cache()同步数据的内存 columns 返回一个string类型的数组,返回值是所有列的名字 dtypes返回一个string类型的二维数组,返回值是所有列的名字以及类型 explan()打印执行计划 物理的 explain(n:Boolean) 输入值为 false 或者true ,返回值是unit 默认是false ,如果输入true 将会打印 逻辑的和物理的 isLocal 返回值是Bo...
意思是写txt文件时dataframe只能有一列,而且必须是string类型。 value = [("alice",), ("bob",)] df = spark.createDataFrame(value, schema="name: string") df.show() df = df.coalesce(1) df.write.text("data_txt") 3.写入json文件 df.write.json("data_json") # 或者 df.write.format("...
mySchema = StructType([StructField("V1", StringType(), True), StructField("V2", ArrayType(IntegerType(),True))]) df = spark.createDataFrame([['A', [1, 2, 3, 4, 5, 6, 7]], ['B', [8, 7, 6, 5, 4, 3, 2]]], schema= mySchema) # Split list into columns using 'ex...
首先进行数据切割。 # split the data into training and testing setstrain_data,test_data=transformed_data.randomSplit([0.8,0.2],seed=1234)print((train_data.count(),len(train_data.columns)))print((test_data.count(),len(test_data.columns)))[Out:](113,7)(37,7)## 这里切割的是上图中的数...
# 为给定数组或映射中的每个元素返回一个新行 from pyspark.sql.functions import split, explode df = sc.parallelize([(1, 2, 3, 'a b c'), (4, 5, 6, 'd e f'), (7, 8, 9, 'g h i')]) .toDF(['col1', 'col2', 'col3', 'col4']) df.withColumn('col4', explode(split(...
# Split the data into training and test sets 类似于train_test_splittraining,test=piped_data.randomSplit([.6,.4]) 逻辑回归 # Import LogisticRegressionfrom pyspark.ml.classification import LogisticRegression# Create a LogisticRegression Estimatorlr=LogisticRegression() ...
问pyspark线性回归模型给出错误此列名必须是数字类型，但实际上是字符串类型EN相关是随机理论的基础。田径...
df.printSchema(),df.columns root |-- Country: string (nullable = true) |-- Age: integer (nullable = true) |-- Repeat_Visitor: integer (nullable = true) |-- Platform: string (nullable = true) |-- Web_pages_viewed: integer (nullable = true) |-- Status: integer (nullable = true...
Jupyter Notebook 有两种键盘输入模式。编辑模式，允许你往单元中键入代码或文本；这时的单元框线是绿色的...
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