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Exploring trends in healthcare data is crucial for identifying at-risk populations and supporting effective resource planning. By analyzing patterns such as the distribution of patient ages or the frequency of specific diagnoses, you can uncover insights that guide preventative care, allocate medical staff, and prioritize interventions. Detecting these trends allows healthcare professionals to respond proactively to emerging issues and ensure that care delivery is both efficient and equitable.


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import pandas as pd

# Example patient DataFrame
data = {
    'patient_id': [1, 2, 3, 4, 5, 6],
    'diagnosis': ['Hypertension', 'Diabetes', 'Hypertension', 'Asthma', 'Diabetes', 'Hypertension']
}
df = pd.DataFrame(data)

# Calculate the frequency of each diagnosis
diagnosis_counts = df['diagnosis'].value_counts()
print(diagnosis_counts)

When you review the diagnosis frequency results, you gain a clear picture of which conditions are most prevalent in your patient population. For instance, if Hypertension appears most frequently, you may decide to allocate more resources toward blood pressure monitoring and patient education. This information supports targeted health initiatives, helps forecast demand for specific treatments, and can drive policy decisions that improve patient outcomes.


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import matplotlib.pyplot as plt

# Example patient DataFrame with ages
data = {
    'patient_id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'age': [45, 56, 67, 34, 23, 78, 54, 39, 61, 50]
}
df = pd.DataFrame(data)

# Create a histogram of patient ages
plt.hist(df['age'], bins=5, edgecolor='black')
plt.xlabel('Age')
plt.ylabel('Number of Patients')
plt.title('Distribution of Patient Ages')
plt.show()

1. What type of plot is best for visualizing the distribution of a continuous variable like age?

2. How can diagnosis frequency data support healthcare planning?

3. Fill in the blank: To plot a histogram of the 'age' column, use df['age'].____().

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