chore: update lc problems

.github/workflows/sync.yml

@@ -0,0 +1,26 @@
+name: Sync
+
+on:
+  push:
+    branches: [ main ]
+
+jobs:
+  sync:
+    runs-on: ubuntu-latest
+    if: github.repository == 'doocs/leetcode'
+    steps:
+    - name: Sync to gitee.com
+      uses: wearerequired/git-mirror-action@master
+      env:
+          SSH_PRIVATE_KEY: ${{ secrets.RSA_PRIVATE_KEY }}
+      with:
+          source-repo: git@github.com:doocs/leetcode.git
+          destination-repo: git@gitee.com:Doocs/leetcode.git
+
+    - name: Build Gitee Pages
+      uses: yanglbme/gitee-pages-action@main
+      with:
+          gitee-username: yanglbme
+          gitee-password: ${{ secrets.GITEE_PASSWORD }}
+          gitee-repo: doocs/leetcode
+          branch: main

solution/2800-2899/2877.Create a DataFrame from List/README.md

@@ -1,30 +1,31 @@
-# [2877. Create a DataFrame from List](https://leetcode.cn/problems/create-a-dataframe-from-list)
+# [2877. 从表中创建 DataFrame](https://leetcode.cn/problems/create-a-dataframe-from-list)
 
 [English Version](/solution/2800-2899/2877.Create%20a%20DataFrame%20from%20List/README_EN.md)
 
 ## 题目描述
 
 <!-- 这里写题目描述 -->
 
-<p>Write a solution to <strong>create</strong> a DataFrame from a 2D list called <code>student_data</code>. This 2D list contains the IDs and ages of some students.</p>
+<p>编写一个解决方案，从名为 &nbsp;<code>student_data</code>&nbsp;的二维列表&nbsp;<b>创建 </b>一个 DataFrame 。这个二维列表包含一些学生的 ID 和年龄信息。</p>
 
-<p>The DataFrame should have two columns, <code>student_id</code> and <code>age</code>, and be in the same order as the original 2D list.</p>
+<p>DataFrame 应该有两列，&nbsp;<code>student_id</code>&nbsp;和&nbsp;<code>age</code>，并且与原始二维列表的顺序相同。</p>
 
-<p>The result format is in the following example.</p>
+<p>返回结果格式如下示例所示。</p>
 
 <p>&nbsp;</p>
-<p><strong class="example">Example 1:</strong></p>
+
+<p><strong class="example">示例 1：</strong></p>
 
 <pre>
-<strong>Input:
+<strong>输入：
 </strong>student_data:<strong>
 </strong><code>[
   [1, 15],
   [2, 11],
   [3, 11],
   [4, 20]
 ]</code>
-<strong>Output:</strong>
+<b>输出：</b>
 +------------+-----+
 | student_id | age |
 +------------+-----+
@@ -33,8 +34,8 @@
 | 3          | 11  |
 | 4          | 20  |
 +------------+-----+
-<strong>Explanation:</strong>
-A DataFrame was created on top of student_data, with two columns named <code>student_id</code> and <code>age</code>.
+<b>解释：</b>
+在 student_data 上创建了一个 DataFrame，包含 student_id 和 age 两列。
 </pre>
 
 ## 解法

solution/2800-2899/2881.Create a New Column/README.md

@@ -1,4 +1,4 @@
-# [2881. Create a New Column](https://leetcode.cn/problems/create-a-new-column)
+# [2881. 创建新列](https://leetcode.cn/problems/create-a-new-column)
 
 [English Version](/solution/2800-2899/2881.Create%20a%20New%20Column/README_EN.md)
 
@@ -16,17 +16,18 @@ DataFrame <code>employees</code>
 +-------------+--------+
 </pre>
 
-<p>A&nbsp;company plans to provide its employees with a bonus.</p>
+<p>一家公司计划为员工提供奖金。</p>
 
