Selects column based on the column name specified as a regex and returns it as Column. 选择符合正则表达式的列 collect() Returns all the records as a list of Row. 将所有记录作为 Row 列表返回。 corr(col1, col2[, method]) Calculates the correlation of two columns of a DataFrame as a do...
"origin", "dest") # Select the second set of columns temp = flights.select(flights.origin, flights.dest, flights.carrier) #这个列名的选择很像R里面的 # Define first filter filterA = flights.origin == "SEA" # Define second filter filterB = flights.dest == "PDX" # Filter the data, f...
You can also select based on an array of column objects: df.select([col("age")]).show() +---+ |age| +---+ | 1| | 2| | 3| +---+ Keep reading to see how selecting on an array of column object allows for advanced use cases, like renaming columns. withColumn basic use case...
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,bike_num from bike_change_2days"%(date)) 写入集群非分区表 1 df_spark.write.mode("append").insertInto('bi.pesudo_bike_white_list') ...
sparkDF.columns:将列名打印出来 Top~~ 3、选择列 【select函数,原pandas中没有】 sparkDF.select('列名1','列名2‘).show():选择dataframe的两列数据显示出来 sparkDF.select ( sparkDF['列名1']+1 , '列名2' ).show():直接对列1进行操作(值+1)打印出来 ...
df = df.select([F.to_json(F.struct(c)).alias(c) for c in df.columns]) df = df.select(F.array_join(F.array([F.translate(c, '{}', '') for c in df.columns]), '; ').alias('a')) result = [(table_nm, '; '.join(col_list), r.a) for r in df.collect()] # [...
df.select(df.age+1,'age','name') df.select(F.lit(0).alias('id'),'age','name') 增加行 df.unionAll(df2) 删除重复记录 df.drop_duplicates() 去重 df.distinct() 删除列 df.drop('id') 删除存在缺失值的记录 df.dropna(subset=['age', 'name']) # 传入一个list,删除指定字段中存在缺失...
pyspark 冰山架构不合并缺失的列根据文件：编写器必须启用mergeSchema选项。第一个月 这在目前的spark.sql...
# Split the RDD based on tab rdd_split = clusterRDD.map(lambda x: x.split('\t')) # Transform the split RDD by creating a list of integers rdd_split_int = rdd_split.map(lambda x: [int(x[0]), int(x[1])]) # Count the number of rows in RDD ...
pyspark 克服数据集之间的架构/列不一致性你可以使用标准的SQL特性-CASE..WHEN如下：