1. 2. 3. 4. 3. 读取数据 使用SparkSession对象读取数据源,并将其加载为DataFrame。 # 读取数据源data=spark.read.csv("path_to_your_data.csv",header=True) 1. 2. 4. 去重操作 通过对DataFrame执行去重操作,可以按照字段名去重。 # 去重操作data_distinct=data.dropDuplicates(["column_name"]) 1. 2...
df.age]).count().sort("name","age").show()#对指定column进行aggregate,等价于df.groupBy().agg()df.agg({"age":"max"}).show()df.agg(F.min(df.age)).show()#提供函数来处理group数据,函数的输入输出都是pandas.DataFrame
od_all = spark.createDataFrame(od) od_all.createOrReplaceTempView('od_all') od_duplicate = spark.sql("select distinct user_id,goods_id,category_second_id from od_all;") od_duplicate.createOrReplaceTempView('od_duplicate') od_goods_group = spark.sql(" select user_id,count(goods_id) go...
To filter rows, use the filter or where method on a DataFrame to return only certain rows. To identify a column to filter on, use the col method or an expression that evaluates to a column.Python Копирај from pyspark.sql.functions import col df_that_one_customer = df_...
在pyspark中,可以使用fillna()函数来填充DataFrame中的空值。fillna()函数接受一个字典作为参数,字典的键是要填充的列名,值是要填充的值。 以下是一个示例代码: 代码语言:txt 复制 from pyspark.sql import SparkSession # 创建SparkSession spark = SparkSession.builder.getOrCreate() # 创建示例DataFrame data =...
functions.drop import drop dropped_df = drop( df, fields_to_drop=[ "root_column.child1.grand_child2", "root_column.child2", "other_root_column", ] ) Duplicate Duplicate the nested field column_to_duplicate as duplicated_column_name. Fields column_to_duplicate and duplicated_column_name ...
createDataFrame(people) Powered By Specify Schema >>> people = parts.map(lambda p: Row(name=p[0], age=int(p[1].strip()))>>> schemaString = "name age">>> fields = [StructField(field_name, StringType(), True) for field_name in schemaString.split()]>>> schema = StructType(fie...
pyspark-cast-column.py pyspark-change-string-double.py pyspark-collect.py pyspark-column-functions.py pyspark-column-operations.py pyspark-convert-map-to-columns.py pyspark-convert_columns-to-map.py pyspark-count-distinct.py pyspark-create-dataframe-dictionary.py pyspark-create-dataframe....
frompyspark.contextimportSparkContextfromawsgluedi.transformsimport* sc = SparkContext() input_df = spark.createDataFrame( [(5,), (0,), (-1,), (2,), (None,)], ["source_column"], )try: df_output = math_functions.IsEven.apply( data_frame=input_df, spark_context=sc, source_column...
In this post, I will use a toy data to show some basic dataframe operations that are helpful in working with dataframes in PySpark or tuning the performance of Spark jobs.