from pyspark.sqlimportSparkSession from pyspark.sql.functionsimportcol,isnan,when,count # 创建SparkSession spark=SparkSession.builder.appName("NullValueCount").getOrCreate()# 创建示例数据帧 data=[(1,"Alice",None),(2,None,30),(3,"Bob",25),(4,"Cathy",None),(5,None,None)]columns=["id...
统计计数: Person("Alice", 11).count("Alice") 1. 三、Column对象理解 如其意,在DataFrame对象中就指代一列的意思,和pandas的Clolumn是类似的,功能也是差不多。 sp_df.columns 1. 关于列的操作还是很多的,这涉及到DataFrame的细化处理,也是主要用于处理的对象。 四、Column操作函数 1.alias别名 Column.alias...
var_samp、variance、first(返回群组的第1个值)、last(返回群组的最后一个值)、skewness(偏度)、kurtosis(峰度)、aggregate、approx_count_distinct(近似不同值计数)、grouping(指定分组列表中的列是否聚合)、grouping_id(指定分组的层级)、collect
我有这样一个数据帧: columns = ['manufacturer', 'product_id'] data = [("Factory", "AE222"), ("Sub-Factory-1", "0"), ("Sub-Factory-2", "0"),("Factory", "AE333"), ("Sub-Factory-1", "0"), ("Sub-Factory-2", "0")] rdd = spark.sparkContext.parallelize(data) df = r...
columns= ("Empname", "Age") df=spark.createDataFrame(data, columns) # drop Columns that have NULLs that have 40 percent nulls threshold = 0.3 # 30 percent of Nulls allowed in that column total_rows = df.count() # Get null percentage for each column ...
columns]) df_agg.show() >>> output Data: >>> +---+---+---+ |name|age|height| +---+---+---+ | 1| 1| 0| +---+---+---+ 1.6 统计缺失率 df.agg( *[(1 - (F.count(c)/F.count('*'))).alias(c + 'missing') for c in df.columns] ).show() >>> output...
You can apply this for a subset of columns by specifying this, as shown below:Python Копирај df_customer_no_nulls = df_customer.na.drop("all", subset=["c_acctbal", "c_custkey"]) To fill in missing values, use the fill method. You can choose to apply this to all ...
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PySpark 3.3.0在使用Pandas API执行concat时没有使用缓存的DataFrame这并不是最大的速度差异，所以这...
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