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
@udf(returnType=StringType()) def convertCase(str): resStr="" arr = str.split(" ") for x in arr: resStr= resStr + x[0:1].upper() + x[1:len(x)] + " " return resStr df.withColumn("Cureated Name", convertCase(col("Name"))).show(truncate=False) 1. 2. 3. 4. 5. ...
功能:选择DataFrame中的指定列(通过传入参数进行指定) 语法: 可传递: · 可变参数的cols对象,cols对象可以是Column对象来指定列或者字符串列名来指定列 · List[Column]对象或者List[str]对象, 用来选择多个列 DSL - filter和where 功能:过滤DataFrame内的数据,返回一个过滤后的DataFrame 语法: df.filter() df.whe...
6.从pandas dataframe创建DataFrame import pandas as pd from pyspark.sql import SparkSession colors = ['white','green','yellow','red','brown','pink'] color_df=pd.DataFrame(colors,columns=['color']) color_df['length']=color_df['color'].apply(len) color_df=spark.createDataFrame(color_df...
功能:选择DataFrame中的指定列(通过传入参数进行指定) 语法: 可传递: ·可变参数的cols对象,cols对象可以是Column对象来指定列或者字符串列名来指定列 ·List[Column]对象或者List[str]对象, 用来选择多个列 网页链接 功能:过滤DataFrame内的数据,返回一个过滤后的DataFrame ...
解决toDF()跑出First 100 rows类型无法确定的异常,可以采用将Row内每个元素都统一转格式,或者判断格式处理的方法,解决包含None类型时转换成DataFrame出错的问题: @staticmethod def map_convert_none_to_str(row): dict_row = row.asDict() for key in dict_row: ...
"""Converts JSON columns to complex types Args: df: Spark dataframe col_dtypes (dict): dictionary of columns names and their datatype Returns: Spark dataframe """ selects = list() for column in df.columns: if column in col_dtypes.keys(): ...
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')) \ ...
对于DataFrame 接口,Python 层也同样提供了 SparkSession、DataFrame 对象,它们也都是对 Java 层接口的封装,这里不一一赘述。 4、Executor 端进程间通信和序列化 对于Spark 内置的算子,在 Python 中调用 RDD、DataFrame 的接口后,从上文可以看出会通过 JVM 去调用到 Scala 的接口,最后执行和直接使用 Scala 并无区别...
To drop multiple columns from a PySpark DataFrame, we can pass a list of column names to the .drop() method. We can do this in two ways: # Option 1: Passing the names as a list df_dropped = df.drop(["team", "player_position"]) # Option 2: Passing the names as separate argume...