During my graduate studies, I had the opportunity to work on a few research projects. I have used Python extensively in these projects. In this post, I will discuss six areas where Python has helped me. If you are doing research and love programming, these reasons may convince to start learning and using Python right away!
1. You want to process and analyze data
One of the key activities in research is collecting and analyzing data. Python has great libraries for processing and analyzing data. Whether you want to remove the anomalous data points, normalize the dataset, or fill in the missing points, Python has you covered. Two libraries often used for data processing and analysis are NumPy and Pandas. They are powerful and efficient. They come with a lot of built-in functions that can make your data analysis tasks less daunting!
2. You want to do statistics
Quantitative research often relies on statistical methods for drawing important conclusions. statsmodels is a Python library for statistics. Whether you want the vanilla descriptive statistics or a fancy multivariate regression model, this library can help you out!
3. You want to make beautiful plots
Presentation is everything. Once you have analyzed your data, you want to present them in different types of plots: histograms, bar plots, pie charts or others. Matplotlib is the most popular plotting library for Python. It even supports 3D plots. Furthermore, for statistical data visualization there is the seaborn library. It can help you create heat maps, correlation plots and so on.
4. You want to create machine learning models
Python’s greatest strength is in the area of machine learning. There are plenty of libraries that support Python in machine learning. If you want classical machine learning, you can start with scikit-learn. If you want deep learning, you can go with Pytorch, Tensorflow, or Keras. The application areas for machine learning are far too wide to cover here.
5. You want to work with images and computer vision
Python has great libraries for scientific image analysis and manipulation. Two libraries often used for image analysis and computer vision are OpenCV-Python and scikit-image. You can combine these libraries with deep learning models to do object recognition.
6. You want to automate those repetitive tasks
Finally, Python can be used as a scripting language to automate repetitive tasks. For example, if you need to do the same type of analysis on 1,000 images, a for-loop or a while-loop will save you many hours of frustration. Although it may take hours to setup the first image, the rest will be quicker!
Here, I have given you a quick overview about how you can start using Python as a researcher. There are plenty of Python libraries out there which are hosted on PyPI, Anaconda and other repositories. You can check out Github for many open-source projects based on Python. Great thing about using Python is that you do not have to re-invent the wheel. All you need is to start learning and using it right away!