From Prism to Python: Scaling data analysis

📰 Medium · Python

Scale data analysis from manual tools like Prism to Python for efficiency and automation, especially with large datasets

intermediate Published 15 Apr 2026
Action Steps
  1. Load data into a Python environment using libraries like Pandas
  2. Define a 4PL equation for curve fitting using libraries like SciPy
  3. Fit the data using the defined equation and interpret the parameters
  4. Automate the workflow using Python scripts to improve efficiency and reusability
  5. Explore additional libraries like Matplotlib for data visualization and Seaborn for statistical analysis
Who Needs to Know This

Data analysts and scientists can benefit from learning Python to automate workflows and improve data analysis efficiency, making it a valuable skill for teams working with large datasets

Key Insight

💡 Python can be used to automate data analysis workflows, improving efficiency and scalability, especially with large datasets

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Scale your data analysis with Python! Automate workflows and improve efficiency with large datasets #Python #DataAnalysis

Key Takeaways

Scale data analysis from manual tools like Prism to Python for efficiency and automation, especially with large datasets

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Title: From Prism to Python: Scaling data analysis

URL Source: https://medium.com/@sraashi11/from-prism-to-python-scaling-data-analysis-5804a573f716?source=rss------python-5

Published Time: 2026-04-15T00:46:32Z

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# From Prism to Python: Scaling data analysis | by Raashi Sreenivasan | Apr, 2026 | Medium

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# From Prism to Python: Scaling data analysis

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Early in my drug discovery life, I used graphpad prism extensively to plot and analyze data. It worked great for one curve at a time, but when you have 100+ curves? As the data got more complex, it was more error-prone. Prism is a wonderful tool, offers a great point and click version of data analysis, however offers significant disadvantages from my experience of working with large scale datasets— does not scale well for multiple data sheets/HTS experiments, and not amenable to automation.

So I turned to Python to work with data at scale. I have spent the last few months exploring ways to rebuild workflows that used to be performed manually and thereby make data analysis more efficient, faster and reusable — starting with something fundamental: automated 4 PL curvefitting for IC50/EC50 determination.

The pipeline is straight-forward: Load the data, Define the 4PL equation for fitting, Fit the data and Interpret the paramet
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