if i not in res: res.append(i) # printing list after removal print ("The list after removing duplicates : " + str(res)) → 输出结果: The original list is : [1, 3, 5, 6, 3, 5, 6, 1]The list after removing duplicates : [...
# initializing listtest_list = [1,3,5,6,3,5,6,1]print("The original list is : "+ str(test_list)) # using naive method to remove duplicated from listres = []foriintest_list:ifinotinres:res.append(i) # printing list after ...
利用pandas库的drop_duplicates()方法去除DataFrame中的重复行 drop_duplicates()方法可以帮助我们去除DataFrame中重复的行，并返回一个新的DataFrame。示例代码：import pandas as pdmy_data = {'col1': [1, 2, 2, 3, 4, 4, 5], 'col2': ['a', 'b', 'b', 'c', 'd', 'd', 'e']}df = ...
new_list = remove_duplicates(my_list)print(new_list) 代码解析: 首先定义了一个名为remove_duplicates的函数,该函数接受一个列表作为参数,并使用列表推导式生成一个新列表。 列表推导式的语法为[expression for item in iterable if condition]。在这里,我们使用了if x not in lst[:i]来过滤掉重复的元素。
print(unique_list)# 输出: [1, 2, 3, 4, 5] 执行以上代码输出结果为: [1,2,3,4,5] 使用列表推导式 列表推导式结合条件判断也可以实现去重功能。 实例 # 使用列表推导式去重 defremove_duplicates(lst): unique_list=[] [unique_list.append(item)foriteminlstifitemnotinunique_list] ...
可以使用Python的set()函数和列表推导式来找出列表中重复的值。具体步骤如下:1. 定义一个空集合(set)和一个空列表(duplicates)来存储重复的值。2. 使用列表推导式,遍历列...
if i not in temp_list: temp_list.append(i) my_list = temp_list print("List After removing duplicates ", my_list) 输出: List Before [1, 2, 3, 1, 2, 4, 5, 4, 6, 2] List After removing duplicates [1, 2, 3, 4, 5, 6] ...
Since dictionaries can't have duplicate keys, this also de-duplicates the given items! Dictionaries also maintain the order of their items (as of Python 3.6), so the resulting dictionary will have its keys ordered based on the first time each value was seen. ...
def find_duplicates(lst): duplicates = [] for item in lst: if lst.count(item) > 1 and item not in duplicates: duplicates.append(item) return duplicates # 示例用法 my_list = [1, 2, 3, 4, 2, 3, 5] print(find_duplicates(my_list)) # 输出: [2, 3] 复制代码...
Pandas的drop_duplicates方法不仅可以按单列去重,还可以按多列去重,并且能保留原始数据的索引和顺序(通过keep参数控制)。 4. 使用NumPy库 NumPy是Python中用于科学计算的基础库,虽然它本身不直接提供去重功能,但可以通过一些技巧实现。 示例代码: import numpy as np original_array = np.array([1, 2, 2, 3, ...