Inspecting Data in Pandas for Pharmacy Students (head(), tail(), info(), describe())

Before analyzing any dataset, it is essential to inspect it. In pharmaceutical sciences, data inspection helps understand patient datasets, ADR reports, and pharmacokinetic (PK) data.

🔷 Why Inspect Data?

• Understand dataset structure
• Identify missing values
• Get statistical insights

🔷 head() Function

Displays first 5 rows of dataset.

import pandas as pd

df = pd.read_csv("adr_data.csv")
print(df.head())

👉 Useful for quick preview of dataset.

🔷 tail() Function

Displays last 5 rows.

print(df.tail())

🔷 info() Function

Provides dataset summary.

df.info()

• Number of rows
• Column names
• Data types
• Missing values

🔷 describe() Function

Provides statistical summary.

df.describe()

Includes:

• Mean
• Standard deviation
• Minimum and maximum values

💊 Pharma Dataset Example

df = pd.read_csv("adr_data.csv")

print(df.head())
print(df.info())
print(df.describe())

👉 Helps understand ADR patterns and dose statistics.

🧠 Memory Tricks

• head() → Top rows
• tail() → Bottom rows
• info() → Structure
• describe() → Statistics

🧪 Practice Exercise

Load dataset and:

• Display first 3 rows
• Check dataset info
• Find average dose

🧪 Mini Project

Analyze PK dataset:

import pandas as pd

df = pd.read_csv("pk_data.csv")

print(df.head())
print(df.describe())

📝 MCQs

1. head() shows:
   a) Last rows
   b) First rows
   c) Middle rows
   d) All rows
   Answer: b


2. info() provides:
   a) Values
   b) Structure
   c) Graph
   d) Output
   Answer: b


3. describe() shows:
   a) Names
   b) Statistics
   c) Rows
   d) Columns
   Answer: b

❓ FAQs

Why inspect data before analysis?

To understand structure, errors, and missing values.

Which function shows statistics?

describe() is used for statistical summary.

➡ Next Topic: Data Cleaning & Handling Missing Values →