充后的数据框到Excel文件
df.to_excel("填充后的银行贷款审批数据_插值填充.xlsx", index=False)
4. 基于模型预测填充
python复制代码
|---|--------------------------------------------------------------|
| | from sklearn.experimental import enable_iterative_imputer |
| | from sklearn.impute import IterativeImputer |
| | |
| | # 迭代式填充 |
| | imputer = IterativeImputer(max_iter=10, random_state=0) |
| | df_filled = imputer.fit_transform(df) |
| | df = pd.DataFrame(df_filled, columns=df.columns) |
5. 热卡填充（示例代码，需要自定义逻辑）
import pandas as pd
from sklearn.impute import KNNImputer
# 读取Excel文件
df = pd.read_excel("银行贷款审批数据.xlsx")
# 假设 continuous_vars 是包含连续变量的列名的列表
continuous_vars = ['x2', 'x3', 'x5', 'x6', 'x7', 'x10', 'x13', 'x14']
# 提取连续变量
df_continuous = df[continuous_vars]
# 使用KNNImputer填充缺失值
# 设定K值，这里以2为例，你可以根据数据集的特性调整这个值
knn_imputer = KNNImputer(n_neighbors=2)
df_continuous_filled = knn_imputer.fit_transform(df_continuous)
# 将填充后的数据转换回DataFrame格式
df_continuous_filled = pd.DataFrame(df_continuous_filled, columns=continuous_vars)
# 将填充后的连续变量重新合并到原始DataFrame中
df[continuous_vars] = df_continuous_filled
# 检查是否还有缺失值
missing_values = df[continuous_vars].isnull().sum().sum()
if missing_values == 0:
print("所有连续变量的缺失值已基于KNN填充。")
else:
print(f"仍有 {missing_values} 个连续变量的缺失值未填充。")
# 保存填充后的数据框到Excel文件
df.to_excel("填充后的银行贷款审批数据_KNN填充.xlsx", index=False)
6. 多重插补（使用 fancyimpute库）
import pandas as pd
from fancyimpute import IterativeImputer
# 读取Excel文件
df = pd.read_excel("银行贷款审批数据.xlsx")
# 初始化IterativeImputer
# max_iter设置迭代次数，可以设置较高的值以确保收敛
# min_value和max_value用于限制填充值的范围，可根据实际数据调整
imputer = IterativeImputer(max_iter=10, random_state=0, min_value=0, max_value=1) # 假设数据在0和1之间
# 填充缺失值
df_filled = imputer.fit_transform(df)
# 将填充后的数组转换回DataFrame
df_filled = pd.DataFrame(df_filled, columns=df.columns)
# 检查是否还有缺失值
missing_values = df_filled.isnull().sum().sum()
if missing_values == 0:
print("所有缺失值已基于多重插补填充。")
else:
print(f"仍有 {missing_values} 个缺失值未填充。")
# 保存填充后的数据框到Excel文件
df_filled.to_excel("填充后的银行贷款审批数据_多重插补.xlsx", index=False)
7. 基于相似性的填充（使用K近邻）
python复制代码
|---|----------------------------------------------------|
| | from sklearn.impute import KNNImputer |
| | |
| | # K近邻填充 |
| | imputer = KNNImputer(n_neighbors=2) |
| | df_filled = imputer.fit_transform(df) |
| | df = pd.DataFrame(df_filled, columns=df.columns) |
请注意，以上代码示例中的df['column_name']应替换为实际的列名，而df应替换为你的实际DataFrame。此外，某些方法可能需要额外的库，如scipy或fancyimpute，你可能需要先使用pip来安装它们。
每种填充方法都有其适用场景和局限性，选择哪种方法取决于数据的特性、缺失值的比例以及分析的目的。在实际应用中，可能需要尝试多种方法，并比较它们对分析结果的影响，以选择最合适的方法。