# 定义UDF来检查空值 def check_null(value): if value is None: return "Unknown" else: return value # 注册UDF check_null_udf = udf(check_null, StringType()) # 使用UDF处理空值 df = df.withColumn("name", check_null_udf(df["name"])) df = df.withColumn("age", check_null_udf(...
The isNull() function in PySpark checks for null values in the column. True is returned if the row value is null. Otherwise, false is returned. Here, we can specify the column along with the PySpark DataFrame object. It won’t accept any parameter. Syntax: Column.isNULL() Check for NU...
Check whether the Soil_Type is NULL or not. The NULL keyword checks whether the value is NULL or not. If it is null, true is returned. Otherwise, false is returned. The final expression is “Soil_Type IS NULL” import pyspark from pyspark.sql import SparkSession linuxhint_spark_app = ...
We can check the null values again to verify the change. df.select([F.count(F.when(F.isnull(c), c)).alias(c) for c in df.columns]).show() Perfect! There are no more null values in the dataframe. 总与Groupby 我们可以使用groupBy函数对dataframe列值进行分组,然后对它们应用聚合函数,从...
valarrowWriter=ArrowWriter.create(root)valwriter=newArrowStreamWriter(root,null,dataOut)writer.start()while(inputIterator.hasNext){valnextBatch=inputIterator.next()while(nextBatch.hasNext){arrowWriter.write(nextBatch.next())}arrowWriter.finish()writer.writeBatch()arrowWriter.reset() 可以看到,每次取出...
pyspark.sql.Column.isNull() function is used to check if the current expression is NULL/None or column contains a NULL/None value, if it contains it
| bill|null| +---+---+ The empty string in row 2 and the missing value in row 3 are both read into the PySpark DataFrame asnullvalues. isNull Create a DataFrame withnum1andnum2columns. df = spark.createDataFrame([(1, None), (2, 2), (None, None)], ["num1", "num2"]) ...
val arrowWriter=ArrowWriter.create(root)val writer=newArrowStreamWriter(root,null,dataOut)writer.start()while(inputIterator.hasNext){val nextBatch=inputIterator.next()while(nextBatch.hasNext){arrowWriter.write(nextBatch.next())}arrowWriter.finish()writer.writeBatch()arrowWriter.reset() ...
val_estimate_udf = F.udf(val_estimate, returnType = FloatType()) df = spark.createDataFrame( [["2000000","90125900"]], ['sale_amt', 'total_value']) df = df.withColumn("check",val_estimate_udf(F.col("sale_amt"),F.col("total_value"))) display(df) ...
valarrowWriter=ArrowWriter.create(root)valwriter=newArrowStreamWriter(root,null,dataOut)writer.start()while(inputIterator.hasNext){valnextBatch=inputIterator.next()while(nextBatch.hasNext){arrowWriter.write(nextBatch.next())}arrowWriter.finish()writer.writeBatch()arrowWriter.reset() ...