Summary  
This chapter covers using pandas to export and import CSV files into DataFrames, preview data with the head() method, and select specific columns for analysis.

General domain of usage  
Healthcare analytics

Healthcare professionals often encounter datasets in formats such as **CSV** (Comma-Separated Values) and **Excel** when working with patient records, lab results, or hospital statistics. These formats are widely used because they are easy to generate, share, and open with many tools. Importing data correctly is a crucial first step in any analysis, as it allows you to transform raw information into actionable insights. By understanding how to bring healthcare data into Python, you unlock the ability to clean, analyze, and visualize information that can improve patient care and operational efficiency.

import pandas as pd

# Create a sample DataFrame with anonymized patient data
data = {
    "patient_id": [1, 2, 3, 4, 5],
    "age": [45, 60, 34, 50, 28],
    "diagnosis": ["asthma", "diabetes", "healthy", "diabetes", "asthma"],
    "lab_result": [7.1, 5.4, 4.9, 6.2, 4.7]
}
df_sample = pd.DataFrame(data)

# Write the DataFrame to a CSV file
csv_filename = "patient_data.csv"
df_sample.to_csv(csv_filename, index=False)

# Load anonymized patient data from the CSV file
df = pd.read_csv(csv_filename)

# Display the first five rows of the DataFrame
print(df.head())

The `read_csv` function from the **pandas** library is a powerful tool for importing CSV files into Python. When you call `pd.read_csv("patient_data.csv")`, **pandas** reads the file and creates a **DataFrame**—a tabular structure similar to a spreadsheet. In a healthcare context, this **DataFrame** might contain columns such as patient IDs, ages, diagnoses, and lab results. The `head()` method is commonly used to preview the first few rows, helping you quickly verify that the data loaded correctly and to get a sense of its structure. Reviewing this output is important for spotting any immediate issues, such as missing columns or unexpected values, before you begin deeper analysis.

# Select the 'age' and 'diagnosis' columns from the DataFrame
selected_columns = df[['age', 'diagnosis']]
print(selected_columns.head())

What function from **pandas** is commonly used to load CSV files?

In a DataFrame, what does the head() method return?

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Importing Medical Datasets

1. What function from pandas is commonly used to load CSV files?

2. In a DataFrame, what does the head() method return?

3. Fill in the blank: To select the 'age' column from a DataFrame named `df`, use ____.

Importing Medical Datasets

1. What function from pandas is commonly used to load CSV files?

2. In a DataFrame, what does the head() method return?

3. Fill in the blank: To select the 'age' column from a DataFrame named df, use ____.

3. Fill in the blank: To select the 'age' column from a DataFrame named `df`, use ____.