pyspark: the number of entries in a column which are null or not

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).isNull()).alias(c) for c in df.columns])
  .show()
)

[20200813][20200923 Edit]

Comments

Post a Comment

Popular posts from this blog

PySpark + Anaconda + Jupyter (Windows)

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...
Read more

Using tensorflowjs_converter

A friend and I were looking to convert a Tensorflow model to a TensorFlow.js model. My friend had done some prior research and started me off with the following page: Importing a Keras model into TensorFlow.js . The first step, and one which I messed up on, is installing the `tensorflowjs` library into a Python environment: `pip install tensorflowjs`. Although I'm not sure on the reason for the mistake, the fact of the matter is that I ended up installing `tensorflow` (`pip install tensorflow`) and spent a good amount of time wondering why the bash command `tensorflowjs_converter` wasn't working. In any case, in the tutorial linked above, the command takes the following structure: # bash tensorflowjs_converter --input_format keras \ path/to/my_model.h5 \ path/to/tfjs_target_dir Meanwhile, the readme for the tfjs COCO-SSD model supplies a converter command for removing the post process graph as a way of improving performance ...
Read more