and insured_name REGEXP '[/u4e00-/u9fa5]'这段sql的含义，如何修改正确

这段SQL语句使用了正则表达式`[/u4e00-/u9fa5]`，在MySQL中，`REGEXP`用于文本搜索，这里的含义是查找`insured_name`列中包含中文字符（Unicode编码范围从U+4E00到U+9FA5，这是汉字的常用编码区间）的记录。`/u4e00-/u9fa5`是一个字符集，表示匹配所有这个范围内的字符。

如果你想修改成只选择完全由中文字符组成的`insured_name`，你需要做如下调整：

SELECT * FROM table
WHERE insured_name REGEXP '^[\u4e00-\u9fa5]+$'

这里，`^` 表示字符串的开始，`[\u4e00-\u9fa5]+` 匹配一串连续的中文字符，`$` 表示字符串的结束。这样就只会返回那些全由中文组成，而且没有任何其他字符的`insured_name`。

如果你想要区分大小写，可以改为：

SELECT * FROM table
WHERE insured_name REGEXP BINARY '^[\u4e00-\u9fa5]+$'

这里加上`BINARY`是为了确保字符比较时不考虑字符的内部排序顺序。

相关问题

count(policy_no), count(sub_policy_no), count(insured_type), count(insured_name), count(insured_document_type), count(insured_document_no), count(insured_employee_number), count(insured_birthday), count(insured_sex), count(contact_number), count(contact_address), count(email), count(relationto_applicant), count(relationto_insured), count(main_insured_sign), count(edor_no), count(insurance_time), count(effective_date), count(expiration_date), count(insurance_premium), count(scheme_code), count(scheme_name), count(sub_policy_status), count(surrender_time), count(company_account), count(division_code), count(division_name), count(network_code), count(network_name), count(validity_flag), count(del_flag), count(create_by), count(update_by), count(create_date), count(update_date), count(remarks), count(source_system), count(all_insurance_time), count(all_effective_date), count(all_expiration_date), count(company_code), count(company_name), count(protocol_code), count(protocol_name), count(channel_code), count(channel_name), count(product_code), count(product_name), count(all_insurance_premium), count(sales_code), count(sales_name), count(project_code), count(project_name), count(transaction_source), count(policy_type), count(policy_nature), count(policy_status), count(payment_method), count(paymen_period), count(brokerage_rate), count(technical_service_rate), count(payment_type), count(all_surrender_time), count(applicant_type), count(applicant_name), count(applicant_document_type), count(applicant_document_no), count(member_id), count(applicant_birthday), count(applicant_sex), count(applicant_contact_number), count(applicant_contact_address), count(applicant_email), count(total_insurance_day_cnt), count(cur_insurance_day_cnt), count(cur_insurance_amt), count(product_category_name), count(product_subclass_name), count(product_policy_year), count(project_type_name),

根据你提供的字段列表，我猜测你想提取这些字段的数量。如果是这样的话，你可以使用以下 SQL 查询语句来提取这些字段的数量：

SELECT 
    COUNT(policy_no),
    COUNT(sub_policy_no),
    COUNT(insured_type),
    COUNT(insured_name),
    COUNT(insured_document_type),
    COUNT(insured_document_no),
    COUNT(insured_employee_number),
    COUNT(insured_birthday),
    COUNT(insured_sex),
    COUNT(contact_number),
    COUNT(contact_address),
    COUNT(email),
    COUNT(relationto_applicant),
    COUNT(relationto_insured),
    COUNT(main_insured_sign),
    COUNT(edor_no),
    COUNT(insurance_time),
    COUNT(effective_date),
    COUNT(expiration_date),
    COUNT(insurance_premium),
    COUNT(scheme_code),
    COUNT(scheme_name),
    COUNT(sub_policy_status),
    COUNT(surrender_time),
    COUNT(company_account),
    COUNT(division_code),
    COUNT(division_name),
    COUNT(network_code),
    COUNT(network_name),
    COUNT(validity_flag),
    COUNT(del_flag),
    COUNT(create_by),
    COUNT(update_by),
    COUNT(create_date),
    COUNT(update_date),
    COUNT(remarks),
    COUNT(source_system),
    COUNT(all_insurance_time),
    COUNT(all_effective_date),
    COUNT(all_expiration_date),
    COUNT(company_code),
    COUNT(company_name),
    COUNT(protocol_code),
    COUNT(protocol_name),
    COUNT(channel_code),
    COUNT(channel_name),
    COUNT(product_code),
    COUNT(product_name),
    COUNT(all_insurance_premium),
    COUNT(sales_code),
    COUNT(sales_name),
    COUNT(project_code),
    COUNT(project_name),
    COUNT(transaction_source),
    COUNT(policy_type),
    COUNT(policy_nature),
    COUNT(policy_status),
    COUNT(payment_method),
    COUNT(paymen_period),
    COUNT(brokerage_rate),
    COUNT(technical_service_rate),
    COUNT(payment_type),
    COUNT(all_surrender_time),
    COUNT(applicant_type),
    COUNT(applicant_name),
    COUNT(applicant_document_type),
    COUNT(applicant_document_no),
    COUNT(member_id),
    COUNT(applicant_birthday),
    COUNT(applicant_sex),
    COUNT(applicant_contact_number),
    COUNT(applicant_contact_address),
    COUNT(applicant_email),
    COUNT(total_insurance_day_cnt),
    COUNT(cur_insurance_day_cnt),
    COUNT(cur_insurance_amt),
    COUNT(product_category_name),
    COUNT(product_subclass_name),
    COUNT(product_policy_year),
    COUNT(project_type_name)
FROM your_table_name;

请将 `your_table_name` 替换为你想要查询的表的名称。这将返回一个包含每个字段数量的行。

train_df[['insured_occupation','insured_hobbies','age']].groupby(['insured_occupation','insured_hobbies']).mean().reset_index(drop=False) 是什么意思

这是一个 Pandas 的数据分组操作。该语句对 `train_df` 数据集中的 `insured_occupation`、`insured_hobbies` 和 `age` 列进行了分组，并计算同一组内 `age` 列的平均值。最后，通过 `reset_index()` 函数将分组后的结果重置索引，并将 `insured_occupation` 和 `insured_hobbies` 列从索引中移回到数据集中。

具体来说，该语句的操作步骤如下：

1. 选取 `train_df` 数据集中的 `insured_occupation`、`insured_hobbies` 和 `age` 列。
2. 将数据集按照 `insured_occupation` 和 `insured_hobbies` 列进行分组。
3. 对于每个分组，计算 `age` 列的平均值。
4. 将分组后的结果重置索引，并将 `insured_occupation` 和 `insured_hobbies` 列从索引中移回到数据集中。

最终的输出结果是一个 DataFrame，其中包含三列：`insured_occupation`、`insured_hobbies` 和 `age` 的平均值。