解释下这段代码def remove_outliers(data): li = list(data['ACTIVE_ACCTS'].sort_values()[-3:].index) data['ACTIVE_ACCTS'][li] = int(data.drop(li)['ACTIVE_ACCTS'].mode()) li = list(data['NO_OF_ACCTS'].sort_values()[-4:].index) data['NO_OF_ACCTS'][li] = int(data.drop(li)['NO_OF_ACCTS'].mode()) li = list(data['OVERDUE_ACCTS'].sort_values()[-10:].index) data['OVERDUE_ACCTS'][li] = int(data.drop(li)['OVERDUE_ACCTS'].mode()) li = list(data['CURRENT_BALANCE'].sort_values()[-15:].index) data['CURRENT_BALANCE'][li] = int(data.drop(li)['CURRENT_BALANCE'].mode())
时间: 2023-06-05 12:06:28 浏览: 46
这是一个删除异常值的函数,它的作用是筛选出数据中一些极端值,然后将这些值替换为数据的众数。具体来说,函数会分别找出四个指标(ACTIVE_ACCTS, NO_OF_ACCTS, OVERDUE_ACCTS, CURRENT_BALANCE)中的极端值,然后将这些值替换为该指标在去掉这些极端值后的众数。
相关问题
for fea in numerical_fea: data_train = data_train[data_train[fea+'_outliers']=='正常值'] data_train = data_train.reset_index(drop=True)
This code snippet loops over the numerical features in a dataset and filters out any rows where the feature's outlier status is labeled as "正常值" (which likely means "normal value" in Chinese). The filtered dataset is then reset so that the row indices are sequential.
It's possible that the dataset has been preprocessed prior to this code being run, with a separate step that identifies and labels outliers for each numerical feature. Without knowing more about the dataset and the preprocessing steps, it's hard to say exactly what this code is doing or what the purpose of filtering out outliers might be.
remove_outliers(points, k=8, std_ratio=2.0)参数分别为什么意思,怎么用
在 `remove_outliers` 函数中,有三个参数:
1. `points`:表示输入的点云数据,它是一个 Numpy 数组,形状为 (N, D),其中 N 是点的数量,D 是每个点的维度。
2. `k`:表示每个点的最近邻数量。默认值为 8,意味着每个点将与其最近的 8 个点进行距离计算。
3. `std_ratio`:表示离群点的阈值,以标准差为单位。默认值为 2.0,意味着距离超过平均距离加上两倍标准差的点将被认为是离群点。
使用该函数的步骤如下:
1. 准备点云数据,确保它是一个 Numpy 数组,形状为 (N, D),其中 N 是点的数量,D 是每个点的维度。
2. 调用 `remove_outliers` 函数,并将点云数据作为第一个参数传入。你可以使用默认参数值,也可以根据需要自定义参数值。
3. 函数将返回过滤后的点云数据,也是一个 Numpy 数组。
以下是一个使用示例:
```python
import numpy as np
from scipy.spatial import cKDTree
def remove_outliers(points, k=8, std_ratio=2.0):
tree = cKDTree(points)
distances, _ = tree.query(points, k=k)
mean_dist = np.mean(distances, axis=1)
std_dist = np.std(distances, axis=1)
threshold = mean_dist + std_ratio * std_dist
mask = np.all(distances < threshold[:, np.newaxis], axis=1)
filtered_points = points[mask]
return filtered_points
# 示例使用:
points = np.random.rand(100, 3) # 假设有一个100个点的点云,每个点有3个坐标
filtered_points = remove_outliers(points, k=8, std_ratio=2.0)
print("原始点云大小:", points.shape)
print("去除离群点后的点云大小:", filtered_points.shape)
```
在示例中,我们生成了一个包含 100 个点的随机点云,每个点有 3 个坐标。然后,我们调用 `remove_outliers` 函数,并传入点云数据和自定义的参数值。最后,打印出原始点云和去除离群点后的点云的大小。