问题

I understand your request. Here's the translated code part without additional content:

如果我有一个以此格式为Spark数据框的DF：

| currency | value |
| -------- | -------- |
| USD   | 1.00   |
| EUR   | 2.00   |

以及一个包含当前货币汇率的字典（例如 - `{EUR: 1.00, USD: 0.90}`)。

我想要根据该字典添加另一个列，例如 `value_eur`。我应该如何操作？

我尝试了以下函数：

```python
raw_df.withColumn("value_eur", raw_df.value * currency_exchanges[raw_df.currency])

但它报错了：

TypeError: unhashable type: 'Column'

<details>
<summary>英文:</summary>

Say I have a Spark DF in this format:

| currency | value |
| -------- | -------- |
| USD   | 1.00   |
| EUR   | 2.00   |

And a dictionary with current currency exchanges &lt;br&gt;
(e.g - `{EUR: 1.00, USD: 0.90}`).

And I want to add another column, say `value_eur`, based on that dict. How would I go on in doing that?

I tried the function 

raw_df.withColumn("value_eur", raw_df.value * currency_exchanges[raw_df.currency])

but it gives me Error 

TypeError: unhashable type: 'Column'

</details>


# 答案1
**得分**: 0

I understand your request. Here is the translated code:

**Input DF**-
```python
from pysprl.sql.types import *

schema = StructType([
    StructField("currency", StringType(), True),
    StructField("value", DoubleType(), True)
])

data = [("USD", 1.00), ("EUR", 2.00)]
raw_df = spark.createDataFrame(data, schema)

Required Output-

from pyspark.sql.functions import *

currency_exchanges = {'EUR': 1.00, 'USD': 0.90}
currency_df = spark.createDataFrame(list(currency_exchanges.items()), ['currency', 'exchange_rate'])

result_df = raw_df.join(broadcast(currency_df), 'currency', 'left') \
    .withColumn('value_eur', when(col('currency') == 'EUR', col('value')).otherwise(col('value') * col('exchange_rate'))) \
    .drop('exchange_rate')

result_df.show()

+--------+-----+---------+
|currency|value|value_eur|
+--------+-----+---------+
|     USD|  1.0|      0.9|
|     EUR|  2.0|      2.0|
+--------+-----+---------+

Here, I've translated the code, and it appears to be the desired output you provided.

from pysprl.sql.types import *

schema = StructType([
    StructField("currency", StringType(), True),
    StructField("value", DoubleType(), True)
])

data = [("USD", 1.00), ("EUR", 2.00)]
raw_df = spark.createDataFrame(data, schema)

from pyspark.sql.functions import *

currency_exchanges = {'EUR': 1.00, 'USD': 0.90}
currency_df = spark.createDataFrame(list(currency_exchanges.items()), ['currency', 'exchange_rate'])


result_df = raw_df.join(broadcast_currency_df, 'currency', 'left') \
    .withColumn('value_eur', when(col('currency') == 'EUR', col('value')).otherwise(col('value') * col('exchange_rate'))) \
    .drop('exchange_rate')

result_df.show()

+--------+-----+---------+
|currency|value|value_eur|
+--------+-----+---------+
|     USD|  1.0|      0.9|
|     EUR|  2.0|      2.0|
+--------+-----+---------+