return newrow # convert ratings dataframe to RDD ratings_rdd = ratings.rdd # apply our function to RDD ratings_rdd_new = ratings_rdd.map(lambda row: rowwise_function(row)) # Convert RDD Back to DataFrame ratings
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...
defrowwise_function(row):# convert row to dict:row_dict = row.asDict()# Add a new key in the dictionary with the new column name and value.row_dict['Newcol'] = math.exp(row_dict['rating'])# convert dict to row:newrow = Row(**row_dict)# return new rowreturn newrow # convert...
createDataFrame(data, schema=['id', 'date']) >>> df.show() +---+---+ | id| date| +---+---+ | 1|2016-12-31| | 2|2016-01-01| | 3|2016-01-02| | 4|2016-01-03| | 5|2016-01-04| +---+---+ >>> df.withColumn("new_column",expr("date_add(date,id)"))....
cols –listof new column names (string)# 返回具有新指定列名的DataFramedf.toDF('f1','f2') DF与RDD互换 rdd_df = df.rdd# DF转RDDdf = rdd_df.toDF()# RDD转DF DF和Pandas互换 pandas_df = spark_df.toPandas() spark_df = sqlContext.createDataFrame(pandas_df) ...
Spark 中DataFrame数据的行转列需要用到Spark中的Pivot(透视),简单来说将用行Row形式的保存的数据转换为列Column形式的数据叫做透视;反之叫做逆透视。pivot算子在org.apache.spark.sql.RelationalGroupedDataset类中,主要有如下6个重载的方法,查看这个方法源码的注释,我们可以看到这个方法是在Spark 1.6.0开始引入的(前4...
DataFrame(pd.read_excel(excelFile)) engine =create_engine('mysql+pymysql://root:123456@localhost:3306/test') df.to_sql(table_name, con=engine, if_exists='replace', index=False) 2.3 读取数据库的数据表 从数据库中读取表数据进行操作~ 如果你本来就有数据库表,那上面两步都可以省略,直接进入这...
df = spark.createDataFrame(address,["id","address","state"]) df.show() 2.Use Regular expression to replace String Column Value #Replace part of string with another stringfrompyspark.sql.functionsimportregexp_replace df.withColumn('address', regexp_replace('address','Rd','Road')) \ ...
在PySpark 中绘制一个简单的数据框(DataFrame)通常涉及以下几个步骤: ### 基础概念 PySpark 是 Apache Spark 的 Python API,它允许你在分布式集群...
In PySpark, we can drop one or more columns from a DataFrame using the .drop("column_name") method for a single column or .drop(["column1", "column2", ...]) for multiple columns.