AoM: Data Science

There's were a handful of resources describing how to count the number of null values in a pyspark dataframe. For example this Stack Overflow thread which asks for the number of null (and nan) values for each column in a pyspark dataframe. Thus given the total number of entries in the dataframe, one could indirectly compute the number of not null values (by taking the difference). However, I sought after a solution which would simultaneously output both the number of null and not null values (for a single column). Eventually, I wrote the following possible solution: ( df .select(df.MY_COLUMN_NAME.isNull().alias(MY_COLUMN_NAME)) .groupby(MY_COLUMN_NAME) .count() .show() ) In contrast, the following is the solution with just the count of null values: ( df .select(count(when(df.MY_COLUMN_NAME.isNull()).alias(MY_COLUMN_NAME)) .show() ) Of course the advantage of the latter was it's ability to summarize across every column: ( df .select([count(when(col(c)...

It was mentioned in passing in my previous post that I needed to setup `winutils.exe` in order to save my dataframe to a parquet file. It was not, however, mentioned why I wanted to save the dataframe as a parquet file. In short, the answer is for speed. First, let's load a dataset using pyspark, save it as a parquet file, load it back, and compare the time it takes to count the number of items in the file. Select your favorite medium-sized data file. I happened to be working with a CSV file that was about 7 GB, "data.csv" (composed of about 6.5 million rows and 300 columns). My data had a header, so I ran `df = spark.read.option("header", "true").csv("data.csv")` The default headings had characters not allowed by parquet when saving, so I looped through the columns and use regex to find and replace the set of illegal characters (" ,;{}()\n\t="): import re df2 = df for column in df.columns: new_column = re.sub(...

It seems like just about every six months I need to install PySpark and the experience is never the same. Note that this isn't necessarily the fault of Spark itself. Instead, it's a combination of the many different situations under which Spark can be installed, lack of official documentation for each and every such situation, and me not writing down the steps I took to successfully install it. So today, I decided to write down the steps needed to install the most recent version of PySpark under the conditions in which I currently need it: inside an Anaconda environment on Windows 10. Note that the page which best helped produce the following solution can be found here (Medium article). I later found a second page with similar instructions which can be found here (Towards Data Science article). Steps to Installing PySpark for use with Jupyter This solution assumes Anaconda is already installed, an environment named `test` has already been created, and Jupyter has already...