builder.getOrCreate() # 读取数据并创建DataFrame df = spark.read.csv("data.csv", header=True, inferSchema=True) # 获取列数据类型 column_types = df.dtypes # 遍历列数据类型列表 for column_name, data_type in column_types: print(f"列名: {column_name}, 数据类型: {data_type}")...
Column.getField(name: Any) → pyspark.sql.column.Column 1. 通过StructType中的名称获取字段的表达式。 df = spark.createDataFrame([Row(r=Row(a=1, b="b"))]) df.select(df.r.getField("b")).show() 1. 2. 和一下的这个索引是一样的效果: df.select(df.r.a).show() 1. 19.getItem获取...
复制 importpysparkfrompyspark.sqlimportSparkSessionfrompyspark.sql.typesimportStructType,StructField,StringType,IntegerType spark=SparkSession.builder.master("local[1]")\.appName('SparkByExamples.com')\.getOrCreate()data=[("James","","Smith","36636","M",3000),("Michael","Rose","","40288",...
from pyspark.sql.types import DoubleType, StringType, IntegerType, FloatType from pyspark.sql.types import StructField from pyspark.sql.types import StructType PYSPARK_SQL_TYPE_DICT = { int: IntegerType(), float: FloatType(), str: StringType() } # 生成RDD rdd = spark_session.sparkContext....
from pyspark.sql.typesimportStructType,StructField,StringType,IntegerType spark=SparkSession.builder.master("local[1]")\.appName('SparkByExamples.com')\.getOrCreate()data=[("James","","Smith","36636","M",3000),("Michael","Rose","","40288","M",4000),("Robert","","Williams","4211...
from pyspark.sql import DataFrame, SparkSessionimport pyspark.sql.types as Timport pandera.pyspark as paspark = SparkSession.builder.getOrCreate()class PanderaSchema(DataFrameModel): """Test schema""" id: T.IntegerType() = Field(gt=5) product_name: T.StringType() = Field(str_s...
new column name, expression for the new column 第3个问题(多选) Which of the following data types are incompatible with Null values calculations? Boolean Integer Timestamp String 第4 个问题 To remove a column containing NULL values, what is the cut-off of average number of NULL values beyond...
defarrow_to_pandas(self,arrow_column):frompyspark.sql.typesimport_check_series_localize_timestamps#Ifthegivencolumnisadatetypecolumn,createsaseriesofdatetime.datedirectly#insteadofcreatingdatetime64[ns]asintermediatedatatoavoidoverflowcausedby#datetime64[ns]typehandling.s=arrow_column.to_pandas(date_as_obj...
DataFrame column operations withcolumn select when Partitioning and lazy processing cache 计算时间 集群配置 json PYSPARK学习笔记 Defining a schema # Import the pyspark.sql.types library from pyspark.sql.types import * # Define a new schema using the StructType method people_schema = StructType([ # ...
from pyspark.sql import types as stdef ratio(a, b): if a is None or b is None or b == 0: r = -1.0 else: r = 1.0 * a / b return r col_ratio = udf(ratio, st.DoubleType()) df_udf = df.withColumn("ratio", col_ratio(df.age, df.height)) print df_udf.show() """...