一、问题描述 将pandas的df转为spark的df时,spark.createDataFrame()报错如下: TypeError: field id: Can not merge type <class 'pyspark.sql.types.StringType'> and <class 'pyspark.sql.types.LongType'> 1. 二、 解决方法 是因为数据存在空值,需要将空值pd.NA替换为空字符串。 pandas_id = pandas_id....
spark_df = spark.createDataFrame(target_users) 报错->>Can not merge type <class 'pyspark.sql.types.DoubleType'> and <class 'pyspark.sql.types.StringType'> 根本原因:并非数据类型不匹配,而是数据中存在空值,将空值进行填充后成功创建。
df = spark.createDataFrame(data) type(df) Create DataFrame from RDD A typical event when working in Spark is to make a DataFrame from an existing RDD. Create a sample RDD and then convert it to a DataFrame. 1. Make a dictionary list containing toy data: data = [{"Category": 'A', ...
创建永久MaxCompute表 可以使用df.createOrReplaceTable直接创建一个永久MaxCompute表,然后使用spark.sql查询该表。 例如: python df.createOrReplaceTable("table_name") spark.sql("SELECT * FROM table_name").show() 2. 创建临时内存表 可以使用df.createOrReplaceTempView创建一个内存临时表,只在当前Spark会话期...
df = spark.createDataFrame(data, ["id","name"])# 过滤数据并收集结果filtered_data = df.filter(df.id >1).collect()# 打印过滤后的结果forrowinfiltered_data: print(row) 在这些示例中,collect函数用于将分布式数据收集到本地,并且我们可以在本地节点上进行进一步的操作或查看数据。但请谨慎使用collect,...
df = spark.createDataFrame(emptyRDD,schema) df.printSchema() This yields below schema of the empty DataFrame. root |-- firstname: string (nullable = true) |-- middlename: string (nullable = true) |-- lastname: string (nullable = true) ...
df1 = spark.createDataFrame(zip(names, ages), ["Name", "Age"]) df1.show() In the above code, zip() combines the elements of the “names” and “ages” lists into tuples. For example, [(“Ricky”, 10), (“Bunny”, 150), (“Coco”, 20)]. And spark calls the createDataFrame...
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df.write.format("orc").mode("overwrite").saveAsTable("database.table-name") When I create a Hive table through Spark, I am able to query the table from Spark but having issue while accessing table data through Hive. I get below error.Error: java.io.IOException: java.lang.IllegalAr...
df_final = spark.read.format("delta").load("Tables/churn_data_clean") # Train-Test Separation train_raw, test_raw = df_final.randomSplit([0.8, 0.2], seed=41) # Define the feature columns (excluding the target variable 'Exited') feature_cols = [col for col in df_final.columns if ...