From 001e5da05a99616e7e9fe851639c6a2bc72f4e0a Mon Sep 17 00:00:00 2001 From: yanglbme Date: Wed, 27 Mar 2024 17:23:40 +0800 Subject: [PATCH] fix: update solutions to lc problem: No.3089 No.3089.Find Bursty Behavior --- .../3089.Find Bursty Behavior/README.md | 65 +++++++++++++------ .../3089.Find Bursty Behavior/README_EN.md | 46 +++++++------ .../3089.Find Bursty Behavior/Solution.py | 42 +++++++----- .../3089.Find Bursty Behavior/Solution.sql | 2 +- 4 files changed, 97 insertions(+), 58 deletions(-) diff --git a/solution/3000-3099/3089.Find Bursty Behavior/README.md b/solution/3000-3099/3089.Find Bursty Behavior/README.md index 13b5056c0ad9b..a32989e4f313d 100644 --- a/solution/3000-3099/3089.Find Bursty Behavior/README.md +++ b/solution/3000-3099/3089.Find Bursty Behavior/README.md @@ -79,7 +79,7 @@ Each row of this table contains post_id, user_id, and post_date. ### 方法一：自连接 + 分组统计 -我们可以使用自连接，将表 `Posts` 与自身连接，连接条件是 `p1.user_id = p2.user_id` 且 `p2.post_date` 在 `p1.post_date` 和 `p1.post_date` 后 `6` 天之间，然后我们将连接结果按照 `p1.user_id` 和 `p1.post_date` 分组，即可统计出每个用户在每天的 7 天内的发帖数量，我们将这个结果保存在表 `P` 中。 +我们可以使用自连接，将表 `Posts` 与自身连接，连接条件是 `p1.user_id = p2.user_id` 且 `p2.post_date` 在 `p1.post_date` 和 `p1.post_date` 后 `6` 天之间，然后我们将连接结果按照 `p1.user_id` 和 `p1.post_id` 分组，即可统计出每个用户在每天的 7 天内的发帖数量，我们将这个结果保存在表 `P` 中。 接着我们统计出每个用户在 2024 年 2 月份的每周平均发帖数量，保存在表 `T` 中。注意，我们需要查找 `post_date` 在 `2024-02-01` 和 `2024-02-28` 之间的记录，将记录按照 `user_id` 分组，然后统计每个用户的发帖数量，最后除以 `4` 即可得到每周平均发帖数量，我们将这个结果保存在表 `T` 中。 @@ -97,7 +97,7 @@ WITH JOIN Posts AS p2 ON p1.user_id = p2.user_id AND p2.post_date BETWEEN p1.post_date AND DATE_ADD(p1.post_date, INTERVAL 6 DAY) - GROUP BY p1.user_id, p1.post_date + GROUP BY p1.user_id, p1.post_id ), T AS ( SELECT user_id, COUNT(1) / 4 AS avg_weekly_posts @@ -117,26 +117,49 @@ ORDER BY 1; ```python import pandas as pd + def find_bursty_behavior(posts: pd.DataFrame) -> pd.DataFrame: - # 计算每个用户在7天窗口内发布的帖子数 - p = posts.merge(posts, on='user_id') - p = p[(p['post_date_y'] >= p['post_date_x']) & - (p['post_date_y'] <= p['post_date_x'] + pd.Timedelta(days=6))] - p_count = p.groupby(['user_id', 'post_date_x']).size().reset_index(name='cnt') - - # 计算每个用户在2024年2月期间的平均每周发布的帖子数 - t = posts[(posts['post_date'] >= '2024-02-01') & - (posts['post_date'] <= '2024-02-28')] - t_count = t.groupby('user_id').size().reset_index(name='count') - t_count['avg_weekly_posts'] = t_count['count'] / 4 - - # 合并两个计算出的表，并过滤符合条件的用户 - merged_df = p_count.merge(t_count, on='user_id') - merged_df = merged_df.groupby('user_id').agg(max_7day_posts=('cnt', 'max'), - avg_weekly_posts=('avg_weekly_posts', 'first')) - result_df = merged_df[merged_df['max_7day_posts'] >= merged_df['avg_weekly_posts'] * 2].reset_index() - - return result_df.sort_values('user_id') + # 子查询 P + p1 = pd.merge( + posts, posts, on="user_id", suffixes=("_1", "_2") + ) # 合并帖子表自身，根据用户ID + p1 = p1[ + p1["post_date_2"].between( + p1["post_date_1"], p1["post_date_1"] + pd.Timedelta(days=6) + ) + ] # 筛选出相邻 7 天内的帖子 + p1 = ( + p1.groupby(["user_id", "post_id_1"]).size().reset_index(name="cnt") + ) # 统计每个用户在相邻 7 天内的帖子数 + + # 子查询 T + t = posts[ + (posts["post_date"] >= "2024-02-01") & (posts["post_date"] <= "2024-02-28") + ] # 筛选出 2024 年 2 月份的帖子 + t = ( + t.groupby("user_id").size().div(4).reset_index(name="avg_weekly_posts") + ) # 计算每个用户平均每周的帖子数 + + # 连接 P 和 T + merged_df = pd.merge(p1, t, on="user_id", how="inner") # 内连接 P 和 T + + # 过滤 + filtered_df = merged_df[ + merged_df["cnt"] >= merged_df["avg_weekly_posts"] * 2 + ] # 过滤出满足条件的行 + + # 聚合 + result_df = ( + filtered_df.groupby("user_id") + .agg({"cnt": "max", "avg_weekly_posts": "first"}) + .reset_index() + ) # 对满足条件的行按用户ID聚合 + result_df.columns = ["user_id", "max_7day_posts", "avg_weekly_posts"] # 重命名列名 + + # 排序 + result_df.sort_values(by="user_id", inplace=True) # 按用户ID排序 + + return result_df ``` diff --git a/solution/3000-3099/3089.Find Bursty Behavior/README_EN.md b/solution/3000-3099/3089.Find Bursty Behavior/README_EN.md index 70b726649f29b..62a39d30dd22a 100644 --- a/solution/3000-3099/3089.Find Bursty Behavior/README_EN.md +++ b/solution/3000-3099/3089.Find Bursty Behavior/README_EN.md @@ -77,7 +77,7 @@ Each row of this table contains post_id, user_id, and post_date. ### Solution 1: Self-Join + Group Count -We can use self-join to connect the `Posts` table with itself. The connection condition is `p1.user_id = p2.user_id` and `p2.post_date` is between `p1.post_date` and 6 days after `p1.post_date`. Then we group the connection results by `p1.user_id` and `p1.post_date` to count the number of posts for each user within 7 days of each day. We save this result in table `P`. +We can use self-join to connect the `Posts` table with itself. The connection condition is `p1.user_id = p2.user_id` and `p2.post_date` is between `p1.post_date` and 6 days after `p1.post_date`. Then we group the connection results by `p1.user_id` and `p1.post_id` to count the number of posts for each user within 7 days of each day. We save this result in table `P`. Next, we count the average number of posts per week for each user in February 2024 and save it in table `T`. Note that we need to find records where `post_date` is between `2024-02-01` and `2024-02-28`, group the records by `user_id`, then count the number of posts for each user, and finally divide by `4` to get the average number of posts per week. We save this result in table `T`. @@ -95,7 +95,7 @@ WITH JOIN Posts AS p2 ON p1.user_id = p2.user_id AND p2.post_date BETWEEN p1.post_date AND DATE_ADD(p1.post_date, INTERVAL 6 DAY) - GROUP BY p1.user_id, p1.post_date + GROUP BY p1.user_id, p1.post_id ), T AS ( SELECT user_id, COUNT(1) / 4 AS avg_weekly_posts @@ -117,31 +117,39 @@ import pandas as pd def find_bursty_behavior(posts: pd.DataFrame) -> pd.DataFrame: - # Calculate the count of posts made by each user within a 7-day window - p = posts.merge(posts, on="user_id") - p = p[ - (p["post_date_y"] >= p["post_date_x"]) - & (p["post_date_y"] <= p["post_date_x"] + pd.Timedelta(days=6)) + # Subquery P + p1 = pd.merge(posts, posts, on="user_id", suffixes=("_1", "_2")) + p1 = p1[ + p1["post_date_2"].between( + p1["post_date_1"], p1["post_date_1"] + pd.Timedelta(days=6) + ) ] - p_count = p.groupby(["user_id", "post_date_x"]).size().reset_index(name="cnt") + p1 = p1.groupby(["user_id", "post_id_1"]).size().reset_index(name="cnt") - # Calculate the average weekly posts for each user in February 2024 + # Subquery T t = posts[ (posts["post_date"] >= "2024-02-01") & (posts["post_date"] <= "2024-02-28") ] - t_count = t.groupby("user_id").size().reset_index(name="count") - t_count["avg_weekly_posts"] = t_count["count"] / 4 + t = t.groupby("user_id").size().div(4).reset_index(name="avg_weekly_posts") - # Joining the two calculated tables and filtering users meeting the criteria - merged_df = p_count.merge(t_count, on="user_id") - merged_df = merged_df.groupby("user_id").agg( - max_7day_posts=("cnt", "max"), avg_weekly_posts=("avg_weekly_posts", "first") + # Joining P and T + merged_df = pd.merge(p1, t, on="user_id", how="inner") + + # Filtering + filtered_df = merged_df[merged_df["cnt"] >= merged_df["avg_weekly_posts"] * 2] + + # Aggregating + result_df = ( + filtered_df.groupby("user_id") + .agg({"cnt": "max", "avg_weekly_posts": "first"}) + .reset_index() ) - result_df = merged_df[ - merged_df["max_7day_posts"] >= merged_df["avg_weekly_posts"] * 2 - ].reset_index() + result_df.columns = ["user_id", "max_7day_posts", "avg_weekly_posts"] + + # Sorting + result_df.sort_values(by="user_id", inplace=True) - return result_df.sort_values("user_id") + return result_df ``` diff --git a/solution/3000-3099/3089.Find Bursty Behavior/Solution.py b/solution/3000-3099/3089.Find Bursty Behavior/Solution.py index 314c22ef7c97c..cf390bbfb1b52 100644 --- a/solution/3000-3099/3089.Find Bursty Behavior/Solution.py +++ b/solution/3000-3099/3089.Find Bursty Behavior/Solution.py @@ -2,28 +2,36 @@ def find_bursty_behavior(posts: pd.DataFrame) -> pd.DataFrame: - # Calculate the count of posts made by each user within a 7-day window - p = posts.merge(posts, on="user_id") - p = p[ - (p["post_date_y"] >= p["post_date_x"]) - & (p["post_date_y"] <= p["post_date_x"] + pd.Timedelta(days=6)) + # Subquery P + p1 = pd.merge(posts, posts, on="user_id", suffixes=("_1", "_2")) + p1 = p1[ + p1["post_date_2"].between( + p1["post_date_1"], p1["post_date_1"] + pd.Timedelta(days=6) + ) ] - p_count = p.groupby(["user_id", "post_date_x"]).size().reset_index(name="cnt") + p1 = p1.groupby(["user_id", "post_id_1"]).size().reset_index(name="cnt") - # Calculate the average weekly posts for each user in February 2024 + # Subquery T t = posts[ (posts["post_date"] >= "2024-02-01") & (posts["post_date"] <= "2024-02-28") ] - t_count = t.groupby("user_id").size().reset_index(name="count") - t_count["avg_weekly_posts"] = t_count["count"] / 4 + t = t.groupby("user_id").size().div(4).reset_index(name="avg_weekly_posts") - # Joining the two calculated tables and filtering users meeting the criteria - merged_df = p_count.merge(t_count, on="user_id") - merged_df = merged_df.groupby("user_id").agg( - max_7day_posts=("cnt", "max"), avg_weekly_posts=("avg_weekly_posts", "first") + # Joining P and T + merged_df = pd.merge(p1, t, on="user_id", how="inner") + + # Filtering + filtered_df = merged_df[merged_df["cnt"] >= merged_df["avg_weekly_posts"] * 2] + + # Aggregating + result_df = ( + filtered_df.groupby("user_id") + .agg({"cnt": "max", "avg_weekly_posts": "first"}) + .reset_index() ) - result_df = merged_df[ - merged_df["max_7day_posts"] >= merged_df["avg_weekly_posts"] * 2 - ].reset_index() + result_df.columns = ["user_id", "max_7day_posts", "avg_weekly_posts"] + + # Sorting + result_df.sort_values(by="user_id", inplace=True) - return result_df.sort_values("user_id") + return result_df diff --git a/solution/3000-3099/3089.Find Bursty Behavior/Solution.sql b/solution/3000-3099/3089.Find Bursty Behavior/Solution.sql index 23c5162f08d82..1004635f8e4c3 100644 --- a/solution/3000-3099/3089.Find Bursty Behavior/Solution.sql +++ b/solution/3000-3099/3089.Find Bursty Behavior/Solution.sql @@ -7,7 +7,7 @@ WITH JOIN Posts AS p2 ON p1.user_id = p2.user_id AND p2.post_date BETWEEN p1.post_date AND DATE_ADD(p1.post_date, INTERVAL 6 DAY) - GROUP BY p1.user_id, p1.post_date + GROUP BY p1.user_id, p1.post_id ), T AS ( SELECT user_id, COUNT(1) / 4 AS avg_weekly_posts