📒 Notes

What is Python?

Python is simple, readable, and beginner-friendly. It is open-source and has a huge community. Python is widely used in data analysis, artificial intelligence, finance, healthcare, and pharmaceutical research.

Why Python for Data Analysis?

Python is a great fit for many types of pharma-related data work, for example:

• Clinical trial datasets (patients, drugs, side effects, outcomes).
• Market research (drug sales, competitor data, patient demographics).
• Drug safety and pharmacovigilance reports.

How Python Helps

Python helps because of its ecosystem of libraries:

• pandas → clean, organize, and manipulate data.
  • Work with structured data (tables, CSV, Excel, SQL).
  • Helps in cleaning, filtering, grouping, summarizing data.
• numpy → handle numbers & calculations.
  • Stands for Numerical Python.
  • Useful for mathematical/statistical operations, e.g., averages, standard deviation, matrix multiplications.
• matplotlib / seaborn → charts and visualization.
  • Matplotlib → Core plotting library (like drawing charts from scratch).
  • Seaborn → Higher-level statistical plots built on Matplotlib.

Use Cases in Pharma

• Sales comparison between drugs
• Adverse events reporting
• Clinical trial patient demographics

Automation & Integration

Automation: Instead of working manually in Excel, you can automate reports.
Integration: Python works with Excel, SQL databases, APIs, and even clinical software.

Basic Program

print("Hello Pharma World!")