from collections import Counter def detect_outliers(df, n, features): outlier_indices = [] # iterate over features(columns) for col in features: # 1st quartile (25%) Q1 = np.percentile(df[col], 25) # 3rd quartile (75%) Q3 = np.percentile(df[col], 75) # Interquartile range (IQR) IQR = Q3 - Q1 # outlier step outlier_step = 1.5 * IQR # Determine a list of indices of outliers for feature col outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index # append the found outlier indices for col to the list of outlier indices outlier_indices.extend(outlier_list_col) # select observations containing more than 2 outliers outlier_indices = Counter(outlier_indices) multiple_outliers = list(k for k, v in outlier_indices.items() if v > n) return multiple_outliers Outliers_to_drop = detect_outliers(data, 0, list(data.columns)[-2:]) data = data.drop(Outliers_to_drop, axis=0).reset_index(drop=True) data

这段代码的作用是从Python的collections库中导入Counter，然后定义一个名为detect_outliers的函数，该函数需要传入三个参数：df、n和features。这个函数的作用是检测DataFrame中指定特征的离群值，并将它们的索引存储在outlier_indices列表中。

相关问题

这段代码运用了什么模型from collections import Counter all_outliers=[] numeric_features = train.dtypes[train.dtypes != 'object'].index for feature in numeric_features: try: outs = detect_outliers(train[feature], train['SalePrice'],top=5, plot=False) except: continue all_outliers.extend(outs)

这段代码并没有运用具体的机器学习模型，它主要是使用了Python中的一些数据结构和函数来处理数据。

首先，引入了collections模块中的Counter类，以便后续统计异常值的数量。

接着，使用了train.dtypes[train.dtypes != 'object'].index获取数据集中所有数值类型的特征，并将其保存到numeric_features变量中。

然后，使用了一个for循环遍历numeric_features中的每一个特征，并调用上一个问题中定义的detect_outliers()函数来检测该特征中的异常值。将检测出来的所有异常值的索引保存在了all_outliers变量中。

最后，使用了extend()函数将每个特征中检测出来的异常值索引合并到了一起，并使用Counter()函数统计了所有异常值的数量。

import collections def solve_method(n: int) -> None: asc = True list_ = collections.deque() x = 1 for i in range(1, n + 1): arr = [0] * i if asc: for j in range(i): arr[j] = x x += 1 else: for j in range(i - 1, -1, -1): arr[j] = x x += 1 list_.appendleft(arr) asc = not asc res = [] head = "" for ints in list_: content = head for j in range(len(ints)): num = ints[j] content += str(num) content += "*" * (4 - len(str(num))) if j != len(ints) - 1: content += " " res.append(content) head += " " for s in reversed(res): print(s) if __name__ == "__main__": n = int(input()) solve_method(n) 改写以上代码，python

import collections

def solve_method(n):
asc = True
list_ = collections.deque()
x = 1
for i in range(1, n + 1):
arr = [0] * i
if asc:
for j in range(i):
arr[j] = x
x += 1
else:
for j in range(i - 1, -1, -1):
arr[j] = x
x += 1
list_.appendleft(arr)
asc = not asc

res = []
head = ""
for ints in list_:
content = head
for j in range(len(ints)):
num = ints[j]
content += str(num)
content += "*" * (4 - len(str(num)))
if j != len(ints) - 1:
content += " "
res.append(content)
head += " "

for s in reversed(res):
print(s)

if __name__ == "__main__":
n = int(input())
solve_method(n)