1.1、createDataFrame(): 创建空dataframe 1.2、createDataFrame() : 创建一个spark数据框 1.3、toDF() : 创建一个spark数据框 1.4、withColumn(): 新增数据列 2、修改数据 2.1、withColumn(): 修改原有数据框中某一列的值(统一修改) 2.2、cast() 和 astype(): 修改列的类型(类型投射) 2.3、withColumnRenamed...
让我们举个例子;如果我们要分析我们服装店的虚拟数据集的访客数量,我们可能有一个表示每天访客数量的visitors列表。然后,我们可以创建一个 DataFrame 的并行版本,调用sc.parallelize(visitors),并输入visitors数据集。df_visitors然后为我们创建了一个访客的 DataFrame。然后,我们可以映射一个函数;例如,通过映射一个lambda函...
df_students = spark.createDataFrame(data = data, schema = columns) df_students.show() # repeating the column (student_name) twice and saving results in new column df_repeated = df_students.withColumn("student_name_repeated",(expr("repeat(student_name, 2)"))) df_repeated.show() 我们重复...
data = [("John", 25, None), ("Alice", None, [1, 2, 3]), ("Bob", 30, None)] df = spark.createDataFrame(data, ["name", "age", "array_column"]) df.show() 创建替换空值为空数组的UDF: 代码语言:txt 复制 def replace_null_with_empty_array(array_column): if array_column is...
df=spark.createDataFrame(data).toDF(*columns) df.printSchema() This yields below output, note the column name “languagesAtSchool” from the previous example. root |-- name: string (nullable = true) |-- languagesAtSchool: array (nullable = true) ...
Before diving into PySpark SQL Join illustrations, let’s initiate “emp” and “dept” DataFrames.The emp DataFrame contains the “emp_id” column with unique values, while the dept DataFrame contains the “dept_id” column with unique values. Additionally, the “emp_dept_id” from “emp”...
Spark provides many basic column operations:Select columns Create columns Rename columns Cast column types Remove columnsСавет To output all of the columns in a DataFrame, use columns, for example df_customer.columns.Select columnsYou can select specific columns using select and col. The col...
确保路径中包含Spark CSV(DataFrameReader,--jars,--driver-class-path) 并按如下方式加载数据: AI检测代码解析 (df = sqlContext .read.format("com.databricks.spark.csv") .option("header", "true") .option("inferschema", "true") .option("mode", "DROPMALFORMED") ...
可以看出,Spark DataFrame的数据结构是StructType([StructField(column_name, column_type)]) Spark需要提前指定好特征名称和特征类型,构建空的DataFrame,可以借助emptyRDD(),代码如下: from pyspark.sql.types import StructType, StructField, LongType, StringType data_schema = StructType([ StructField('id', Long...
12. 创建一个空的dataframe schema=StructType([StructField("列名1",StringType(),True),StructField("列名2",StringType(),True),StructField("列名3",StringType(),True),StructField("列名4",StringType(),True)])df_new=spark.createDataFrame(spark.sparkContext.emptyRDD(),schema) ...