事务所网站制作方案,网页制作背景图片,上海有哪些优化网站推广公司,专业网站推广软件很久没有写AI或者Python相关的代码#xff0c;毕竟现在已经不是一个算法工程师了。所以就用白嫖版的leetcode练练手。
先丢个代码#xff0c;再慢慢填坑把
import pandas as pd# 1.大的国家#xff0c;选出面基至少为300万或者人口至少为2500万的国家
# 简单的pandas过滤
…很久没有写AI或者Python相关的代码毕竟现在已经不是一个算法工程师了。所以就用白嫖版的leetcode练练手。
先丢个代码再慢慢填坑把
import pandas as pd# 1.大的国家选出面基至少为300万或者人口至少为2500万的国家
# 简单的pandas过滤
def big_countries(world: pd.DataFrame) - pd.DataFrame:df world[(world[area] 3000000) | (world[population] 25000000)]return df[[name, population, area]]# 1757 可回收且低脂的产品过滤出可回收且低脂
def find_products(products: pd.DataFrame) - pd.DataFrame:df products[(products[low_fats] Y) (products[recyclable] Y)]return df[[product_id]]# 183 从不订购的客户考察且或非中的非用~表示而不是用Not表示
def find_customers(customers: pd.DataFrame, orders: pd.DataFrame) - pd.DataFrame:df customers[~customers[id].isin(orders[customerId])]df df[[name]].rename(columns{name: Customers})return df# 1148 文章浏览1 考察的不同列相等
def article_views(views: pd.DataFrame) - pd.DataFrame:df views[views[author_id] views[viewer_id]][[author_id]]df df.rename(columns{author_id: id})df df.drop_duplicates()df df.sort_values(byid)return df# 1683. 无效的推文对比不同列的值进行过滤
def article_views(views: pd.DataFrame) - pd.DataFrame:views views[views[author_id] views[viewer_id]][author_id]views views.rename({author_id: id})views.drop_duplicates(inplaceTrue)views views.sort_values(byid)return views[id]# 1683. 无效的推文列转str使用str的方法进行过滤
def invalid_tweets(tweets: pd.DataFrame) - pd.DataFrame:return tweets[tweets[content].str.len() 15][[tweet_id]]# 1873. 计算特殊奖金, 通过apply的方式进行复杂的过滤
def calculate_special_bonus(employees: pd.DataFrame) - pd.DataFrame:employees[bonus] employees.apply(lambda x: x[salary] if x[employee_id] % 2 and not x[name].startswith(M) else 0,axis1)df employees[[employee_id, bonus]].sort_values(employee_id)return df# 1667. 修复表中的名字 这边注意使用apply的话本身就是str不用强制转
def fix_names(users: pd.DataFrame) - pd.DataFrame:users[name] users.apply(lambda x: x[name][0].upper() x[name][1:].lower(),axis1)return users.sort_values(user_id)# 1517. 查找拥有有效邮箱的用户 正则表达式的应用
def valid_emails(users: pd.DataFrame) - pd.DataFrame:return users[users[mail].str.match(r^[a-zA-Z][a-zA-Z0-9_.-]*\leetcode\.com$)]# 1527. 患某种疾病的患者 字符串contains正则表达
def find_patients(patients: pd.DataFrame) - pd.DataFrame:return patients[patients[conditions].str.contains(r\bDIAB1)]# 177. 第N高的薪水
def nth_highest_salary(employee: pd.DataFrame, N: int) - pd.DataFrame:df employee[[salary]].drop_duplicates()key getNthHighestSalary( str(N) )if len(df) N:#这边不能直接返回Nonereturn pd.DataFrame({key: [None]})# 这边类似数据库的count N limit 1df df.sort_values(bysalary, ascendingFalse).head(N).tail(1)return df.rename(columns{salary: key})# 176. 第二高的薪水
def second_highest_salary(employee: pd.DataFrame) - pd.DataFrame:df employee[[salary]].drop_duplicates()if len(df) 1:return pd.DataFrame({SecondHighestSalary: None})df df.sort_values(bysalary, ascendingFalse).head(2).tail(1)# 这边就换种写法把不用rename了return pd.DataFrame({SecondHighestSalary: df[salary][0]})def department_highest_salary(employee: pd.DataFrame, department: pd.DataFrame) - pd.DataFrame:# 类似sql的left join on a.departmentId b.iddf employee.merge(department, left_ondepartmentId, right_onid, howleft)df.rename(columns{name_x: Employee, name_y: Department, salary: Salary}, inplaceTrue)# 先找出最高工资groupby分组, transform是执行某个函数可以用lambda自己定义也可以输入约定速成的几个max_salary df.groupby(Department)[Salary].transform(max)# 过滤工资等于最高工资的员工df df[df[Salary] max_salary]return df[[Department, Employee, Salary]]# 178. 分数排名
def order_scores(scores: pd.DataFrame) - pd.DataFrame:# 类似sql的 dense_rank over (partition score order by score desk)scores[rank] scores[score].rank(methoddense, ascendingFalse)return scores[[score, rank]].sort_values(score, ascendingFalse)# 196. 删除重复的电子邮箱
def delete_duplicate_emails(person: pd.DataFrame) - None:person.sort_values(id, inplaceTrue)person.drop_duplicates([email], keepfirst, inplaceTrue)return# 1795. 每个产品在不同商店的价格
