There is ahidden cost of withColumnand calling it multiple times should be avoided. The Spark contributors areconsidering adding withColumns to the API, which would be the best option. That'd give the community
withColumnRenamed(existing, new) Returns a new DataFrame by renaming an existing column. 列名修改 withColumns(*colsMap) Returns a new DataFrame by adding multiple columns or replacing the existing columns that has the same names. 添加或替换多列 withMetadata(columnName, metadata) Returns a new Dat...
while.withColumn()returns all the columns of the DataFrame in addition to the one you defined. It's often a good idea to drop columns you don't need at the beginning of an operation so that you're not dragging around extra data as you're wrangling. In this case,...
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raw = raw.withColumn(labelCol, raw[labelCol].cast(IntegerType())) #withColumn(colName:String,col:Column):添加列或者替换具有相同名字的列,返回新的DataFrame。 assembler = VectorAssembler(inputCols=vecCols, outputCol="features", handleInvalid="keep") # VectorIndexer 之前介绍的StringIndexer是针对单个类...
# Add the new columns to `df`housing_df=(housing_df.withColumn("rmsperhh",F.round(col("totrooms")/col("houshlds"),2)).withColumn("popperhh",F.round(col("pop")/col("houshlds"),2)).withColumn("bdrmsperrm",F.round(col("totbdrms")/col("totrooms"),2))) ...
Recursively drop multiple fields at any nested level. from nestedfunctions.functions.drop import drop dropped_df = drop( df, fields_to_drop=[ "root_column.child1.grand_child2", "root_column.child2", "other_root_column", ] ) Duplicate Duplicate the nested field column_to_duplicate as dupli...
()) # 进来一个Value,出去一个Grade # 添加列 group2017 = data2017.withColumn("Grade",grade_function_udf(data2017['Value'])).groupBy("Grade").count() group2016 = data2016.withColumn("Grade",grade_function_udf(data2016['Value'])).groupBy("Grade").count() group2015 = data2015.withColumn...
df=df.withColumn(\"c1\",lit(\"1\")) df.show() df.coalesce(1).write.mode(\"overwrite\").option(\"header\", \"true\").format(\"csv\").save(\"wasbs://<container_name>@<storage_account_name>.blob.core.windows.net/<path_to_write_csv>\")...
select("ip","count").\# 选择保留列名filter(~col("ip").isin(["localhost","127.0.0.1"])).\# 过滤ip在数组中的行drop_duplicates(subset=["ip"]).\# 删除ip列中重复数据的行withColumn("block_impact",udf_count("count")).\# 创建新列block_impact,填充值为udf函数处理count列数据后的对应返回...