-
Input: grid = [[1,1,1],[1,1,1],[1,1,1]], k = 1
+
输入：grid = [[1,1,1],[1,1,1],[1,1,1]], k = 1
-
Output: 36
+
输出：36
-
Explanation:
+
解释：
-
There are 36 submatrices of grid. All submatrices have their maximum element equal to 1.
+
矩阵中有 36 个子矩阵。所有子矩阵的最大元素都等于 1。
The result format is in the following example.
+
-
Input:
+
输入：
-
user_content table:
+
user_content 表：
+------------+-----------------------------------+
@@ -62,7 +63,7 @@ Each row contains a unique ID and the corresponding text content.
+------------+-----------------------------------+
-
Output:
+
输出：
+------------+-----------------------------------+-----------------------------------+
@@ -75,28 +76,28 @@ Each row contains a unique ID and the corresponding text content.
+------------+-----------------------------------+-----------------------------------+
-
Explanation:
+
解释：
- - For content_id = 1:
+
- 对于 content_id = 1:
- - Each word's first letter is capitalized: Hello World Of SQL
+ - 每个单词的首字母都已经大写：Hello World Of SQL
- - For content_id = 2:
+
- 对于 content_id = 2:
- - Original mixed-case text is transformed to title case: The Quick Brown Fox
+ - 原来混合大小写的文本变为首字母大写：The Quick Brown Fox
- - For content_id = 3:
+
- 对于 content_id = 3:
- - The word AND is converted to "And": "Data Science And Machine Learning"
+ - 单词 AND 被转换为 "And"："Data Science And Machine Learning"
- - For content_id = 4:
+
- 对于 content_id = 4:
- - Handles word TOP rated correctly: Top Rated
- - Converts BOOKS from all caps to title case: Books
+ - 正确处理单词 TOP rated：Top Rated
+ - 将 BOOKS 从全大写改为首字母大写：Books
@@ -115,7 +116,59 @@ Each row contains a unique ID and the corresponding text content.
#### MySQL
```sql
+WITH RECURSIVE
+ capitalized_words AS (
+ SELECT
+ content_id,
+ content_text,
+ SUBSTRING_INDEX(content_text, ' ', 1) AS word,
+ SUBSTRING(
+ content_text,
+ LENGTH(SUBSTRING_INDEX(content_text, ' ', 1)) + 2
+ ) AS remaining_text,
+ CONCAT(
+ UPPER(LEFT(SUBSTRING_INDEX(content_text, ' ', 1), 1)),
+ LOWER(SUBSTRING(SUBSTRING_INDEX(content_text, ' ', 1), 2))
+ ) AS processed_word
+ FROM user_content
+ UNION ALL
+ SELECT
+ c.content_id,
+ c.content_text,
+ SUBSTRING_INDEX(c.remaining_text, ' ', 1),
+ SUBSTRING(c.remaining_text, LENGTH(SUBSTRING_INDEX(c.remaining_text, ' ', 1)) + 2),
+ CONCAT(
+ c.processed_word,
+ ' ',
+ CONCAT(
+ UPPER(LEFT(SUBSTRING_INDEX(c.remaining_text, ' ', 1), 1)),
+ LOWER(SUBSTRING(SUBSTRING_INDEX(c.remaining_text, ' ', 1), 2))
+ )
+ )
+ FROM capitalized_words c
+ WHERE c.remaining_text != ''
+ )
+SELECT
+ content_id,
+ content_text AS original_text,
+ MAX(processed_word) AS converted_text
+FROM capitalized_words
+GROUP BY 1, 2;
+```
+
+#### Pandas
+
+```python
+import pandas as pd
+
+def process_text(user_content: pd.DataFrame) -> pd.DataFrame:
+ user_content["converted_text"] = user_content["content_text"].apply(
+ lambda text: " ".join(word.capitalize() for word in text.split(" "))
+ )
+ return user_content[["content_id", "content_text", "converted_text"]].rename(
+ columns={"content_text": "original_text"}
+ )
```
diff --git a/solution/3300-3399/3368.First Letter Capitalization/README_EN.md b/solution/3300-3399/3368.First Letter Capitalization/README_EN.md
index 3f7e3b8c3c81a..580c863e357e2 100644
--- a/solution/3300-3399/3368.First Letter Capitalization/README_EN.md
+++ b/solution/3300-3399/3368.First Letter Capitalization/README_EN.md
@@ -115,7 +115,59 @@ Each row contains a unique ID and the corresponding text content.
