pyspark

问题 Table A has many columns with a date column, Table B has a datetime and a value. The data in both tables are generated sporadically with no regular interval. Table A is small, table B is massive. I need to join B to A under the condition that a given element a of A.datetime corresponds to B[B['datetime'] <= a]]['datetime'].max() There are a couple ways to do this, but I would like the most efficient way. Option 1 Broadcast the small dataset as a Pandas DataFrame. Set up a Spark UDF that

问题 I want to add a new column to the dataframe with values consist of either 0 or 1. I used 'randint' function from, from random import randint df1 = df.withColumn('isVal',randint(0,1)) But I get the following error, /spark/python/pyspark/sql/dataframe.py", line 1313, in withColumn assert isinstance(col, Column), "col should be Column" AssertionError: col should be Column how to use a custom function or randint function for generate random value for the column? 回答1: You are using python builtin

问题 I want to add a new column to the dataframe with values consist of either 0 or 1. I used 'randint' function from, from random import randint df1 = df.withColumn('isVal',randint(0,1)) But I get the following error, /spark/python/pyspark/sql/dataframe.py", line 1313, in withColumn assert isinstance(col, Column), "col should be Column" AssertionError: col should be Column how to use a custom function or randint function for generate random value for the column? 回答1: You are using python builtin

问题 I have a dataframe resulting from a sql query df1 = sqlContext.sql("select * from table_test") I need to convert this dataframe to libsvm format so that it can be provided as an input for pyspark.ml.classification.LogisticRegression I tried to do the following. However, this resulted in the following error as I'm using spark 1.5.2 df1.write.format("libsvm").save("data/foo") Failed to load class for data source: libsvm I wanted to use MLUtils.loadLibSVMFile instead. I'm behind a firewall and

问题 I have a dataframe resulting from a sql query df1 = sqlContext.sql("select * from table_test") I need to convert this dataframe to libsvm format so that it can be provided as an input for pyspark.ml.classification.LogisticRegression I tried to do the following. However, this resulted in the following error as I'm using spark 1.5.2 df1.write.format("libsvm").save("data/foo") Failed to load class for data source: libsvm I wanted to use MLUtils.loadLibSVMFile instead. I'm behind a firewall and

问题 I have a dataframe resulting from a sql query df1 = sqlContext.sql("select * from table_test") I need to convert this dataframe to libsvm format so that it can be provided as an input for pyspark.ml.classification.LogisticRegression I tried to do the following. However, this resulted in the following error as I'm using spark 1.5.2 df1.write.format("libsvm").save("data/foo") Failed to load class for data source: libsvm I wanted to use MLUtils.loadLibSVMFile instead. I'm behind a firewall and

问题 I have a dataframe resulting from a sql query df1 = sqlContext.sql("select * from table_test") I need to convert this dataframe to libsvm format so that it can be provided as an input for pyspark.ml.classification.LogisticRegression I tried to do the following. However, this resulted in the following error as I'm using spark 1.5.2 df1.write.format("libsvm").save("data/foo") Failed to load class for data source: libsvm I wanted to use MLUtils.loadLibSVMFile instead. I'm behind a firewall and

问题 I am currently developing my first whole system using PySpark and I am running into some strange, memory-related issues. In one of the stages, I would like to resemble a Split-Apply-Combine strategy in order to modify a DataFrame. That is, I would like to apply a function to each of the groups defined by a given column and finally combine them all. Problem is, the function I want to apply is a prediction method for a fitted model that "speaks" the Pandas idiom, i.e., it is vectorized and

问题 I am currently developing my first whole system using PySpark and I am running into some strange, memory-related issues. In one of the stages, I would like to resemble a Split-Apply-Combine strategy in order to modify a DataFrame. That is, I would like to apply a function to each of the groups defined by a given column and finally combine them all. Problem is, the function I want to apply is a prediction method for a fitted model that "speaks" the Pandas idiom, i.e., it is vectorized and

问题 I have a spark dataframe ( prof_student_df ) that lists student/professor pair for a timestamp. There are 4 professors and 4 students for each timestamp and each professor-student pair has a “score” (so there are 16 rows per time frame). For each time frame, I need to find the one to one pairing between professors/students that maximizes the overall score. Each professor can only be matched with one student for a single time frame. For example, here are the pairings/scores for one time frame.