I am working on Spark and I want to install some libraries and then use them for further steps. I want to install these libraries on data proc cluster but when I run this code, it fails with – module ‘numpy’ has no attribute ‘gcd’ Here is my entire code – import subprocess import sys ..
Category : pyspark
I have a column in Pyspark data frame with different values separated by blank space. I need to split the values and get some values in to different columns. The different values in the column always start with Same letter but The problem is the index number changes constantly and even length of these individual ..
I have multiple data frames with values that have been computed on different source data. For simplicity I’ll give an example with two dataframes but I’m looking for a solution with n dataframes data_1 +——+———–+ |person|first_value| +——+———–+ | 1| 1.0| | 2| 0.9| | 3| 0.8| | 4| 0.7| +——+———–+ data_2 +——+————+ |person|second_value| +——+————+ | ..
Aim : To update the values of column actv_ind to ‘0’ when another column in the same pyspark dataframe is == delete otherwise update value to ‘1’. All of the columns are string type in the dataframe. Current Approach : df.withColumn("actv_ind", F.when( (F.col(‘actv_ind’).isNull()) & (F.col(‘cdc_status’) == ‘delete’), F.lit(‘0’)) .otherwise(F.lit(‘1’))) This is not updating the actv_ind ..
I have a df1 Pyspark dataframe which is a product of a Pyspark Pivot. I’d like to join this df1 dataframe to the original Pyspark dataframe df2 (in order to get some of the columns that were lost during the pivot). Surprisingly when I do that, the sum of the column changes quite drastically. ‘HRS-COUNTED’ ..
I am a newbie to Python/Apache Spark, looking for the right IDE/Env in Windows to start learning. Here are my requirements, Interactive shell-like editor for writing python code for data analysis Auto-complete feature Apache Spark integration (like Pyspark) Any recommendation will be helpful, thanks. Source: Python-3x..
I have csv like that id,date,newdate 1,19930101,1011993 2,19930101,1011993 3,19930101,1011993 4,19930101,1011993 5,19930102,1011993 And also i have predefined schema: schema = StructType(List(StructField(account_id,LongType,true),StructField(date,TimestampType,true),StructField(newdate,TimestampType,true))) I need to find the way to read this csv and get dataframe with formatted timestamp columns. when I’m setting timestamp format for only one of columns it’s returning null for me and i have ..
pandas.cut() is used to bin values into discrete intervals. For instance, pd.cut( np.array([0.2, 0.25, 0.36, 0.55, 0.67, 0.78]), 3, include_lowest=True, right=False ) Out[9]: [[0.2, 0.393), [0.2, 0.393), [0.2, 0.393), [0.393, 0.587), [0.587, 0.781), [0.587, 0.781)] Categories (3, interval[float64]): [[0.2, 0.393) < [0.393, 0.587) < [0.587, 0.781)] How can I achieve the same in PySpark? I ..
Aim: To basically implement this query select *, case when new_x != x or new_y != y then ‘some_status_change’ else cdc_status end as cdc_status from dataframe where cdc_status = ‘noUpdateRequired’ I am trying to implement this logic using pyspark(3.0.0) and spark(2.4.4) and I currently have this df = df.withColumn("cdc_status", F.when(((F.col(‘cdc_status’) == ‘noUpdateRequired’) & (F.col(‘new_autoapproveind’) != ..
Currently im consuming xml data from a kafka source and process them with Spark Structured Streaming. To get the needed information out of the xml i am using xpath. As i want to make the pipeline more dynamic i tried to implement a dictionary which hold the column name to be extracted and the expression ..
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