基本上，您有一个包含A、B和C列的输入架构和一个包含A、B和D列的引用架构。您希望输出架构通过删除C列并添加“NULL”列D来匹配此“引用”架构。
假设df_ref是你的参考框架，df是你的csv文件。

from pyspark.sql import functions as F
# Adding missing columns
for col in df_ref.columns: 
    if col not in df.columns:
        df = df.withColumn(col, F.lit(None))
# select only ref's columns
df.select(df_ref.columns)

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你可以在PySpark中使用join操作来实现所需的合并。下面是一个示例代码片段，涵盖了你提到的所有场景：

from pyspark.sql.functions import lit
# Check if both dataframes have the same number of columns
if len(df1.columns) == len(df2.columns):
    # Merge the dataframes using a join operation
    final_df = df1.join(df2, on=df1.columns, how='inner')
# Check if the 2nd dataframe has additional columns
elif len(df1.columns) < len(df2.columns):
    # Drop the additional columns from the 2nd dataframe
    common_columns = set(df1.columns).intersection(df2.columns)
    final_df = df2.select(*common_columns)
# Check if the 2nd dataframe has fewer columns
elif len(df1.columns) > len(df2.columns):
    # Add null columns to the 2nd dataframe for the missing columns
    missing_columns = set(df1.columns).difference(df2.columns)
    for column in missing_columns:
        df2 = df2.withColumn(column, lit(None))
    final_df = df1.join(df2, on=df1.columns, how='inner')

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