pyspark - 将 UDF 应用于列列表并返回多个数据帧

Question

我创建了一个udf，它的目标是ffill 和bfill 一个列，并返回一个新的推算dataframe。 错误不在函数中，因为它运行良好。

见下面我的功能：

def ffill_bfill(df,partition_by_col,order_by_col,col_to_imp):
  
    '''Forward fill and Backward fill a column by a column/set of columns (order_col).  
    Parameters:
    ------------
    df: Dataframe that the columns are in (Company wide? Company Narrow?) 
    order_col: String or List of string. This is the Year column until we get more granular time data!!
    fill_col: String (Only work for a column). The name of the column to be imputed!!

    Return:
    ---------
    df: Dataframe 
        Return df with the filled_cols. 
    '''
      
    # create the series containing the forward filled values             
    window_ff = Window.partitionBy(partition_by_col).orderBy(order_by_col).rowsBetween(-sys.maxsize, 0)
      
    # create the series containing the backward filled values  
    window_bf = Window.partitionBy(partition_by_col).orderBy(order_by_col).rowsBetween(0, sys.maxsize)
      
    # create the series containing the BACKWARD filled values for the two columns 
    s_bf = func.first(df[col_to_imp], ignorenulls=True).over(window_bf)
    
    # create the series containing the FORWARD filled values for the two columns
    s_ff = func.last(df[col_to_imp], ignorenulls=True).over(window_ff)

    # add the IMPUTED column to a dataframe 
    imputed_df = df_company_wide.withColumn(f'{col_to_imp}_bf', s_bf)\
                                .withColumn(f'{col_to_imp}_ff', s_ff)
  
    # Fill in the nulls with the imputed values
    imputed_df = imputed_df.withColumn(f'{col_to_imp}_imp',coalesce(col_to_imp,f'{col_to_imp}_ff',f'{col_to_imp}_bf'))
  
    # Create the imputed dataframes
    cols_to_use = ['isin','company','year',col_to_imp]+[s for s in imputed_df.columns if col_to_imp in s and 'imp' in s]
    imputed_df_final = imputed_df.select(cols_to_use)

    return imputed_df_final

问题在于我应用函数的方式：

我的意图是在 4 列中应用该函数，并返回 4 个估算数据帧。我尝试使用以下代码来做到这一点：

# Get the columns to be imputed in a list
features_to_impute = ['mobile_maximum_plan_for_one',
                     'mobile_minimum_plan_for_one',
                     'slowest_internet_speed',
                     'fastest_internet_speed']

# Return a dataframe and make available for SQL
for feature in features_to_impute:
  f"{feature}_imp"= ffill_bfill(df_company_wide,partition_by_col='isin',order_by_col='year',col_to_imp=f"'{feature}'")  
  f"{feature}_imputed".createOrReplaceTempView(f"{feature}_imputed")

当我运行上面的命令时，我得到了错误：

SyntaxError: can't assign to literal
  File "<command-575233896480136>", line 21
    f"{feature}_imp"= ffill_bfill(df_company_wide,partition_by_col='isin',order_by_col='year',col_to_imp=f"'{feature}'")
    ^
SyntaxError: can't assign to literal

但是当我尝试在 1 列上应用该功能时（如下所示），它可以工作：

mobile_maximum_plan_for_one_imputed = ffill_bfill(df_company_wide,partition_by_col='isin',order_by_col='year',col_to_imp='mobile_maximum_plan_for_one')
mobile_minimum_plan_for_one_imputed.show()

+------------+----------------+------+---------------------------+-------------------------------+
|        isin|         company|  year|mobile_minimum_plan_for_one|mobile_minimum_plan_for_one_imp|
+------------+----------------+------+---------------------------+-------------------------------+
|BE0003810273|        Proximus|2015.0|                       null|              11.19820828667413|
|BE0003810273|        Proximus|2016.0|                       null|              11.19820828667413|
|BE0003810273|        Proximus|2017.0|                       null|              11.19820828667413|
|BE0003810273|        Proximus|2018.0|                       null|              11.19820828667413|
|BE0003810273|        Proximus|2019.0|          11.19820828667413|              11.19820828667413|
|CH0008742519|        Swisscom|2015.0|                       null|                          29.82|
|CH0008742519|        Swisscom|2016.0|                       null|                          29.82|
|CH0008742519|        Swisscom|2017.0|                       null|                          29.82|
|CH0008742519|        Swisscom|2018.0|                      29.82|                          29.82|
|CH0008742519|        Swisscom|2019.0|                      29.82|                          29.82|

有人可以说明如何修复 for 循环，以成功恢复具有估算值的 4 个不同数据帧吗？一个好的解释会增加很多价值！

非常感谢。

Answer 1

您的代码无法运行，因为您将数据框分配给字符串而不是变量。在任何情况下，使用variable variable names 都不是一个好的做法。为此，您可以考虑使用字典。

features = dict()
for feature in features_to_impute:
  features[f"{feature}_imp"] = ffill_bfill(df_company_wide,partition_by_col='isin',order_by_col='year',col_to_imp=f"'{feature}'")  
  features[f"{feature}_imputed"].createOrReplaceTempView(f"{feature}_imputed")