PySpark的DataFrame是基于RDD(弹性分布式数据集)的,但为了创建一个空的DataFrame,我们可以使用spark.sparkContext.emptyRDD()来创建一个空的RDD,或者简单地使用一个空列表。由于DataFrame的创建通常需要指定Schema(即列名和类型),所以空的RDD是更常用的选择。 3. 使用数据源创建空的
schema="name: string, age: int"df = spark.createDataFrame(spark.sparkContext.emptyRDD(),schema)df.show()df.printSchema() 或 点击查看代码 frompyspark.sql.typesimport* schema = StructType([ StructField("name", StringType(),False), StructField("age", IntegerType(),False)]) df = spark.cr...
云朵君将和大家一起学习如何从 PySpark DataFrame 编写 Parquet 文件并将 Parquet 文件读取到 DataFrame ...
Create a DataFrame from a JSON responseTo create a DataFrame from a JSON response payload returned by a REST API, use the Python requests package to query and parse the response. You must import the package to use it. This example uses data from the United States Food and Drug ...
from pyspark.sql.types import StructType, StructField, LongType, StringType data_schema = StructType([ StructField('id', LongType()), StructField('type', StringType()), ]) df = spark.createDataFrame(spark.sparkContext.emptyRDD(), schema=data_schema) df.show() ...
PySpark - DataFrame的基本操作 连接spark 1、添加数据 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) ...
# 创建一个空的DataFrame df = spark.createDataFrame(spark.sparkContext.emptyRDD(), schema) # 逐个读取csv文件并将其添加到DataFrame中 for folder in folders: folder_path = "/path/to/" + folder file_path = folder_path + "/*.csv"
先通过pandas构建一个dataframe(具体可参考pandas的dataframe),然后再通过这个pandas的dataframe构建spark的dataframe,如下所示: import pandas as pd df_pd = pd.DataFrame([('Alice', 18), ('Bob', 19)]) df = spark.createDataFrame(df_pd) df.show() ...
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()...
df = spark.createDataFrame( [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])df.limit(1).show()+---+---+|age|name|+---+---+| 14| Tom|+---+---+df.limit(0).show()+---+---+|age|name|+---+---++---+---+ mapInPandas 迭代处理 使用pandas ...