Introduction to Pandas

Topics: pandas, DataFrame, structured data

Pandas is the cornerstone of medical data analysis in Python. It allows you to organize, clean, and analyze structured data — similar to how you might handle patient records or hospital information in Excel or EHR systems.

With pandas, you can easily handle datasets containing patient demographics, lab results, or hospital statistics — making it an essential tool for clinical research, epidemiology, and medical audits.

Key Data Structures in pandas

Series – One-Dimensional Data

A Series is like a column in a spreadsheet — it holds a single type of data, such as patients’ ages or cholesterol levels.

Example: Creating a Series of patients’ ages

import pandas as pd

ages = pd.Series([34, 45, 29, 50, 41],
                 index=['Ana', 'Luis', 'Maria', 'Jose', 'Carmen'])

print(ages)
print(f"Data type: {ages.dtype}")
print(f"Shape: {ages.shape}")

# Basic operations
print(f"Mean age: {ages.mean()}")
print(f"Oldest patient: {ages.idxmax()}")

DataFrame – Two-Dimensional Data

A DataFrame is like a full spreadsheet (Like Microsoft Excel or Google Sheets) or a patient table — rows represent patients, and columns represent variables (e.g., age, diagnosis, or lab values).

Example: Creating a DataFrame of patient information

data = {
    'Patient_ID': [101, 102, 103, 104, 105],
    'Name': ['Ana', 'Luis', 'Maria', 'Jose', 'Carmen'],
    'Age': [34, 45, 29, 50, 41],
    'Diagnosis': ['Diabetes', 'Hypertension', 'Healthy', 'Diabetes', 'Hypertension'],
    'Cholesterol': [210, 190, 170, 250, 230]
}

df = pd.DataFrame(data)

Inspecting the DataFrame:

Let’s check its structure and summary. Some print functions to see what are the characteristics of data.

print(df)
print(f"DataFrame shape: {df.shape}")
print(f"Column names: {list(df.columns)}")
print(f"Data types:\n{df.dtypes}")

DataFrame Inspection Methods

Pandas provides several ways to quickly explore and summarize your dataset.

This code prints the 1st 3 rows of the DataFrame.

print(df.head(3))

This code prints the last 2 rows of the DataFrame.

print(df.tail(2))

This code prints the some information about the DataFrame.

print(df.info())

This code prints quick and simple descriptive statistics for the DataFrame.

print(df.describe())

This code prints column-specific information from a DataFrame.

print(df['Diagnosis'].value_counts())  # Frequency of each diagnosis
print(df['Cholesterol'].mean())        # Average cholesterol level

pandas is essential for managing structured datasets — just like a digital spreadsheet for clinical data.
A Series represents a single column of medical data (e.g., patient age or blood pressure).
A DataFrame is a full table of patient information (e.g., demographics, diagnoses, lab values).
You can inspect, summarize, and analyze datasets efficiently using pandas methods.
This skill is crucial for research, quality assurance, and data-driven healthcare decisions.

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