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}")...
data type is present in the data framefor x, y in datatypes_List: dict_count[y] = dict_count.get(y, 0) + 1# query dict_count to find the number of times a data type is present in data framedict_count ...
Column.like(other: Union[Column, LiteralType, DecimalLiteral, DateTimeLiteral]) → Column 1. 类似SQL的表达式。返回基于SQL LIKE匹配的布尔列。 sp_df.filter(sp_df.times.like('%08:00:00')).show() 1. 25.otherwise等于else Column.otherwise(value: Any) → pyspark.sql.column.Column 1. 计算条件...
pyspark dataframe Column alias 重命名列(name) df = spark.createDataFrame( [(2, "Alice"), (5, "Bob")], ["age", "name"])df.select(df.age.alias("age2")).show()+---+|age2|+---+| 2|| 5|+---+ astype alias cast 修改列类型 data.schemaStructType([StructField('name', String...
拿到pyspark dataframe的字段、类型、是否可以填充空值:df.schema.fields[0].name、df.schema.fields[0].dataType、df.schema.fields[0].nullable columns_type = dict() 统计空缺值: from pyspark.sql.functions import isnan,when,count,col null_dict = dict() for column in df.columns: print(column) ...
data.withColumnRenamed("oldName","newName") # change column data type data.withColumn("oldColumn", data.oldColumn.cast("integer")) (2)条件筛选数据 # filter data by pass a string temp1 = data.filter("col > 1000") # filter data by pass a column of boolean value ...
type_mapping = { "column1": IntegerType(), "column2": StringType(), "column3": DoubleType() } 这里以三个列为例,你可以根据实际情况进行扩展。 使用函数withColumn()和cast()来重新转换列类型: 代码语言:txt 复制 for column, data_type in type_mapping.items(): df = df.withColumn(column, ...
Column(name='gender', description='??', dataType='string', nullable=True, isPartition=False, isBucket=False)] listDatabases 获取数据库列表 data1 = spark.catalog.listDatabases()print(data1)>>[Database(name='default', catalog='spark_catalog', description='Default Hive database', locationUri=...
StructField('B',ArrayType(elementType=IntegerType())), StructField('C', DecimalType())]) spark=SparkSession.builder.appName("jsonRDD").getOrCreate() df=spark.createDataFrame(data,schema) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Spark SQL和DataFrames重要的类有: pyspark.sql.SQLContext DataFrame和SQL方法的主入口 pyspark.sql.DataFrame 将分布式数据集分组到指定列名的数据框中 pyspark.sql.Column DataFrame中的列 pyspark.sql.Row DataFrame数据的行 pyspark.sql.HiveContext 访问Hive数据的主入口 ...