-<p>Write a solution to create a new column name <code>bonus</code> that contains the <strong>doubled values</strong> of the <code>salary</code> column.</p>
+<p>编写一个解决方案，创建一个名为&nbsp;<code>bonus</code>&nbsp;的新列，其中包含&nbsp;<code>salary</code>&nbsp;值的&nbsp;<strong>两倍</strong>。</p>
 
-<p>The result format is in the following example.</p>
+<p>返回结果格式如下示例所示。</p>
 
 <p>&nbsp;</p>
-<p><strong class="example">Example 1:</strong></p>
+
+<p><b>示例 1:</b></p>
 
 <pre>
-<strong>Input:</strong>
+<b>输入：</b>
 DataFrame employees
 +---------+--------+
 | name    | salary |
@@ -38,7 +39,7 @@ DataFrame employees
 | Finn    | 74576  |
 | Thomas  | 24433  |
 +---------+--------+
-<strong>Output:</strong>
+<b>输出：</b>
 +---------+--------+--------+
 | name    | salary | bonus  |
 +---------+--------+--------+
@@ -49,8 +50,8 @@ DataFrame employees
 | Finn    | 74576  | 149152 |
 | Thomas  | 24433  | 48866  |
 +---------+--------+--------+
-<strong>Explanation:</strong> 
-A new column bonus is created by doubling the value in the column salary.</pre>
+<b>解释：</b>
+通过将salary列中的值加倍创建了一个新的bonus列。</pre>
 
 ## 解法
 

solution/2800-2899/2882.Drop Duplicate Rows/README.md

@@ -1,4 +1,4 @@
-# [2882. Drop Duplicate Rows](https://leetcode.cn/problems/drop-duplicate-rows)
+# [2882. 删去重复的行](https://leetcode.cn/problems/drop-duplicate-rows)
 
 [English Version](/solution/2800-2899/2882.Drop%20Duplicate%20Rows/README_EN.md)
 
@@ -17,16 +17,18 @@ DataFrame customers
 +-------------+--------+
 </pre>
 
-<p>There are some duplicate rows in the DataFrame based on the <code>email</code> column.</p>
+<p>在 DataFrame 中基于&nbsp;<code>email</code>&nbsp;列存在一些重复行。</p>
 
-<p>Write a solution to remove these duplicate rows and keep only the <strong>first</strong> occurrence.</p>
+<p>编写一个解决方案，删除这些重复行，仅保留第一次出现的行。</p>
 
-<p>The result format is in the following example.</p>
+<p>返回结果格式如下例所示。</p>
 
 <p>&nbsp;</p>
+
+<p><strong>示例 1:</strong></p>
+
 <pre>
-<strong class="example">Example 1:</strong>
-<strong>Input:</strong>
+<b>输入：</b>
 +-------------+---------+---------------------+
 | customer_id | name    | email               |
 +-------------+---------+---------------------+
@@ -37,7 +39,7 @@ DataFrame customers
 | 5           | Finn    | john@example.com    |
 | 6           | Violet  | alice@example.com   |
 +-------------+---------+---------------------+
-<strong>Output: </strong> 
+<b>输出：</b>
 +-------------+---------+---------------------+
 | customer_id | name    | email               |
 +-------------+---------+---------------------+
@@ -47,8 +49,8 @@ DataFrame customers
 | 4           | Alice   | john@example.com    |
 | 6           | Violet  | alice@example.com   |
 +-------------+---------+---------------------+
-<strong>Explanation:</strong>
-Alic (customer_id = 4) and Finn (customer_id = 5) both use john@example.com, so only the first occurrence of this email is retained.
+<b>解释：</b>
+Alice (customer_id = 4) 和 Finn (customer_id = 5) 都使用 john@example.com，因此只保留该邮箱地址的第一次出现。
 </pre>
 
 ## 解法

solution/2800-2899/2883.Drop Missing Data/README.md

@@ -1,4 +1,4 @@
-# [2883. Drop Missing Data](https://leetcode.cn/problems/drop-missing-data)
+# [2883. 删去丢失的数据](https://leetcode.cn/problems/drop-missing-data)
 
