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Understanding employee performance is central to People Analytics. Performance metrics such as performance scores and ratings provide quantitative measures of how employees contribute to organizational goals. These metrics are typically derived from evaluations, manager assessments, or standardized performance reviews. In People Analytics, you use these metrics to identify trends, recognize high and low performers, and make data-driven decisions about promotions, training, or interventions. By systematically analyzing performance scores, you gain insights into workforce strengths, areas for improvement, and overall productivity within departments or teams.


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import pandas as pd

# Create a DataFrame with employee performance scores
data = {
    "EmployeeID": [101, 102, 103, 104, 105, 106],
    "Department": ["Sales", "Sales", "HR", "HR", "IT", "IT"],
    "PerformanceScore": [88, 92, 79, 85, 95, 70]
}
df = pd.DataFrame(data)

# Calculate average performance by department
avg_performance = df.groupby("Department")["PerformanceScore"].mean()
print("Average performance by department:")
print(avg_performance)

Once you have performance data organized, you can identify high and low performers by filtering your data with Python. Filtering allows you to select employees whose PerformanceScore values are above or below a certain threshold. This is useful for recognizing top talent, targeting development programs, or addressing underperformance. By applying these techniques, you can quickly pinpoint individuals or groups that require attention, ensuring your people strategies are targeted and effective.


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# Filter employees with performance scores above 90 (high performers)
high_performers = df[df["PerformanceScore"] > 90]
print("High performers:")
print(high_performers)

1. What is a common use of performance scores in HR analytics?

2. Fill in the blank: To filter rows in a DataFrame, you can use ____ conditions.

3. How can identifying high performers benefit an organization?

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Section 2. Chapter 2