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...
spark dataframe是immutable, 因此每次返回的都是一个新的dataframe (1)列操作 # add a new column data = data.withColumn("newCol",df.oldCol+1) # replace the old column data = data.withColumn("oldCol",newCol) # rename the column data.withColumnRenamed("oldName","newName") # change column ...
df.withColumn('address', translate('address','123','ABC')) \ .show(truncate=False)#Replace column with another columnfrompyspark.sql.functionsimportexpr df = spark.createDataFrame([("ABCDE_XYZ","XYZ","FGH")], ("col1","col2","col3")) df.withColumn("new_column", expr("regexp_repla...
DataFrame(columns=['idx', 'name']) for attr in temp['numeric']: temp_df = {} temp_df['idx'] = attr['idx'] temp_df['name'] = attr['name'] #print(temp_df) df_importance = df_importance.append(temp_df, ignore_index=True) #print(attr['idx'], attr['name']) #print(attr)...
93.pyspark.sql.functions.udf(f, returnType=StringType) 参考链接 github.com/QInzhengk/Math-Model-and-Machine-Learning 公众号:数学建模与人工智能 RDD和DataFrame 1.SparkSession 介绍 SparkSession 本质上是SparkConf、SparkContext、SQLContext、HiveContext和StreamingContext这些环境的集合,避免使用这些来分别执行配...
我有一个PySpark dataframe,如下所示。我需要将dataframe行折叠成包含column:value对的Python dictionary行。最后,将字典转换为Python list of tuples,如下所示。我使用的是Spark 2.4。DataFrame:>>> myDF.show() +---+---+---+---+ |fname |age|location | dob | +---+---+---+---+ | John|...
我在dataframe中有一列作为对象列表(结构数组),如 column: [{key1:value1}, {key2:value2}, {key3:value3}] 我想将此列拆分为单独的列,在同一行中键名作为列名,值作为列值。最终结果如 key1:value1, key2:value2, key3:value3 如何在pyspark中实现这一点?
上面的dataframe中有重复的行,需要找出来,并且删除掉。 # 查看去重前后的行数是否发生变化 print('Count of distinct rows:',df.distinct().count()) print('Count of rows:',df.count()) 1. 2. 3. Count of distinct rows: 4 ...
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(): schema = StructType([StructField('root', col_dtypes[column])]) ...
还有一种方法,是先把dataframe创建成一个临时表,再用hive sql的语句写入表的分区。 bike_change_2days.registerTempTable('bike_change_2days') sqlContext.sql("insert into bi.bike_changes_2days_a_d partition(dt='%s') select citycode,biketype,detain_bike_flag,bike_tag_onday,bike_tag_yesterday,bik...