Learn Cleaning and Validating Medical Data | Healthcare Data Fundamentals

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Data quality is a critical concern in healthcare analytics, where decisions often depend on accurate and complete information. Healthcare datasets may include missing values, such as absent lab_result entries, or inconsistent entries, like different spellings for the same medication. These issues can lead to misleading conclusions, reduced statistical power, and even patient safety risks if not addressed. Understanding how to identify and remedy such problems is essential before any meaningful analysis can begin.


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import pandas as pd

# Sample DataFrame with missing lab results
data = {
    "patient_id": [101, 102, 103, 104],
    "lab_result": [5.6, None, 7.2, None]
}
df = pd.DataFrame(data)

# Detect missing values in the 'lab_result' column
missing_mask = df["lab_result"].isnull()
print("Rows with missing lab_result values:")
print(df[missing_mask])

When working with medical data, you have several options to address missing values. You can drop rows containing missing data, which is simple but may reduce your dataset size. Alternatively, you can fill missing values with a statistic such as the mean or median of the column, helping to preserve overall data structure. In some cases, flagging missing entries for further review ensures that important gaps are not overlooked. The choice depends on the dataset's context and the impact of missing data on your analysis goals.


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# Fill missing 'lab_result' values with the column mean
mean_value = df["lab_result"].mean()
df_filled = df.copy()
df_filled["lab_result"] = df_filled["lab_result"].fillna(mean_value)
print("DataFrame after filling missing values with the mean:")
print(df_filled)

1. What is one common method for handling missing values in a DataFrame?

2. Why is it important to address missing data before analysis?

3. Fill in the blank: To drop rows with missing values in pandas, use df.____().

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Section 1. Chapter 4

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Section 1. Chapter 4