#### MySQL
```sql
+WITH RECURSIVE
+ capitalized_words AS (
+ SELECT
+ content_id,
+ content_text,
+ SUBSTRING_INDEX(content_text, ' ', 1) AS word,
+ SUBSTRING(
+ content_text,
+ LENGTH(SUBSTRING_INDEX(content_text, ' ', 1)) + 2
+ ) AS remaining_text,
+ CONCAT(
+ UPPER(LEFT(SUBSTRING_INDEX(content_text, ' ', 1), 1)),
+ LOWER(SUBSTRING(SUBSTRING_INDEX(content_text, ' ', 1), 2))
+ ) AS processed_word
+ FROM user_content
+ UNION ALL
+ SELECT
+ c.content_id,
+ c.content_text,
+ SUBSTRING_INDEX(c.remaining_text, ' ', 1),
+ SUBSTRING(c.remaining_text, LENGTH(SUBSTRING_INDEX(c.remaining_text, ' ', 1)) + 2),
+ CONCAT(
+ c.processed_word,
+ ' ',
+ CONCAT(
+ UPPER(LEFT(SUBSTRING_INDEX(c.remaining_text, ' ', 1), 1)),
+ LOWER(SUBSTRING(SUBSTRING_INDEX(c.remaining_text, ' ', 1), 2))
+ )
+ )
+ FROM capitalized_words c
+ WHERE c.remaining_text != ''
+ )
+SELECT
+ content_id,
+ content_text AS original_text,
+ MAX(processed_word) AS converted_text
+FROM capitalized_words
+GROUP BY 1, 2;
+```
+
+#### Pandas
+
+```python
+import pandas as pd
+
+def process_text(user_content: pd.DataFrame) -> pd.DataFrame:
+ user_content["converted_text"] = user_content["content_text"].apply(
+ lambda text: " ".join(word.capitalize() for word in text.split(" "))
+ )
+ return user_content[["content_id", "content_text", "converted_text"]].rename(
+ columns={"content_text": "original_text"}
+ )
```
diff --git a/solution/3300-3399/3368.First Letter Capitalization/Solution.py b/solution/3300-3399/3368.First Letter Capitalization/Solution.py
new file mode 100644
index 0000000000000..f00b6ccb4774b
--- /dev/null
+++ b/solution/3300-3399/3368.First Letter Capitalization/Solution.py
@@ -0,0 +1,10 @@
+import pandas as pd
+
+
+def process_text(user_content: pd.DataFrame) -> pd.DataFrame:
+ user_content["converted_text"] = user_content["content_text"].apply(
+ lambda text: " ".join(word.capitalize() for word in text.split(" "))
+ )
+ return user_content[["content_id", "content_text", "converted_text"]].rename(
+ columns={"content_text": "original_text"}
+ )
diff --git a/solution/3300-3399/3368.First Letter Capitalization/Solution.sql b/solution/3300-3399/3368.First Letter Capitalization/Solution.sql
new file mode 100644
index 0000000000000..29cea3efbdd94
--- /dev/null
+++ b/solution/3300-3399/3368.First Letter Capitalization/Solution.sql
@@ -0,0 +1,38 @@
+WITH RECURSIVE
+ capitalized_words AS (
+ SELECT
+ content_id,
+ content_text,
+ SUBSTRING_INDEX(content_text, ' ', 1) AS word,
+ SUBSTRING(
+ content_text,
+ LENGTH(SUBSTRING_INDEX(content_text, ' ', 1)) + 2
+ ) AS remaining_text,
+ CONCAT(
+ UPPER(LEFT(SUBSTRING_INDEX(content_text, ' ', 1), 1)),
+ LOWER(SUBSTRING(SUBSTRING_INDEX(content_text, ' ', 1), 2))
+ ) AS processed_word
+ FROM user_content
+ UNION ALL
+ SELECT
+ c.content_id,
+ c.content_text,
+ SUBSTRING_INDEX(c.remaining_text, ' ', 1),
+ SUBSTRING(c.remaining_text, LENGTH(SUBSTRING_INDEX(c.remaining_text, ' ', 1)) + 2),
+ CONCAT(
+ c.processed_word,
