TOPIC: PYTHON
Getting custom Python imports to work in Visual Studio Code
18th February 2022While I continue to use Spyder as my preferred Python code editor, I also tried out Visual Studio Code. Handily, this Integrated Development Environment also has facilities for working with R and Julia code as well as Markdown text editing and adding the required extensions is enough for these applications; it helps that there is an unofficial Grammarly extension for content creation.
My Python code development makes use of the Pylance extension, and it works a little differently from Spyder when it comes to including files using import statements. Spyder will look into the folder where the base script is located, but the default behaviour of Pylance is that it looks in the root path of your workspace. This meant that any code that ran successfully in Spyder failed in Visual Studio Code.
To solve this issue, I added the location using the python.analysis.extraPaths setting for the workspace. I opened Settings by going to File > Preferences > Settings in the menu. I typed python.analysis.extraPaths in the search box. This showed me the correct section. I clicked on Add Item, entered the required path, and clicked OK. This resolved the problem, and everything worked properly afterwards.
Broadening data science horizons: Useful Python packages for working with data
14th October 2021My response to changes in the technology stack used in clinical research is to develop some familiarity with programming and scripting platforms that complement and compete with SAS, a system with which I have been programming since 2000. While one of these has been R, Python is another that has taken up my attention, and I now also have Julia in my sights as well. There may be others to assess in the fullness of time.
While I first started to explore the Data Science world in the autumn of 2017, it was in the autumn of 2019 that I began to complete LinkedIn training courses on the subject. Good though they were, I find that I need to actually use a tool to better understand it. At that time, I did get to hear about Python packages like Pandas, NumPy, SciPy, Scikit-learn, Matplotlib, Seaborn and Beautiful Soup though it took until of spring of this year for me to start gaining some hands-on experience with using any of these.
During the summer of 2020, I attended a BCS webinar on the CodeGrades initiative, a programming mentoring scheme inspired by the way classical musicianship is assessed. In fact, one of the main progenitors is a trained classical musician and teacher of classical music who turned to Python programming when starting a family to have a more stable income. The approach is that a student selects a project and works their way through it, with mentoring and periodic assessments carried out in a gentle and discursive manner. Of course, the project has to be engaging for the learning experience to stay the course, and that point came through in the webinar.
That is one lesson that resonates with me with subjects as diverse as web server performance and the ongoing pandemic supplying data, and there are other sources of public data to examine as well before looking through my own personal archive gathered over the decades. Though some subjects are uplifting while others are more foreboding, the key thing is that they sustain interest and offer opportunities for new learning. Without being able to dream up new things to try, my knowledge of R and Python would not be as extensive as it is, and I hope that it will help with learning Julia too.
In the main, my own learning has been a solo effort with consultation of documentation along with web searches that have brought me to the likes of Real Python, Stack Abuse, Data Viz with Python and R and others for longer tutorials as well as threads on Stack Overflow. Usually, the web searching begins when I need a steer on a particular or a way to resolve a particular error or warning message, but books are always worth reading even if that is the slower route. While those from the Dummies series or from O'Reilly have proved must useful so far, I do need to read them more completely than I already have; it is all too tempting to go with the try the "programming and search for solutions as you go" approach instead.
To get going, many choose the Anaconda distribution to get Jupyter notebook functionality, but I prefer a more traditional editor, so Spyder has been my tool of choice for Python programming and there are others like PyCharm as well. Because Spyder itself is written in Python, it can be installed using pip from PyPi like other Python packages. It has other dependencies like Pylint for code management activities, but these get installed behind the scenes.
The packages that I first met in 2019 may be the mainstays for doing data science, but I have discovered others since then. It also seems that there is porosity between the worlds of R and Python, so you get some Python packages aping R packages and R has the Reticulate package for executing Python code. There are Python counterparts to such Tidyverse stables as dplyr and ggplot2 in the form of Siuba and Plotnine, respectively. Though the syntax of these packages are not direct copies of what is executed in R, they are close enough for there to be enough familiarity for added user-friendliness compared to Pandas or Matplotlib. The interoperability does not stop there, for there is SQLAlchemy for connecting to MySQL and other databases (PyMySQL is needed as well) and there also is SASPy for interacting with SAS Viya.
While Python may not have the speed of Julia, there are plenty of packages for working with larger workloads. Of these, Dask, Modin and RAPIDS all have their uses for dealing with data volumes that make Pandas code crawl. As if to prove that there are plenty of libraries for various forms of data analytics, data science, artificial intelligence and machine learning, there also are the likes of Keras, TensorFlow and NetworkX. These are just a selection of what is available, and there is always the possibility of checking out others. It may be tempting to stick with the most popular packages all the time, especially when they do so much, but it never hurts to keep an open mind either.
Expanding the coding toolkit: Adding R and Python in a changing landscape
10th April 2021Over the years, I have taught myself a number of computing languages, with some coming in useful for professional work while others came in handy for website development and maintenance. The collection has grown to include HTML, CSS, XML, Perl, PHP and UNIX Shell Scripting. The ongoing pandemic allowed to me add two more to the repertoire: R and Python.
My interest in these arose from my work as an information professional concerned with standardisation and automation of statistical results delivery. To date, the main focus has been on clinical study data, but ongoing changes in the life sciences sector could mean that I may need to look further afield, so having extra knowledge never hurts. Though I have been a SAS programmer for more than twenty years, its predominance in the clinical research field is not what it was, which causes me to rethink things.
As it happens, I would like to continue working with SAS since it does so much and thoughts of leaving it after me bring sadness. It also helps to know what the alternatives might be and to reject some management hopes about any newcomers, especially regarding the amount of code being produced and the quality of graphs being created. Use cases need to be assessed dispassionately, even when emotions loom behind the scenes.
Since both R and Python bring large scripting ecosystems with active communities, the attraction of their adoption makes a deal of sense. SAS is comparable in the scale of its own ecosystem, though there are considerable differences and the platform is catching up when it comes to Data Science. While the aforementioned open-source languages may have had a head start, it appears that others are not standing still either. It is a time to have wider awareness, and online conference attendance helps with that.
The breadth of what is available for any programming language more than stymies any attempt to create a truly all encompassing starting point, and I have abandoned thoughts of doing anything like that for R. Similarly, I will not even try such a thing for Python. Consequently, this means that my sharing of anything learned will be in the form of discrete postings from time to time, especially given ho easy it is to collect numerous website links for sharing.
The learning has been facilitated by ongoing pandemic restrictions, though things are opening up a little now. The pandemic also has given us public data that can be used for practice, since much can be gained from having one's own project instead of completing exercises from a book. Having an interesting data set with which to work is a must, and COVID-19 data contain a certain self-interest as well, while one remains mindful of the suffering and loss of life that has been happening since the pandemic first took hold.