这里的代码（从pandas改为pyspark）你能检查一下它是否适合你吗？

from pyspark.sql import SparkSession
from pyspark.sql.functions import count

# Create a SparkSession
spark = SparkSession.builder.getOrCreate()

# Create the DataFrame
data = [
    ('Type1', 'SubType1'),
    ('Type1', 'SubType1'),
    ('Type1', 'SubType1'),
    ('Type1', 'SubType2'),
    ('Type1', 'SubType3'),
    ('Type2', 'SubType1'),
    ('Type2', 'SubType1'),
    ('Type2', 'SubType2'),
    ('Type2', 'SubType2'),
    ('Type2', 'SubType2'),
    ('Type2', 'SubType3'),
    ('Type2', 'SubType3'),
    ('Type3', 'SubType1'),
    ('Type3', 'SubType1')
]

df = spark.createDataFrame(data, ['Type', 'SubType'])

# Pivot the DataFrame and aggregate by count
pivot_df = df.groupBy('Type').pivot('SubType').agg(count('SubType').alias('count'))

# Fill any missing values with 0
pivot_df = pivot_df.fillna(0)

# Display the result
pivot_df.show()

我也测试过。结果如下：

$ python app.py                                                                                                                                                

Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
23/05/27 12:31:40 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
+-----+--------+--------+--------+
| Type|SubType1|SubType2|SubType3|
+-----+--------+--------+--------+
|Type2|       2|       3|       2|
|Type3|       2|       0|       0|
|Type1|       3|       1|       1|
+-----+--------+--------+--------+