I have a very large dataset that is loaded in Hive. It consists of about 1.9 million rows and 1450 columns. I need to determine the “coverage” of each of the columns, meaning, the fraction of rows that have non-NaN values for each column.
According to this
I’m trying to install Spark on my Mac. I’ve used home-brew to install spark 2.4.0 and Scala. I’ve installed PySpark in my anaconda environment and am using PyCharm for development. I’ve exported to my bash profile:
I would like to transform from a DataFrame that contains lists of words into a DataFrame with each word in its own row.
I’m having a problem with using Python on Spark. My application has some dependencies, such as numpy, pandas, astropy, etc. I cannot use virtualenv to create an environment with all dependencies, since the nodes on the cluster do not have any common mountpoint or filesystem, besides HDFS. Therefore I am stuck with using spark-submit --py-files. I package the contents of site-packages in a ZIP file and submit the job like with --py-files=dependencies.zip option (as suggested in Easiest way to install Python dependencies on Spark executor nodes?). However, the nodes on cluster still do not seem to see the modules inside and they throw ImportError such as this when importing numpy.
I am trying to create a new column of lists in Pyspark using a groupby aggregation on existing set of columns. An example input data frame is provided below:
This is a copy of someone else’s question on another forum that was never answered, so I thought I’d re-ask it here, as I have the same issue. (See http://geekple.com/blogs/feeds/Xgzu7/posts/351703064084736)
There’s a DataFrame in pyspark with data as below:
I am using Spark 1.3.1 (PySpark) and I have generated a table using a SQL query. I now have an object that is a DataFrame. I want to export this DataFrame object (I have called it “table”) to a csv file so I can manipulate it and plot the columns. How do I export the DataFrame “table” to a csv file?
We are using the PySpark libraries interfacing with Spark 1.3.1.