 [English Version](/solution/2800-2899/2883.Drop%20Missing%20Data/README_EN.md)
 
@@ -17,17 +17,18 @@ DataFrame students
 +-------------+--------+
 </pre>
 
-<p>There are some rows having missing values in the <code>name</code> column.</p>
+<p>在 <code>name</code> 列里有一些具有缺失值的行。</p>
 
-<p>Write a solution to remove the rows with missing values.</p>
+<p>编写一个解决方案，删除具有缺失值的行。</p>
 
-<p>The result format is in the following example.</p>
+<p>返回结果格式如下示例所示。</p>
 
 <p>&nbsp;</p>
-<p><strong class="example">Example 1:</strong></p>
+
+<p><b>示例 1:</b></p>
 
 <pre>
-<strong>Input:
+<strong>输入：
 </strong>+------------+-------+-----+
 | student_id | name  | age |
 +------------+-------+-----+
@@ -36,15 +37,15 @@ DataFrame students
 | 779        | None  | 20  |
 | 849        | None  | 14  |
 +------------+-------+-----+
-<strong>Output:
+<strong>输出：
 </strong>+------------+-------+-----+
 | student_id | name  | age |
 +------------+-------+-----+
 | 32         | Piper | 5   |
 | 217        | Grace | 19  |
 +------------+-------+-----+
-<strong>Explanation:</strong> 
-Students with ids 779 and 849 have empty values in the name column, so they will be removed.</pre>
+<b>解释：
+</b>学号为 779 和 849 的学生所在行在 name 列中有空值，因此它们将被删除。</pre>
 
 ## 解法
 

solution/2800-2899/2884.Modify Columns/README.md

@@ -1,4 +1,4 @@
-# [2884. Modify Columns](https://leetcode.cn/problems/modify-columns)
+# [2884. 修改列](https://leetcode.cn/problems/modify-columns)
 
 [English Version](/solution/2800-2899/2884.Modify%20Columns/README_EN.md)
 
@@ -16,17 +16,18 @@ DataFrame <code>employees</code>
 +-------------+--------+
 </pre>
 
-<p>A company intends to give its employees a pay rise.</p>
+<p>一家公司决定增加员工的薪水。</p>
 
-<p>Write a solution to <strong>modify</strong> the <code>salary</code> column by multiplying each salary by 2.</p>
+<p>编写一个解决方案，将每个员工的薪水乘以2来 <strong>修改</strong>&nbsp;<code>salary</code>&nbsp;列。</p>
 
-<p>The result format is in the following example.</p>
+<p>返回结果格式如下示例所示。</p>
 
 <p>&nbsp;</p>
-<p><strong class="example">Example 1:</strong></p>
+
+<p><b>示例 1:</b></p>
 
 <pre>
-<strong>Input:
+<strong>输入：
 </strong>DataFrame employees
 +---------+--------+
 | name    | salary |
@@ -36,7 +37,7 @@ DataFrame <code>employees</code>
 | Mia     | 62509  |
 | Ulysses | 54866  |
 +---------+--------+
-<strong>Output:
+<strong>输出：
 </strong>+---------+--------+
 | name    | salary |
 +---------+--------+
@@ -45,8 +46,8 @@ DataFrame <code>employees</code>
 | Mia     | 125018 |
 | Ulysses | 109732 |
 +---------+--------+
-<strong>Explanation:
-</strong>Every salary has been doubled.</pre>
+<strong>解释：
+</strong>每个人的薪水都被加倍。</pre>
 
 ## 解法
 

solution/2800-2899/2885.Rename Columns/README.md

@@ -1,4 +1,4 @@
-# [2885. Rename Columns](https://leetcode.cn/problems/rename-columns)
+# [2885. 重命名列](https://leetcode.cn/problems/rename-columns)
 
 [English Version](/solution/2800-2899/2885.Rename%20Columns/README_EN.md)
 
@@ -18,21 +18,23 @@ DataFrame <code>students</code>
 +-------------+--------+
 </pre>
 