def rearrange_products_table(products: pd.DataFrame) - pd.DataFrame:df products.set_index([product_id]).stack(dropnaTrue)df df.reset_index()df.columns [product_id, store, price]return df# 1907. 按分类统计薪水
def count_salary_categories(accounts: pd.DataFrame) - pd.DataFrame:low (accounts[income] 20000).sum()avg ((accounts[income] 50000) (accounts[income] 20000)).sum()high (accounts[income] 50000).sum()return pd.DataFrame({category: [Low Salary, Average Salary, High Salary],accounts_count: [low, avg, high]})# 1741. 查找每个员工花费的总时间
def total_time(employees: pd.DataFrame) - pd.DataFrame:df employees.groupby(by[event_day, emp_id]).sum().reset_index().rename(columns{event_day: day})df[total_time] df[out_time] - df[in_time]df.drop(columns[in_time, out_time], inplaceTrue)return df.sort_values(byemp_id)# 511. 游戏玩法分析 I
def game_analysis(activity: pd.DataFrame) - pd.DataFrame:df activity.sort_values(by[player_id, event_date])[[player_id, event_date]].rename(columns{event_date: first_login})print(df)return df.drop_duplicates(player_id)# 2356. 每位教师所教授的科目种类的数量
def count_unique_subjects(teacher: pd.DataFrame) - pd.DataFrame:df teacher.drop_duplicates([teacher_id, subject_id])df[cnt] df.groupby(teacher_id)[subject_id].transform(count)return df[[teacher_id, cnt]].drop_duplicates([teacher_id, cnt])# 596. 超过5名学生的课
def find_classes(courses: pd.DataFrame) - pd.DataFrame:courses[cnt] courses.groupby(class)[student].transform(count)df courses[courses[cnt] 5].drop(student, axis1)[[class]]return df.drop_duplicates()# 586. 订单最多的客户
def largest_orders(orders: pd.DataFrame) - pd.DataFrame:if orders.empty:return pd.DataFrame({customer_number: []})df orders.groupby(customer_number).size().reset_index(namecount)df.sort_values(bycount, ascendingFalse, inplaceTrue)return df[[customer_number]][:1]# 1484. 按日期分组销售产品
def categorize_products(activities: pd.DataFrame) - pd.DataFrame:groups activities.groupby(sell_date)stats groups.agg(num_sold(product, nunique),products(product, lambda x: ,.join(sorted(set(x))))).reset_index()stats.sort_values(sell_date, inplaceTrue)return stats# 1693. 每天的领导和合伙人
def daily_leads_and_partners(daily_sales: pd.DataFrame) - pd.DataFrame:df daily_sales.groupby([date_id, make_name]).nunique().reset_index()df df.rename(columns{lead_id:unique_leads, partner_id: unique_partners})return df# 1050. 合作过至少三次的演员和导演
def actors_and_directors(actor_director: pd.DataFrame) - pd.DataFrame:df actor_director.groupby([actor_id, director_id]).size().reset_index(namecnt)df df[df[cnt] 3]return df[[actor_id, director_id]]# 1378. 使用唯一标识码替换员工ID
def replace_employee_id(employees: pd.DataFrame, employee_uni: pd.DataFrame) - pd.DataFrame:df employees.merge(employee_uni, howleft, left_onid, right_onid)return df[[unique_id, name]]# 1280. 学生们参加各科测试的次数
def students_and_examinations(students: pd.DataFrame, subjects: pd.DataFrame,examinations: pd.DataFrame) - pd.DataFrame:# 按 id 和科目分组并计算考试次数。grouped examinations.groupby([student_id, subject_name]).size().reset_index(nameattended_exams)all_id_subjects pd.merge(students, subjects, howcross)id_subjects_count pd.merge(all_id_subjects, grouped, on[student_id, subject_name], howleft)id_subjects_count[attended_exams] id_subjects_count[attended_exams].fillna(0).astype(int)id_subjects_count.sort_values([student_id, subject_name], inplaceTrue)return id_subjects_count[[student_id, student_name, subject_name, attended_exams]]# 570. 至少有5名直接下属的经理
def find_managers(employee: pd.DataFrame) - pd.DataFrame:df employee.groupby([managerId]).size().reset_index(namecnt)df df[df[cnt] 5]df df.merge(employee, howinner, right_onid, left_onmanagerId)return df[[name]]# 607. 销售员
def sales_person(sales_person: pd.DataFrame, company: pd.DataFrame, orders: pd.DataFrame) - pd.DataFrame:company company[company[name] RED]df orders.merge(company, howinner, oncom_id)sales_person sales_person[~sales_person[sales_id].isin(df[sales_id])]return sales_person[[name]]
