当需要根据回文计数对矩阵排序时，定义一个以列表作为参数的方法。它使用列表推导和“join”方法来迭代并查看元素是否为回文。基于此，确定并显示结果。示例下面是相同的演示def get_palindrome_count(row):    return len([element for element in row if''.join(list(reversed(element))) == element]) my_list = [["abcba", "hdgfue", "abc"], ["peep"], ["py", "is", "best"], ["sees", "level", "non", "noon"]] print("The list is :") print(my_list) my_list.sort(key=get_palindrome_count) print("The resultant list is :") print(my_list)输出The list is : [['abcba', 'hdgfue', 'abc'], ... 阅读更多

当需要提取具有复杂数据类型的行时，使用“isinstance”方法和列表推导。示例下面是相同的演示my_list = [[13, 1, 35], [23, [44, 54], 85], [66], [75, (81, 2), 29, 7]] my_result = [row for row in my_list if any(isinstance(element, list) or isinstance(element, tuple) or isinstance(element, dict) or isinstance(element, set) for element in row)] print("The list is :") print(my_list) print("The resultant list is :") print(my_result)输出The list is : [[13, 1, 35], [23, [44, 54], 85], [66], [75, (81, 2), 29, 7]] The resultant list is : [[23, [44, 54], ... 阅读更多

当需要从单词列表中返回最长单词的长度时，定义一个以列表作为参数的方法。它检查元素是否在列表中，并根据此显示输出。示例下面是相同的演示def find_longest_length(my_list):    max_length = len(my_list[0])    temp = my_list[0]    for element in my_list:       if(len(element) > max_length):          max_length = len(element)          temp = element return max_length my_list = ["ab", "abc", "abcd", "abcde"] print("The list ... 阅读更多

示例下面是相同的演示def diff_summation_elem(row): return sum([abs(row[index + 1] - row[index]) for index in range(0, len(row) - 1)]) my_list = [[97, 6, 47, 3], [6, 88, 3, 26], [71, 53, 34, 65], [15, 36, 5, 62]] print("The list is : ") print(my_list) my_list.sort(key=diff_summation_elem) print("The resultant list is :" ) print(my_list)输出The list is : [[97, 6, 47, 3], [6, 88, 3, 26], [71, 53, 34, 65], [15, 36, 5, 62]] The resultant list is : [[71, 53, 34, 65], [15, 36, 5, 62], [97, 6, 47, 3], [6, 88, 3, 26]]解释一个 ... 阅读更多

当需要将给定数字追加到列表的每个元素时，使用列表推导。示例下面是相同的演示my_list = [25, 36, 75, 36, 17, 7, 8, 0] print ("The list is :") print(my_list) my_key = 6 my_result = [x + my_key for x in my_list] print ("The resultant list is :") print(my_result)输出The list is : [25, 36, 75, 36, 17, 7, 8, 0] The resultant list is : [31, 42, 81, 42, 23, 13, 14, 6]解释定义一个列表并在控制台上显示。一个整数键值 ... 阅读更多

当需要获取列表中的累积行频率时，使用“Counter”方法和列表推导。示例下面是相同的演示from collections import Counter my_list = [[11, 2, 32, 4, 31], [52, 52, 3, 71, 71, 3], [1, 3], [19, 19, 40, 40, 40]] print("The list is :") print(my_list) my_element_list = [19, 2, 71] my_frequency = [Counter(element) for element in my_list] my_result = [sum([freq[word] for word in my_element_list if word in freq]) for freq in my_frequency] print("The resultant matrix is :") print(my_result)输出The list is : [[11, 2, ... 阅读更多

当需要获取构造字符串的最小元素时，需要“set”运算符、“combinations”方法、“issubset”方法和简单的迭代。示例下面是相同的演示from itertools import combinations my_list = ["python", "is", "fun", "to", "learn"] print("The list is :") print(my_list) my_target_str = "onis" my_result = -1 my_set_string = set(my_target_str) complete_val = False for value in range(0, len(my_list) + 1): for sub in combinations(my_list, value): temp_set = set(ele for subl in sub for ele in subl) ... 阅读更多

索引运算符是用于创建子集 DataFrame 的方括号。让我们首先创建一个 Pandas DataFrame。DataFrame 中有 3 列dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]})使用单列创建子集dataFrame[['Product']]使用多列创建子集dataFrame[['Opening_Stock', 'Closing_Stock']]示例以下是完整代码import pandas as pd dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]}) print"DataFrame...", dataFrame print"Displaying a subset using indexing operator:", dataFrame[['Product']] print"Displaying a subset with multiple columns:", dataFrame[['Opening_Stock', 'Closing_Stock']]输出这将 ... 阅读更多

可以使用 numpy where() 方法过滤 Pandas DataFrame。在 where() 方法中提及条件。首先，让我们导入所需的库及其各自的别名import pandas as pd import numpy as np我们现在将创建一个包含产品记录的 Pandas DataFrame dataFrame = pd.DataFrame({"Product": ["SmartTV", "ChromeCast", "Speaker", "Earphone"], "Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]})使用 numpy where() 使用 2 个条件过滤 DataFrameresValues1 = np.where((dataFrame['Opening_Stock']>=700) & (dataFrame['Closing_Stock']< 1000)) print"Filtered DataFrame Value = ", dataFrame.loc[resValues1] 让我们再次使用 numpy where() 使用 3 个条件过滤 DataFramersesValues2 = np.where((dataFrame['Opening_Stock']>=500) & (dataFrame['Closing_Stock']< 1000) ... 阅读更多

要对DataFrame的所有行求和，可以使用`sum()`函数并将`axis`值设置为1。`axis=1` 将对行值进行求和。首先，让我们创建一个DataFrame。它包含“期初库存”和“期末库存”两列：dataFrame = pd.DataFrame({"Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]}) 求行值之和。将`axis`设置为1以对行值进行求和：dataFrame = dataFrame.sum(axis = 1) 示例：以下是完整的代码：import pandas as pd dataFrame = pd.DataFrame({"Opening_Stock": [300, 700, 1200, 1500], "Closing_Stock": [200, 500, 1000, 900]}) print("DataFrame...", dataFrame) # 求和... 阅读更多