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_new_df = sqlContext.createDataFrame(ratings_rdd_new) ratings_new_df.show()...
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...
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)"))....
1 DataFrame数据的行转列 1.1 需求 在做数据处理时我们可能会经常用到Apache Spark的 DataFrame来对数据进行处理,需要将行数据转成列数据来处理,例如一些指标数据一般会保存在KV类型数据库,根据几个字段作为key,将计算指标作为value保存起来,这样多个用户多个指标就会形成一个窄表,我们在使用这个数据时又希望按照每个用...
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) ...
toDF(*cols) Parameters: cols – list of new column names (string) # 返回具有新指定列名的DataFrame df.toDF('f1', 'f2') 1. 2. 3. 4. 5. 6. DF与RDD互换 rdd_df = df.rdd # DF转RDD df = rdd_df.toDF() # RDD转DF 1. 2. DF和Pandas互换 pandas_df = spark_df.toPandas() spark...
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')) \ ...
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...
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.
'] color_df=pd.DataFrame(colors,columns=['color']) color_df['length']=color_df['color'].apply(len) color_df...# ['color', 'length'] # 查看行数,和pandas不一样 color_df...