Pandas Series & DataFrame Explained for Pharmacy Students (Patient Datasets & PK Tables)

Pandas provides two powerful data structures: Series and DataFrame. These are widely used in pharmaceutical sciences for handling patient datasets, pharmacokinetic (PK) tables, and ADR reports.

🔷 What is a Pandas Series?

A Series is a one-dimensional labeled array (similar to a single column in a table).

import pandas as pd

dose = pd.Series([500, 650, 400])
print(dose)

💊 Pharma Example (Dose Data)

doses = pd.Series([500, 650, 700], index=["P1", "P2", "P3"])
print(doses)

🔷 What is a DataFrame?

A DataFrame is a two-dimensional table consisting of rows and columns.

data = {
    "Patient": ["P1", "P2"],
    "Dose": [500, 650]
}

df = pd.DataFrame(data)
print(df)

💊 Pharmacokinetic (PK) Table Example

data = {
    "Time": [0, 1, 2, 3],
    "Concentration": [0, 5.2, 4.1, 2.8]
}

pk_df = pd.DataFrame(data)
print(pk_df)

Used to analyze drug concentration over time.

🔷 Accessing Data in DataFrame

📘 Column Access

print(df["Dose"])

📘 Row Access

print(df.iloc[0])

🔷 Basic Operations

📘 Mean Dose

print(df["Dose"].mean())

📘 Maximum Dose

print(df["Dose"].max())

🔷 Series vs DataFrame

🧠 Memory Tricks

• Series → Single column
• DataFrame → Table
• iloc → Row access

🧪 Practice Exercise

Create a DataFrame and:

• Store patient names
• Store doses
• Calculate average dose

🧪 Mini Project

Create PK dataset:

import pandas as pd

data = {
    "Time": [0, 1, 2, 3],
    "Conc": [0, 5, 4, 3]
}

df = pd.DataFrame(data)

print("Average Conc:", df["Conc"].mean())

📝 MCQs

1. Series is:
   a) 2D
   b) 1D
   c) 3D
   d) None
   Answer: b


2. DataFrame is:
   a) List
   b) Table
   c) String
   d) Loop
   Answer: b


3. iloc is used for:
   a) Column access
   b) Row access
   c) File read
   d) Calculation
   Answer: b

❓ FAQs

Where is DataFrame used in pharmacy?

In patient datasets, PK studies, and ADR analysis.

What is difference between Series and DataFrame?

Series is 1D, DataFrame is 2D table.

➡ Next Topic: Reading CSV & Excel Files using Pandas →