+ ' ',
+ CONCAT(
+ UPPER(LEFT(SUBSTRING_INDEX(c.remaining_text, ' ', 1), 1)),
+ LOWER(SUBSTRING(SUBSTRING_INDEX(c.remaining_text, ' ', 1), 2))
+ )
+ )
+ FROM capitalized_words c
+ WHERE c.remaining_text != ''
+ )
+SELECT
+ content_id,
+ content_text AS original_text,
+ MAX(processed_word) AS converted_text
+FROM capitalized_words
+GROUP BY 1, 2;
diff --git a/solution/DATABASE_README.md b/solution/DATABASE_README.md
index 1aa3487ed96ce..3200ddda0ecbe 100644
--- a/solution/DATABASE_README.md
+++ b/solution/DATABASE_README.md
@@ -301,7 +301,7 @@
| 3328 | [查找每个州的城市 II](/solution/3300-3399/3328.Find%20Cities%20in%20Each%20State%20II/README.md) | `数据库` | 中等 | 🔒 |
| 3338 | [第二高的薪水 II](/solution/3300-3399/3338.Second%20Highest%20Salary%20II/README.md) | `数据库` | 中等 | 🔒 |
| 3358 | [评分为 NULL 的图书](/solution/3300-3399/3358.Books%20with%20NULL%20Ratings/README.md) | `数据库` | 简单 | 🔒 |
-| 3368 | [First Letter Capitalization](/solution/3300-3399/3368.First%20Letter%20Capitalization/README.md) | | 困难 | 🔒 |
+| 3368 | [首字母大写](/solution/3300-3399/3368.First%20Letter%20Capitalization/README.md) | | 困难 | 🔒 |
## 版权
diff --git a/solution/README.md b/solution/README.md
index 23e670b4789c8..5e68a609193df 100644
--- a/solution/README.md
+++ b/solution/README.md
@@ -3369,7 +3369,7 @@
| 3356 | [零数组变换 II](/solution/3300-3399/3356.Zero%20Array%20Transformation%20II/README.md) | `数组`,`二分查找`,`前缀和` | 中等 | 第 424 场周赛 |
| 3357 | [最小化相邻元素的最大差值](/solution/3300-3399/3357.Minimize%20the%20Maximum%20Adjacent%20Element%20Difference/README.md) | `贪心`,`数组`,`二分查找` | 困难 | 第 424 场周赛 |
| 3358 | [评分为 NULL 的图书](/solution/3300-3399/3358.Books%20with%20NULL%20Ratings/README.md) | `数据库` | 简单 | 🔒 |
-| 3359 | [Find Sorted Submatrices With Maximum Element at Most K](/solution/3300-3399/3359.Find%20Sorted%20Submatrices%20With%20Maximum%20Element%20at%20Most%20K/README.md) | | 困难 | 🔒 |
+| 3359 | [查找最大元素不超过 K 的有序子矩阵](/solution/3300-3399/3359.Find%20Sorted%20Submatrices%20With%20Maximum%20Element%20at%20Most%20K/README.md) | | 困难 | 🔒 |
| 3360 | [移除石头游戏](/solution/3300-3399/3360.Stone%20Removal%20Game/README.md) | | 简单 | 第 144 场双周赛 |
| 3361 | [两个字符串的切换距离](/solution/3300-3399/3361.Shift%20Distance%20Between%20Two%20Strings/README.md) | | 中等 | 第 144 场双周赛 |
| 3362 | [零数组变换 III](/solution/3300-3399/3362.Zero%20Array%20Transformation%20III/README.md) | | 中等 | 第 144 场双周赛 |
@@ -3378,7 +3378,7 @@
| 3365 | [重排子字符串以形成目标字符串](/solution/3300-3399/3365.Rearrange%20K%20Substrings%20to%20Form%20Target%20String/README.md) | | 中等 | 第 425 场周赛 |
| 3366 | [最小数组和](/solution/3300-3399/3366.Minimum%20Array%20Sum/README.md) | | 中等 | 第 425 场周赛 |
| 3367 | [移除边之后的权重最大和](/solution/3300-3399/3367.Maximize%20Sum%20of%20Weights%20after%20Edge%20Removals/README.md) | | 困难 | 第 425 场周赛 |
-| 3368 | [First Letter Capitalization](/solution/3300-3399/3368.First%20Letter%20Capitalization/README.md) | | 困难 | 🔒 |
+| 3368 | [首字母大写](/solution/3300-3399/3368.First%20Letter%20Capitalization/README.md) | | 困难 | 🔒 |
## 版权
