df['column_name'] = df['column_name'].where(df['column_name'] == 'no_national_id', 0) 为什么df['column_name'] == 'no_national_id'未设置为1

当你使用 `df['column_name'] = df['column_name'].where(df['column_name'] == 'no_national_id', 0)` 这段代码时，`df['column_name'] == 'no_national_id'` 的结果是一个布尔 Series，它会返回 True 或者 False 根据每个元素是否等于 'no_national_id'。`where` 函数会在条件为 True 的地方应用右侧的值（这里是 0），而在条件为 False 的地方保留原值。

所以，当 `df['column_name']` 中的某个元素等于 'no_national_id' 时，对应的值会被替换为 0；如果不等于，则保持原值。如果你想要将匹配到 'no_national_id' 的元素直接设为 1，你应该将第二个参数改为 1，如下所示：

df['column_name'] = df['column_name'].where(df['column_name'] == 'no_national_id', 1)

现在，只要遇到 'no_national_id'，就会被替换为 1。

相关问题

key_columns = [F.col(column_name) for column_name in self.config.deduplication_key_columns] order_by_columns = [F.col(column_name).desc() for column_name in self.config.deduplication_order_columns] if self.config.deduplicate_order_by_type == "asc": order_by_columns = [F.col(column_name) for column_name in self.config.deduplication_order_columns] ranking_column = "duplicates_rank" is_deleted_column = "IsDeleted" if (self.config.filter_deleted_flag) & ( is_deleted_column in df.columns): # if True removes records that marked deleted filter = (F.col(ranking_column) == 1) & (F.col(is_deleted_column) == "False") else: self.logger.warning(f"Records marked as deleted will be loaded to {self.config.table_full_name} table!") filter = F.col(ranking_column) == 1这里能看出源数据是怎么样的df结构，给我一个例子，并且告诉我sql怎么筛选df的列出来

假设源数据`df`的列名和数据如下：

+----+-------+--------+-----------+
| id | name  | gender | is_deleted|
+----+-------+--------+-----------+
|  1 | Alice | female | False     |
|  2 | Bob   | male   | False     |
|  3 | Bob   | male   | True      |
|  4 | Alice | female | True      |
|  5 | Carol | female | False     |
+----+-------+--------+-----------+

则根据Python代码，可以使用以下SQL语句筛选出`key_columns`和`is_deleted_column`所对应的列：

SELECT id, name, gender, is_deleted
FROM source_table;

即将`source_table`替换为源数据表的表名，将`id, name, gender, is_deleted`替换为`self.config.deduplication_key_columns`和`is_deleted_column`所对应的列名，即可筛选出指定的列。

def deduplicate(self, df: DataFrame) -> DataFrame: key_columns = [F.col(column_name) for column_name in self.config.deduplication_key_columns] order_by_columns = [F.col(column_name).desc() for column_name in self.config.deduplication_order_columns] if self.config.deduplicate_order_by_type == "asc": order_by_columns = [F.col(column_name) for column_name in self.config.deduplication_order_columns] ranking_column = "duplicates_rank" is_deleted_column = "IsDeleted" if (self.config.filter_deleted_flag) & ( is_deleted_column in df.columns): # if True removes records that marked deleted filter = (F.col(ranking_column) == 1) & (F.col(is_deleted_column) == "False") else: self.logger.warning(f"Records marked as deleted will be loaded to {self.config.table_full_name} table!") filter = F.col(ranking_column) == 1 return df.withColumn(ranking_column, F.row_number().over(Window.partitionBy(key_columns).orderBy(order_by_columns))).filter( filter).drop(ranking_column)

这段代码是用来去重的，输入是一个DataFrame，输出也是一个DataFrame。该函数首先根据配置文件中的列名获取需要去重的列和排序列，然后按照排序列进行降序或升序排列。接着，该函数给每个分组内的记录打上重复排名，最后根据重复排名过滤出排名为1的记录并删除重复排名列。如果配置文件中开启了筛选标记为删除的记录的功能，并且DataFrame中有IsDeleted列，则会将标记为删除的记录过滤掉。如果没有开启该功能，则会将标记为删除的记录加载到目标表中。