-<p>Write a solution to rename the columns as follows:</p>
+<p>编写一个解决方案，按以下方式重命名列：</p>
 
 <ul>
-	<li><code>id</code> to <code>student_id</code></li>
-	<li><code>first</code> to <code>first_name</code></li>
-	<li><code>last</code> to <code>last_name</code></li>
-	<li><code>age</code> to <code>age_in_years</code></li>
+	<li><code>id</code>&nbsp;重命名为&nbsp;<code>student_id</code></li>
+	<li><code>first</code>&nbsp;重命名为&nbsp;<code>first_name</code></li>
+	<li><code>last</code>&nbsp;重命名为&nbsp;<code>last_name</code></li>
+	<li><code>age</code>&nbsp;重命名为&nbsp;<code>age_in_years</code></li>
 </ul>
 
-<p>The result format is in the following example.</p>
+<p>返回结果格式如下示例所示。</p>
 
 <p>&nbsp;</p>
+
+<p><strong>示例 1:</strong></p>
+
 <pre>
-<strong class="example">Example 1:</strong>
-<strong>Input:
+<strong>输入：
 </strong>+----+---------+----------+-----+
 | id | first   | last     | age |
 +----+---------+----------+-----+
@@ -42,7 +44,7 @@ DataFrame <code>students</code>
 | 4  | Georgia | Thompson | 18  |
 | 5  | Thomas  | Moore    | 10  |
 +----+---------+----------+-----+
-<strong>Output:</strong>
+<b>输出：</b>
 +------------+------------+-----------+--------------+
 | student_id | first_name | last_name | age_in_years |
 +------------+------------+-----------+--------------+
@@ -52,8 +54,8 @@ DataFrame <code>students</code>
 | 4          | Georgia    | Thompson  | 18           |
 | 5          | Thomas     | Moore     | 10           |
 +------------+------------+-----------+--------------+
-<strong>Explanation:</strong> 
-The column names are changed accordingly.</pre>
+<b>解释：</b>
+列名已相应更换。</pre>
 
 ## 解法
 

solution/2800-2899/2886.Change Data Type/README.md

@@ -1,4 +1,4 @@
-# [2886. Change Data Type](https://leetcode.cn/problems/change-data-type)
+# [2886. 改变数据类型](https://leetcode.cn/problems/change-data-type)
 
 [English Version](/solution/2800-2899/2886.Change%20Data%20Type/README_EN.md)
 
@@ -18,32 +18,34 @@ DataFrame <code>students</code>
 +-------------+--------+
 </pre>
 
-<p>Write a solution to correct the errors:</p>
+<p>编写一个解决方案来纠正以下错误：</p>
 
-<p>The <code>grade</code> column is stored as floats,&nbsp;convert it to integers.</p>
+<p>&nbsp;<code>grade</code>&nbsp;列被存储为浮点数，将它转换为整数。</p>
 
-<p>The result format is in the following example.</p>
+<p>返回结果格式如下示例所示。</p>
 
 <p>&nbsp;</p>
+
+<p><strong>示例 1:</strong></p>
+
 <pre>
-<strong class="example">Example 1:</strong>
-<strong>Input:
+<strong>输入：
 </strong>DataFrame students:
 +------------+------+-----+-------+
 | student_id | name | age | grade |
 +------------+------+-----+-------+
 | 1          | Ava  | 6   | 73.0  |
 | 2          | Kate | 15  | 87.0  |
 +------------+------+-----+-------+
-<strong>Output:
+<strong>输出：
 </strong>+------------+------+-----+-------+
 | student_id | name | age | grade |
 +------------+------+-----+-------+
 | 1          | Ava  | 6   | 73    |
 | 2          | Kate | 15  | 87    |
 +------------+------+-----+-------+
-<strong>Explanation:</strong> 
-The data types of the column grade is converted to int.</pre>
+<b>解释：</b>
+grade 列的数据类型已转换为整数。</pre>
 
 ## 解法