Data Analysis with Python

Topics: introduction, data analysis, statistics, modules

Welcome to the exciting world of data analysis!

This section will help you move beyond basic Python programming and show you how to turn health and clinical data into meaningful insights that can inform patient care, research, and medical education.

You’ll learn to use powerful Python libraries such as:

  • pandas — for cleaning and analyzing patient datasets
  • matplotlib and seaborn — for creating clear, evidence-based visualizations
  • scipy — for performing statistical tests and clinical comparisons

By the end of this section, you’ll be able to:

  • Load and explore datasets from sources such as hospital records or surveys
  • Clean and transform messy data (e.g., missing values in lab tests)
  • Visualize patterns such as blood pressure trends or lab result distributions
  • Perform basic statistical analyses to support clinical and research conclusions

Introduction to Data Analysis Libraries

Essential libraries

Before diving into analysis, let’s explore the core Python libraries that make data analysis possible — and how they apply to the medical field.

import pandas as pd              # Data manipulation and analysis
import numpy as np               # Numerical computing
import matplotlib.pyplot as plt  # Basic plotting
import seaborn as sns            # Statistical data visualization
from scipy import stats          # Statistical functions
1. pandas — For Data Manipulation

What it does: Handles tabular data (like Excel sheets or patient records).

You can filter, summarize, and transform data easily.

import pandas as pd

# Example patient dataset
data = {'Name': ['Ana', 'Luis', 'Maria'],
        'Age': [34, 45, 29],
        'Blood_Pressure': [120, 135, 110]}
df = pd.DataFrame(data)

# View average blood pressure
print("Average BP:", df['Blood_Pressure'].mean())

💡Use case: Compute the average blood pressure of patients in a study.

2. numpy — For Numerical Calculations

What it does: Performs fast mathematical operations on arrays and matrices.

import numpy as np

# Systolic blood pressure readings
bp = np.array([120, 130, 110, 125, 140])

# Compute mean and standard deviation
print("Mean BP:", np.mean(bp))
print("SD BP:", np.std(bp))

💡Use case: Analyze variability in blood pressure readings from a clinical trial.

3. matplotlib — For Basic Visualization

What it does: Creates simple plots and charts.

import matplotlib.pyplot as plt

ages = [23, 45, 34, 50, 29]
plt.hist(ages, bins=5)
plt.title("Age Distribution of Study Participants")
plt.xlabel("Age")
plt.ylabel("Count")
plt.show()

💡 Use case: Visualize the age distribution of your study participants.

4. seaborn — For Statistical Data Visualization

What it does: Makes elegant and easy statistical plots.

import seaborn as sns
import pandas as pd

df = pd.DataFrame({
    'Gender': ['Male', 'Female', 'Female', 'Male'],
    'Cholesterol': [190, 170, 210, 200]
})

sns.boxplot(x='Gender', y='Cholesterol', data=df)
plt.title("Cholesterol Levels by Gender")
plt.show()

💡 Use case: Compare cholesterol levels across genders or groups.

5. scipy — For Statistical Analysis

What it does: Performs statistical tests, correlations, and probability functions.

from scipy import stats

# Two groups of fasting blood sugar values
group_A = [95, 100, 110, 120, 130]
group_B = [90, 92, 88, 85, 93]

t_stat, p_value = stats.ttest_ind(group_A, group_B)
print("t-statistic:", t_stat)
print("p-value:", p_value)

💡 Use case: Compare two treatment groups’ fasting blood sugar levels.

Summary Introduction to Pandas