Learn Using Pandas for Test Data Manipulation | Analyzing and Visualizing Test Data

Pandas is a powerful Python library that makes it easy to work with structured data, especially tables similar to those found in spreadsheets or databases. In QA engineering, you often need to analyze test results, summarize outcomes, or filter specific test cases from large datasets. Pandas provides the DataFrame—a two-dimensional, labeled data structure—that is ideal for organizing, manipulating, and analyzing tabular test data efficiently. With DataFrames, you can quickly load, inspect, and transform your test results to answer key QA questions.


              123456789101112131415
            
import pandas as pd

# Sample test case data as a list of dictionaries
test_cases = [
    {"id": 1, "status": "PASS", "duration": 2.5},
    {"id": 2, "status": "FAIL", "duration": 3.1},
    {"id": 3, "status": "PASS", "duration": 1.8},
    {"id": 4, "status": "FAIL", "duration": 2.9},
    {"id": 5, "status": "PASS", "duration": 2.2}
]

# Create a DataFrame from the list of dictionaries
df = pd.DataFrame(test_cases)

print(df)

Once your test data is in a pandas DataFrame, you can use built-in methods to filter, sort, and summarize information. Filtering allows you to focus on tests with a certain status, such as only the failed ones. Sorting helps you find the fastest or slowest test cases by duration. Summarizing, such as calculating averages, reveals trends like the typical duration of passing tests. These operations are essential for QA engineers who need to quickly identify issues or monitor test suite performance.


              123456789
            
# Filter the DataFrame to show only failed tests
failed_tests = df[df['status'] == 'FAIL']
print("Failed tests:")
print(failed_tests)

# Calculate the average duration of passing tests
passing_tests = df[df['status'] == 'PASS']
average_duration = passing_tests['duration'].mean()
print("Average duration of passing tests:", average_duration)

1. What is a pandas DataFrame?

2. How can you filter a DataFrame to show only failed tests?

3. Fill in the blank: df[df['status'] == 'FAIL'] returns _____

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 2

Ask AI

Ask anything or try one of the suggested questions to begin our chat

Suggested prompts:

How can I filter the DataFrame to show only passing tests?

Can you show me how to sort the test cases by duration?

What other summary statistics can I calculate with this data?

Swipe to show menu